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(New York) - A new partnership with the pioneering satellite-imaging and data company Planet will enable Human Rights Watch to increase substantially its use of satellite imagery to document rights violations around the world, Human Rights Watch said. Satellite imagery plays an increasingly important role in exposing abuses, especially in countries where access for human rights investigators is blocked or heavily restricted, such as Burma or Syria.

‎Planet is an integrated aerospace and data analytics company that operates history's largest fleet of Earth-imaging satellites, collecting a massive amount of information about the changing planet with a core mission of "using space to help life on Earth." Decision makers in business, government, and nonprofit organizations use Planet's data and machine learning-powered analytics to develop new technologies, drive revenue, power research, and solve some of the world's toughest challenges. In November, Planet met its goal of imaging the entire Earth's landmass every day.

"Planet's generous arrangement with Human Rights Watch, with access to its vast collection of high-resolution satellite data, will help provide timely evidence of unfolding abuses in some of the world's most dangerous spots," said Kenneth Roth, executive director of Human Rights Watch. "This partnership will boost our ability to monitor complex events as they occur and help identify those responsible for atrocities."

In recent months, satellite images have played a key role in documenting widespread burning of ethnic Rohingya villages in Burma, attacks against schools in Syria, and airstrikes in populated neighborhoods of Mosul, Iraq. Beyond its use in areas of armed conflict, Planet's data is uniquely suited for research on abuses related to land rights and the environment.

"Planet seeks to use its groundbreaking technology to promote human rights, protect the earth, and diminish suffering," said Andrew Zolli, who leads global impact initiatives at Planet. "As one of the first satellite companies to originate entirely from the civilian sector, Planet is uniquely suited to support Human Rights Watch's mission of documenting rights abuses wherever and whenever they occur."

Your tax deductible gift can help stop human rights violations and save lives around the world.

(New York) - A new partnership with the pioneering satellite-imaging and data company Planet will enable Human Rights Watch to increase substantially its use of satellite imagery to document rights violations around the world, Human Rights Watch said. Satellite imagery plays an increasingly important role in exposing abuses, especially in countries where access for human rights investigators is blocked or heavily restricted, such as Burma or Syria.

‎Planet is an integrated aerospace and data analytics company that operates history's largest fleet of Earth-imaging satellites, collecting a massive amount of information about the changing planet with a core mission of "using space to help life on Earth." Decision makers in business, government, and nonprofit organizations use Planet's data and machine learning-powered analytics to develop new technologies, drive revenue, power research, and solve some of the world's toughest challenges. In November, Planet met its goal of imaging the entire Earth's landmass every day.

"Planet's generous arrangement with Human Rights Watch, with access to its vast collection of high-resolution satellite data, will help provide timely evidence of unfolding abuses in some of the world's most dangerous spots," said Kenneth Roth, executive director of Human Rights Watch. "This partnership will boost our ability to monitor complex events as they occur and help identify those responsible for atrocities."

In recent months, satellite images have played a key role in documenting widespread burning of ethnic Rohingya villages in Burma, attacks against schools in Syria, and airstrikes in populated neighborhoods of Mosul, Iraq. Beyond its use in areas of armed conflict, Planet's data is uniquely suited for research on abuses related to land rights and the environment.

"Planet seeks to use its groundbreaking technology to promote human rights, protect the earth, and diminish suffering," said Andrew Zolli, who leads global impact initiatives at Planet. "As one of the first satellite companies to originate entirely from the civilian sector, Planet is uniquely suited to support Human Rights Watch's mission of documenting rights abuses wherever and whenever they occur."

Your tax deductible gift can help stop human rights violations and save lives around the world.

Shackling of People with Psychosocial Disabilities Worldwide

Social Media Platforms Remove Evidence of War Crimes

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A new partnership with the pioneering satellite-imaging and data company Planet will enable Human Rights Watch to substantially increase its use of satellite imagery to document rights violations around the world, Human Rights Watch said today.

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American Association for the Advancement of Science

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Outbreaks of hostilities along international borders can be difficult to predict. Even in cases where the countries involved share a well-known and long-running dispute, escalations often appear to occur with little to no warning. With the benefit of hindsight, indicators of an impending conflict may be identifiable; however these are often overlooked as a crisis develops. A number of factors can explain observers' frequent inability to discern the warning signs of an impending conflict. For example, the areas in which such conflicts first erupt are often inaccessible to researchers, either due to their remoteness, bureaucratic or administrative obstacles, or unstable internal security. Furthermore, indicators that are identified post-facto as having led to a crisis can often appear to be innocuous as they are taking place. Satellite imagery, with its ability to reliably document remote areas over a long period of time, provides a way to overcome many of these challenges. This module is designed to help independent investigators apply the lessons learned from AAAS's border conflict research to their own work. It formed part of a project on studying border conflict made possible by a grant from the United States Institute of Peace. Read more at www.aaas.org/geotech/borders.

The following Google Earth layers accompany the exercises described in Introduction to Remote Sensing of Cross-Border Conflicts: A Guide for Analysts:
Lesson 2: Evaluating Infrastructure
Lesson 3: Evaluating Military Vehicles and Equipment

Lesson 4: Evaluating Damage and Direct Evidence of Conflict

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Introduction to Remote Sensing of Cross Border Conflicts: A Guide for Analysts

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Outbreaks of hostilities along international borders can be difficult to predict. Even in cases where the countries involved share a well-known and long-running dispute, escalations often appear to occur with little to no warning. With the benefit of hindsight, indicators of an impending conflict may be identifiable; however these are often overlooked as a crisis develops.

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Amnesty International has released stunning satellite imagery that it says "show devastation of catastrophic proportions" in Baga and Doron Baga, two towns in northeastern Nigeria that were attacked by Boko Haram.

As we've pointed out in the past few days, there has been confusion over just how deadly this attack was. Amnesty International has consistently said that the Islamic militants tore through the towns, burning buildings and killing as many as 2,000 people. The government, however, has denied those claims, saying the death toll is closer to 150.

The new images, the human rights group says, show the government estimates are wrong.

"Up until now, the isolation of the Baga combined with the fact that Boko Haram remains in control of the area has meant that it has been very difficult to verify what happened there," said Daniel Eyre, Amnesty Nigeria researcher said in a statement. "Residents have not been able to return to bury the dead, let alone count their number. But through these satellite images combined with graphic testimonies a picture of what is likely to be Boko Haram's deadliest attack ever is becoming clearer."

Here are two relevant photographs from the town of Doron Baga. The first was taken on January 2. The red represents healthy vegetation:

The second image was taken January 7:

(For a larger view of images one and two, click on the links.)

According to Amnesty, those pictures show that more than 3,100 structures were damaged or destroyed by fire, "affecting most of the 4 square kilometer town."

The Washington Post has a bit of background:

"In August, Boko Haram leader Abubakar Shekau announced the establishment of his 'Islamic Caliphate,' quickly taking over every corner of Borno State in northeast Nigeria. But one town called Baga, populated by thousands of Nigerians along the western shores of Lake Chad, held out. Anchored by a multinational military base manned by troops from Niger to Chad, it was the last place in Borno under the national government's control. Over the weekend, that changed."

Boko Haram has been in the news lately, because the Islamic extremists took responsibility for the mass kidnapping of schoolgirls from the town of Chibok in Borno last April.

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Satellite Images Show 'Catastrophic' Destruction Of Boko Haram Attack In Nigeria

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Amnesty International has released stunning satellite imagery that it says "show devastation of catastrophic proportions" in Baga and Doron Baga, two towns in northeastern Nigeria that were attacked by Boko Haram. As we've pointed out in the past few days, there has been confusion over just how deadly this attack was.

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Satellite images show Syrian destruction and displacement | World Vision USA

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The escalating conflict in Gaza and Israel

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Amnesty International is taking all measures to bring you the latest human rights news on the situation. Backed by world-class verification and a longstanding commitment to impartiality, we are monitoring activities of all parties to the conflict and will continue to publish our investigations into human rights abuses.

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American Association for the Advancement of Science

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Cross-border conflicts can often be difficult to predict. Even in cases where the countries involved share a well-known and long running dispute, for a variety of reasons conflicts may appear to observers to have escalated with little or no warning. There are many explanations for observers' inability to discern the warning signs of an impending conflict. For example, the areas in which such conflicts first erupt are often remote, present bureaucratic or administrative obstacles, or have an unstable internal security situation. This document is designed for human rights and humanitarian actors to provide essential information regarding the possibilities of using remote sensing technology to predict border conflicts. The handbook will provide understanding of the capabilities of the technology in documenting border conflicts and provides details concerning the amount of resources this analysis requires, in terms of time, staff expertise, software, and data costs. This work formed part of a project on studying border conflict made possible by a grant from the United States Institute of Peace. Read more at www.aaas.org/geotech/borders.

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Monitoring Border Conflicts with Satellite Imagery: A Handbook for Practitioners

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Cross-border conflicts can often be difficult to predict. Even in cases where the countries involved share a well-known and long running dispute, for a variety of reasons conflicts may appear to observers to have escalated with little or no warning. There are many explanations for observers' inability to discern the warning signs of an impending conflict.

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Amnesty International tracking violence against civilians in Myanmar 

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Amnesty International tracking violence against civilians in Myanmar 

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The Myanmar military's utter disregard for civilian suffering grows more shocking and brazen by the day. The UN Security Council must urgently refer the situation in Myanmar to the International Criminal Court.

Ming Yu Hah, Deputy Regional Director for Campaigns

"There are no signs of the conflict between the Arakan Army and the Myanmar military abating - and civilians continue to bear the brunt," said Ming Yu Hah, Amnesty International's Deputy Regional Director for Campaigns .

Amnesty International is also concerned at Both the Arakan Army and Myanmar military use antipersonnel devices, and as such definitively establishing provenance is not always possible. Current restrictions on access preclude on-the-ground documentation efforts by Amnesty International. Several incidents involving civilians injured or killed by landmines have been credibly reported in Rakhine and Chin States in recent months by local civil society and media outlets. On e of the most recent instances was on September 18, w hen a 44-year-old Chin woman stood on a landmine while collecting bamboo shoots near the Myanmar military's Light Infantry Battalion 289 base in . She died of her injuries a short time after.

Amnesty International also notes with alarm recent local media reports of the Myanmar military using Rohingya children for forced portering in Buthidaung Township, in an area where clashes with the Arakan Army are ongoing.

"I didn't think it could be our village. I thought it was somewhere else. I tried to call my wife and she wasn't answering. I heard it two times - - within one minute.

I had to run away, leaving my daughter's body. I came back later when they stopped shooting.

Father of a seven year-old girl killed in a shelling attack

By one local civil society group's estimate, the number of civilians killed in this conflict since December 2018 in Rakhine and Chin States stands at 289, with 641 injured. The true figure cannot be independently verified, as a mobile internet shutdown and broader government crackdowns on media reporting have impeded documentation efforts in conflict-affected areas. However, in July 2020, Amnesty International was indiscriminate airstrikes and shelling by the Myanmar military, killing or injuring civilians, including children.

'One from the road and one from the mountain': Burned village attacked from two directions

Over 120 structures in Taung Pauk and Hpa Yar Paung appear burned in Planet imagery from 10 September 2020. Fires were remotely detected in the villages with satellite sensors on 3 September 2020.

One witness, v illager U Kyaw Tin *, who lives in the area, told Amnesty International that he was walking with his cow when the Myanmar military launched an assault on Yar Paung began on 3 September. "[The y ] started shooting, they entered the village. I didn't know exactly where the shooting came from ... We were trying to run to the other side. We didn't really see what exactly was going on, because we all were running."

These figures are based on numbers provided by the Rakhine State government and UNOCHA's humanitarian partners. Local civil society groups indicate the true displacement figure is likely higher, as villagers have fled to areas now only nominally under government control. This adds to the existing mass displacement crisis in Rakhine, where over 130,000 Rohingya have been

'We didn't know anything': Internet shutdown amid the pandemic

The Myanmar government had stated the mobile internet blackout was necessary to prevent "incitement" and remote detonations of anti-personnel explosive devices by the Arakan Army.

We don't have any connection and we don't know anything about what's going on, about the conflict and the attacks in other places.

Healthcare access in Rakhine State remains abysmal, and particularly so for the Rohingya population, who have long been subject to severe movement restrictions and, often, extortion by police and military. The Myanmar government should ensure full, unfettered access to humanitarian actors and allow all people in the state to access healthcare.

Amnesty International is concerned that the sweeping powers granted under COVID-19 orders are ripe for abuse - particularly in conflict-affected areas.

Impunity and secrecy mar military sexual violence scandal

On 11 September 2020, the Myanmar military when the allegations were first raised in July.

Last week, in a statement on the incident, the military publicly but not the perpetrators.

The international community must raise the alarm about the situation in Rakhine State now, or face questions later about why they failed to act - again.

Ming Yu Hah

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Villages burned and civilians killed in Myanmar as Rakhine State conflict escalates

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The Myanmar military's utter disregard for civilian suffering grows more shocking and brazen by the day. The UN Security Council must urgently refer the situation in Myanmar to the International Criminal Court.

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The has been pounded by relentless airstrikes since Hamas militants launched a full-scale assault on southern Israel Saturday, leaving at least 1,200 people dead in the Jewish state, including at least 25 Americans.

The aerial assault by the Israel Defense Forces has been unprecedented in scale, with the IDF saying it has struck at least 2,000 sites in the besieged Palestinian enclave. As of Thursday, more than 1,350 people had been killed in Gaza, according to the region's Heath Ministry.

Photographs from Gaza show harrowing scenes, including entire residential blocks reduced to craters in the ground and parents mourning dead children in overcrowded hospitals. Maps, aerial videos and satellite images reveal the scale of destruction.

What parts of Gaza have been targeted?

Israel has said its bombardments are targeting weapons storage centers and infrastructure used or occupied by Hamas militants, but the United Nations reports that at least a dozen health facilities in the territory have also been hit in the strikes.

At least 450 targets in the Al Furkan neighborhood of Gaza City have been struck, the IDF said, along with another 70 in Gaza City's Al-Daraj neighborhood.

The IDF released aerial video Wednesday of an airstrike on the Islamic University in Gaza, which it said was being used by Hamas for weapons development and militant training. Israeli Prime Minister Benjamin Netanyahu has also tweeted video of the IDF bombings.

Israel has long accused Hamas of positioning weapons and fighters amid the civilian population in Gaza, including in schools and medical facilities.

מטוסי קרב תקפו לפני זמן קצר את האוניברסיטה האסלאמית שמשמשת כמוקד כח מדיני וצבאי חשוב של ארגון הטרור חמאס ברצועת עזה ומוסד הדרכה לפיתוח וייצור אמצעי לחימה.
צה"ל ממשיך בגלי תקיפה נרחבים ברצועה גם בשעה זו pic.twitter.com/NaRPoOepfM
- צבא ההגנה לישראל (@idfonline) October 11, 2023

U.S. space technology company Maxar on Tuesday released satellite imagery that shows entire areas reduced to rubble.

The charity Doctors Without Borders (MSF) said Wednesday that all of the patients it treated at its Gaza City clinic were aged between 10 and 14.

"This is because the majority of the injured in Gaza are women and children, since they are the ones who are most often in the houses that get destroyed in the airstrikes," said Ayman Al-Djaroucha, MSF deputy project coordinator in Gaza.

The median age of the Gazan population is 18, with 65% of the region's population under the age of 24.

Palestinian civilians "are not our targets," IDF spokesperson Maj. Libby Weiss told CBS News Wednesday.

"Loss of life here is tragic," Weiss said. "But again, we must make sure that Hamas cannot launch massacres and slaughter civilians as they did this past weekend. It is just a reality with which we cannot live any more."

Many of Gaza's residents have been unable to escape the bombardments, Palestinian journalist Hassan Jaber told CBS News. "There is no safe place in Gaza," he said, adding that many Palestinians do not have access to bomb shelters and some could face starvation within days.

"There are no shelters or bunkers or safe routes or safe zones in Gaza," Gaza resident Omar Ghraieb told CBS News over the phone on Tuesday.

"We are a family of five people and these unfortunate events unfolded so very fast... We didn't really have enough time to actually stock up enough on food, medicine and water," Ghraeib said. "We are having three to four hours of electricity every 24 hours."

What has been the impact of the strikes?

The United Nations estimated early Thursday that about 338,000 Gaza residents had been displaced by the conflict since Saturday, creating a humanitarian crisis. A panel of independent U.N. experts has said the aerial assault amounts to "collective punishment" of civilians, which they call "a war crime."

So far, no humanitarian corridor has been established for civilians to escape Gaza, or for desperately needed aid supplies to get in.

Hamas controls the , a narrow enclave that runs for about 25 miles along a stretch of the Mediterranean Sea between Israel and Egypt, which has been blockaded by the Israeli military since Hamas took over in 2007. Gaza is one of the most densely populated places in the world and houses about 2.3 million people.

"They are all victims," one woman, who had taken shelter in the grounds of a Gaza hospital, told CBS News Wednesday. "They're all women, children. What did they do?"

- Holly Williams contributed to this report.

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Maps and satellite images reveal Gaza devastation as Israel retaliates for Hamas attack

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The Gaza Strip has been pounded by relentless airstrikes since Hamas militants launched a full-scale assault on southern Israel.

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The Israel Defense Forces (IDF) has given residents of northern Gaza 24 hours to evacuate further south in a sign that it is preparing for a large-scale offensive on Gaza City after last weekend's surprise attacks by Hamas militants.

Stéphane Dujarric, a United Nations spokesperson, confirmed to the Associated Press that it had received a notification from the Israeli government that it was calling on 1.1 million civilians to leave their homes-a move he described as "impossible" in the timescale without "devastating humanitarian consequences."

Newsweek reached out to the IDF via email for comment on Friday.

Aid organizations have already warned of a worsening crisis in the region after Israeli Defense Minister Yoav Gallant ordered a "complete siege" of Gaza, including the cutting off of water, food and energy supplies.

Several have now said the mass movement was not feasible under the current circumstances, which have seen the Israeli Air Force launch intensive air strikes on targets in the Palestinian exclave after last weekend's attacks.

Around 1,500 Hamas and Islamic Jihad militants staged armed assaults on Israel on Saturday, including the killing of civilians. Israel subsequently launched its heaviest-ever airstrikes on Gaza. As of Friday, at least 1,300 people had been killed in Israel and 1,530 people killed in Gaza, according to the AP.

Some 423,000 people are thought to have been displaced by the air strikes, the U.N. said on Thursday. The IDF has been warning civilians in areas about to be hit to leave and has said Hamas houses military targets within civilian populations.

On Friday morning, the IDF confirmed it had told residents in Gaza City to leave their homes and head south of the Gaza River, describing it as "for your own safety and the safety of your families."

"You will be able to return to Gaza City only when another announcement permitting it is made," the IDF said in a statement. "Hamas terrorists are hiding in Gaza City inside tunnels underneath houses and inside buildings populated with innocent Gazan civilians."

However, Hamas, which has de facto ruled the exclave since 2006, reportedly told civilians to defy the order, accusing Israel of "trying to spread false propaganda to sow confusion among the residents of Gaza."

A map of the regions of Gaza, based on U.N. population figures from September, shows that around 749,000 live in or around Gaza City, while a further 444,000 are based in North Gaza-a combined total of 1.2 million. While hundreds of thousands have already been displaced, the evacuation order is calling around half of Gaza's population to shift southwards in one day.

As one of the most densely-populated places in the world, there are already more than an estimated 1 million people in Gaza's southern regions, posing questions of how those fleeing the north will be housed until they can return.

U.N. aid workers have been told to leave northern Gaza for Rafah, a city further south. Nebal Farsakh, a spokesperson for the Palestinian Red Crescent in Gaza City, told the AP that there were patients in hospitals who could not be moved.

On Thursday, the World Health Organization said there had been a "complete blockade" of humanitarian aid entering Gaza and called for a humanitarian corridor to be opened.

"Hospitals in Gaza are at a breaking point," Tedros Ghebreyesus, the agency's director general, wrote on X, formerly Twitter, on Friday. "Without immediate entry of aid, essential health care services will come to a halt."

But Egypt, with whom Gaza shares its southern border near Rafah, has resisted calls to allow Palestinian refugees passage into the Sinai Peninsula. President Abdel Fattah el-Sisi said on Thursday that Palestinians must "remain on their land," according to The New York Times.

Melanie Ward, the chief executive of Medical Aid for Palestinians, told The Guardian that the air strikes on Gaza and the Israeli fuel blockade had made the evacuation of so many people impossible at such short notice.

"Even if they wanted to go, how are they supposed to move?" Ward said. "The roads have been bombed and destroyed. The bombing is continuing. Fuel has been blocked or is under siege so vehicles do not have fuel. Where are they supposed to go and how are they supposed to move? What about elderly people and the disabled?"

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Gaza evacuation map reveals difficulties in relocating 1.1 million people

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The Israel Defense Force has given residents of northern Gaza 24 hours to leave, the U.N. said, something humanitarian organizations have said was unfeasible.

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A video showing satellite imagery of the Gaza Strip before and after an intensive campaign of air strikes by the Israeli Air Force has gone viral, showing the extent of the destruction caused to the densely populated Palestinian territory.

The side-by-side comparison had attracted about 101,000 views at the time of publication, after being posted on Sunday, revealing how many buildings have been reduced to rubble or damaged structurally. One aerial image appears to show the Ahmed Yassin mosque in Gaza City, which was hit on October 9.

The original poster, Muhammad Shehada, a self-described Gazan who works for advocacy group Euro-Med Human Rights Monitor, described it as "televised, livestreamed carnage." Euro-Med has accused Israel of war crimes and "systematic killings," while attributing none of the civilian casualties to Hamas, which initially launched an assault on Israel and has urged against evacuating areas being targeted.

Newsweek approached the Israel Defense Forces (IDF) via email for comment on Monday.

Gaza is literally being wiped out: Before/After👇

Israel killed more Gazans in 8 days than in all of the 7-YEAR long 1st intifada (with its "breaking the bones" policy)

Over 2329 killed, inc. 750 kids in 8 days

12 Gazans killed every hour!

A televized, livestreamed carnage... pic.twitter.com/ixx2SbeB3n
- Muhammad Shehada (@muhammadshehad2) October 15, 2023

Around 1,500 Hamas and Islamic Jihad militants staged armed assaults on Israel on October 7, including the killing of civilians and the taking of hostages. Since then, Israel has targeted militant sites in Gaza, which it says are often placed in civilian areas or buildings, and it has forewarned civilians in those areas ahead of air strikes.

The violence has so far taken the lives of more than 4,000 people, according to the latest death toll by the Associated Press. The Gaza Health Ministry said 2,670 Palestinians have been killed and 9,600 wounded, while over 1,400 Israelis were killed, and at least 155 others, including children, were taken hostage by militants, according to Israeli officials.

Aid organizations have warned of a worsening crisis in the region after Israeli Defense Minister Yoav Gallant ordered a "complete siege" of Gaza-which Hamas controls-including the cutting off of supplies and an intensive campaign of air strikes. Israel has indicated it was preparing a much larger offensive on the Palestinian exclave, which will likely increase the death toll significantly on both sides.

With units massing on the border with Gaza, on Saturday the IDF said it was "currently preparing to implement a wide range of operational offensive plans" including coordinated strikes by air, sea and land.

Ahead of a widely anticipated ground offensive, Israel has asked around 1.1 million Palestinians living in the northern parts of Gaza to evacuate south-around half the population of the Palestinian exclave.

After initially giving those residents 24 hours to leave, and following an outcry from humanitarian organizations, it has highlighted routes from the north that it said it would not target with air strikes within given periods.

There were reports early on Monday morning that a cease-fire of a few hours had been agreed by Egypt, the U.S. and Israeli officials to coincide with the reopening of a border crossing between Gaza and the Sinai Peninsula. However the office of the Israeli Prime Minister, Benjamin Netanyahu, later denied this, stating: "There is no cease-fire."

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Gaza before and after satellite photos show devastation of Israeli strikes

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The side-by-side comparison reveals how many buildings have been reduced to rubble or damaged structurally.

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News releases | Research | Social science | Technology

A fire in Central Park seems to appear as a smoke plume and a line of flames in a satellite image. Colorful lights on Diwali night in India, seen from space, seem to show widespread fireworks activity.

Both images exemplify what a new University of Washington-led study calls "location spoofing." The photos - created by different people, for different purposes - are fake but look like genuine images of real places. And with the more sophisticated AI technologies available today, researchers warn that such "deepfake geography" could become a growing problem.

So, using satellite photos of three cities and drawing upon methods used to manipulate video and audio files, a team of researchers set out to identify new ways of detecting fake satellite photos, warn of the dangers of falsified geospatial data and call for a system of geographic fact-checking.

"This isn't just Photoshopping things. It's making data look uncannily realistic," said Bo Zhao, assistant professor of geography at the UW and lead author of the study, which published April 21 in the journal Cartography and Geographic Information Science. "The techniques are already there. We're just trying to expose the possibility of using the same techniques, and of the need to develop a coping strategy for it."

As Zhao and his co-authors point out, fake locations and other inaccuracies have been part of mapmaking since ancient times. That's due in part to the very nature of translating real-life locations to map form, as no map can capture a place exactly as it is. But some inaccuracies in maps are spoofs created by the mapmakers. The term "paper towns" describes discreetly placed fake cities, mountains, rivers or other features on a map to prevent copyright infringement. On the more lighthearted end of the spectrum, an official Michigan Department of Transportation highway map in the 1970s included the fictional cities of "Beatosu and "Goblu," a play on "Beat OSU" and "Go Blue," because the then-head of the department wanted to give a shoutout to his alma mater while protecting the copyright of the map.

But with the prevalence of geographic information systems, Google Earth and other satellite imaging systems, location spoofing involves far greater sophistication, researchers say, and carries with it more risks. In 2019, the director of the National Geospatial Intelligence Agency, the organization charged with supplying maps and analyzing satellite images for the U.S. Department of Defense, implied that AI-manipulated satellite images can be a severe national security threat.

To study how satellite images can be faked, Zhao and his team turned to an AI framework that has been used in manipulating other types of digital files. When applied to the field of mapping, the algorithm essentially learns the characteristics of satellite images from an urban area, then generates a deepfake image by feeding the characteristics of the learned satellite image characteristics onto a different base map - similar to how popular image filters can map the features of a human face onto a cat.

Next, the researchers combined maps and satellite images from three cities - Tacoma, Seattle and Beijing - to compare features and create new images of one city, drawn from the characteristics of the other two. They designated Tacoma their "base map" city and then explored how geographic features and urban structures of Seattle (similar in topography and land use) and Beijing (different in both) could be incorporated to produce deepfake images of Tacoma.

In the example below, a Tacoma neighborhood is shown in mapping software (top left) and in a satellite image (top right). The subsequent deep fake satellite images of the same neighborhood reflect the visual patterns of Seattle and Beijing. Low-rise buildings and greenery mark the "Seattle-ized" version of Tacoma on the bottom left, while Beijing's taller buildings, which AI matched to the building structures in the Tacoma image, cast shadows - hence the dark appearance of the structures in the image on the bottom right. Yet in both, the road networks and building locations are similar.

The untrained eye may have difficulty detecting the differences between real and fake, the researchers point out. A casual viewer might attribute the colors and shadows simply to poor image quality. To try to identify a "fake," researchers homed in on more technical aspects of image processing, such as color histograms and frequency and spatial domains.

Some simulated satellite imagery can serve a purpose, Zhao said, especially when representing geographic areas over periods of time to, say, understand urban sprawl or climate change. There may be a location for which there are no images for a certain period of time in the past, or in forecasting the future, so creating new images based on existing ones - and clearly identifying them as simulations - could fill in the gaps and help provide perspective.

The study's goal was not to show that geospatial data can be falsified, Zhao said. Rather, the authors hope to learn how to detect fake images so that geographers can begin to develop the data literacy tools, similar to today's fact-checking services, for public benefit.

"As technology continues to evolve, this study aims to encourage more holistic understanding of geographic data and information, so that we can demystify the question of absolute reliability of satellite images or other geospatial data," Zhao said. "We also want to develop more future-oriented thinking in order to take countermeasures such as fact-checking when necessary," he said.

Co-authors on the study were Yifan Sun, a graduate student in the UW Department of Geography; Shaozeng Zhang and Chunxue Xu of Oregon State University; and Chengbin Deng of Binghamton University.

For more information, contact Zhao at zhaobo@uw.edu.

Tag(s): Bo Zhao * College of Arts & Sciences * Department of Geography

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A growing problem of 'deepfake geography': How AI falsifies satellite images

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Using satellite photos of three cities and drawing upon methods used to manipulate video and audio files, a team of researchers led by the University of Washington set out to identify new ways of...

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Abstract

Can we predict civil war? This article sheds light on this question by evaluating 9 years of, at the time, future predictions made by Hegre et al. (2013) in 2011. We evaluate the ability of this study to predict observed conflicts in the 2010-2018 period, using multiple metrics. We also evaluate the original performance evaluation, i.e., whether the performance measures presented by Hegre et al. hold in this new 9-year window. Overall, we conclude that Hegre et al. were able to produce meaningful and reasonably accurate predictions of armed conflict. Of course, they did not always hit the mark. We find that the model has performed worse in predicting low level incidence of conflict than in predicting major armed conflict. The model also failed to predict some important broader regional shifts. These, however, represent important insights for future research and illustrate the utility in predictive models for both testing and developing theory.

Es posible predecir una guerra civil? En este artículo, intentamos responder esta pregunta a partir de un análisis de nueve años de predicciones para el futuro (en ese momento) hechas por Hegre y cols. (2013) en 2011. Evaluamos la capacidad de este estudio para predecir los conflictos observados en el período de 2010 a 2018, para lo cual usamos diversos parámetros. También analizamos la evaluación del rendimiento original, es decir, si los criterios de rendimiento que presentaron Hegre y cols. se mantienen en este nuevo período de 9años. En términos generales, concluimos que Hegre y cols. fueron capaces de hacer predicciones significativas y bastante precisas en relación con los conflictos armados. Por supuesto, no siempre acertaron. Observamos que el modelo no funcionó tan bien en la predicción de la incidencia de conflictos a pequeña escala en comparación con la predicción de conflictos armados más importantes. El modelo tampoco fue capaz de predecir algunos cambios regionales significativos de gran alcance. Sin embargo, estas predicciones aportan información importante para futuras investigaciones, así como también ejemplifican la utilidad de los modelos de predicción para proponer teorías y ponerlas a prueba.

Pouvons-nous prédire les guerres civiles ? Cet article apporte un éclairage á cette question en évaluant les neuf années de prédictions futures qui avaient á l'époque été réalisées par Hegre et al. (2013) en 2011. Nous avons évalué la capacité de cette étude á prédire les conflits observés sur la période 2010-2018 á l'aide de plusieurs métriques. Nous avons également analysé l'évaluation de performances d'origine, notamment afin de déterminer si les mesures de performances présentées par Hegre et al. resteront valables dans la nouvelle fenêtre de 9 ans á venir. Dans l'ensemble, nous en sommes arrivés á la conclusion qu'Hegre et al. avaient réussi á produire des prédictions significatives et raisonnablement exactes des conflits armés. Bien évidemment, ils n'ont pas toujours vu juste. Nous avons constaté que ce modèle avait eu de moins bons résultats dans la prédiction des conflits mineurs que dans la prédiction des conflits armés majeurs. Ce modèle n'a en outre pas permis de prédire quelques importants changements régionaux plus étendus. Cependant, ces conclusions offrent d'importants renseignements pour les recherches futures et illustrent l'utilité des modèles prédictifs, que ce soit pour mettre á l'épreuve ou développer une théorie.

Introduction

Almost 10 years ago in this journal, Hegre et al. (2013) published predictions for the probability of internal armed conflict for most countries in the world for the next 40 years. The article demonstrated that it was possible to construct predicted probabilities of armed conflict decades into the future using dynamic models and projections for key independent variables.

Hegre et al. (2013) contributed to a wave of articles rekindling interest in prediction as a tool both to improve the research on armed conflict and also to make the research more policy relevant ( Hegre et al. 2017). Ward, Greenhill and Bakke (2010) and Schrodt (2014), in particular, argued that prediction needed to become a standard tool in evaluating models and theories on armed conflict. Such reliable long-run forecasts of armed conflict are also needed to construct projections for other critical socio-economic variables such as gross domestic product (GDP) per capita. The GDP per capita forecasts in Dellink et al. (2017) that underpin much climate change research, for instance, are certainly overly optimistic for all scenarios ( Buhaug and Vestby 2019); however, as they completely ignore armed conflict. Joint modeling of armed conflict and such socio-ecomomic variables is necessary to improve forecasts of both. For this to be feasible, we first need to know whether we can produce reliable forecasts of armed conflict. To that end, about a decade after these studies, the literature has reached a level of maturity that allow us to assess whether we are indeed able to make accurate and reliable predictions of civil conflict ( Ryan-Mosley 2019).

These efforts have been challenged, however. As complex social phenomena, armed conflicts are claimed to be too heterogeneous and idiosyncratic to allow prediction, and what caused the conflicts of yesterday are unlikely to be useful to anticipate those of tomorrow. In a compelling critique, Bowlsby et al. (2019) review the prediction models in Goldstone et al. (2010), Hegre et al. (2013), and Chenoweth and Ulfelder (2017). They show that the predictive power varies substantially over time, in particular for the Goldstone et al. (2010) model. The predictive power peaked in the period originally used to evaluate their performance, and it performed particularly poorly in the most recent years. Cederman and Weidmann (2017) are skeptical of long-term forecasting for related reasons.

Although this skepticism is well taken, the retrospective empirical evaluation of Hegre et al. (2013) we present below suggest that there is reason to be more optimistic about long-term forecasting of conflict. The Hegre et al. (2013) study is especially useful for a reassessment. It presented global- and regional-level predictions for the expected incidence of civil conflict in the world for every year from 2010 to 2050. It is the only study we are aware of that has made country-level conflict forecasts publicly available. Included in the replication material for the study was the predicted probability of civil conflict for every country-year throughout this period. Now almost 10 years later, we have a sizable amount of observed conflict history against which these predictions can be confronted. We can now evaluate over a long stretch of time how accurate and reliable these predictions were.

We replicate the original out-of-sample evaluation done by Hegre et al. (2013) for the period 2001-2009, and then add to this a new evaluation of how well their predictions held up in the period from 2010 to 2018. More specifically, we first study how well, overall, the model performed in the 2010-2018 period in predicting both minor and major conflict. Second, we evaluate their evaluations-we examine to what extent the measures of predictive accuracy reported in the original study were indicative of the predictive performance of the model in the subsequent 9-year period. We also evaluate the forecasts in terms of precision-recall curves, a metric the original study did not consider. Third, we discuss a set of important cases that the authors either predicted correctly (such as Nigeria) or missed (such as Libya).

In the next section, we briefly explain the prediction methodology used by Hegre et al. (2013). In Section 3, we evaluate their predictions, discuss the results, and discuss some illustrative " hit" and " miss" cases. Section 4 concludes and discusses some implications of this exercise for the study of armed conflict and for the use of predictive models.

Prediction Methodology

The central innovation in the Hegre et al. (2013) model, is the development of a "dynamic simulation model," estimated by means of a multinomial logit model. This model is dynamic in the sense that the prediction algorithm takes into account what has occurred at t − x when predicting the risk of armed conflict at t and, moreover, that the "realised" prediction at t is carried forward and similarly informs the probability of conflict at t + x. Hegre et al. (2013) estimate the probabilities of transitioning between on of the conflict states-"no conflict,""minor conflict," and "major conflict" -using a multinomial logit model with the conflict level at t as the outcome variable, and the level at t − 1 as a set of dummy variables. In the multinomial model (see Greene 1997, 914-917) for the three outcomes ( j = 0: " no conflict," j = 1: "minor conflict," and j = 2: "major conflict"), the probabilities of the three outcomes are given by:

Using a "dynamic multinomial logit" model allows them to capture that variables may increase the risk of conflict onset, but not its duration. This is achieved by adding interaction terms between the state at t − 1 and predictor variables. They also include information on the conflict state at earlier points in time by adding to the model a function of the number of years in each state up to t − 2. The same simulation framework has been used to perform long-term predictions of conflict as a function of the "shared socio-economic pathways" ( Hegre et al. 2016), for studying the intensity of the conflict trap ( Hegre, Nygård and Ræder 2017), and for evaluating the effectiveness of peacekeepers ( Hegre, Hultman and Nygård 2019). The dynamic simulation concept has recently been extended to be used for short-term, geographically disaggregated violence early warning ( Hegre et al. 2019).

Hegre et al. (2013) build an ensemble of models ( Montgomery, Hollenbach and Ward 2012) using unweighted averages to combine predictions across models. Here, we only focus on the results of the combined model. The constituent models include a range of predictors that capture conflict history for both the country in question and neighboring countries; socio-economic indicators for infant mortality, oil dependence, and education rates; demographic indicators for ethnic composition, youth bulges, and population size; as well as regional markers. Models also include interactions with conflict state at t − 1 as well as the log of time in peace. For a full discussion and description of the variables, see Hegre et al. (2013).

Evaluation

We will discuss three aspects of the predictions made by Hegre et al. (2013). First, we evaluate the model's performance for the 2010-18 period, including assessing whether the out-of-sample performance metrics reported by Hegre et al. (2013) were good estimates of the performance in the true future. Second, we look into some actual cases to explore where the prediction model did well and where it did not do so well. Third, we look into how well the model has succeeded (so far) in forecasting global levels of armed conflict in the aggregate. Throughout, we compare predictions to observed data on armed conflict from the UCDP/PRIO Armed Conflict Database ( Gleditsch et al. 2002; Pettersson, Högbladh and Öberg 2019).

Predictive Accuracy Compared to the Original Out-of-Sample Evaluation

Table 1 reports metrics of predictive accuracy. It is comparable to table 2 in Hegre et al. (2013), but adds metrics for 2010-18 as well as the area under the precision-recall curve (AUPRC). The original out-of-sample performance metrics were constructed by training models on data up to and including the year 2000 and producing forecasts for 2001-2009 as if this was the future. The metrics compare these forecasts with observed conflict as reported by the UCDP. For ease of comparison, in the rows labeled "2001-2009," we have recalculated metrics for the out-of-sample period in the original study. In the rows labeled "2010-2018," we report the same metrics assessing how the predictions of Hegre et al. (2013) compare to observed conflicts over the 2010-2018 period. As in the original study, we report performance across the entire period (2001-2009, compared to 2010-2018) and the 3-year period at the end of the evaluation window (2007-2009, compared to 2016-2018).

We report the area under the receiver pperator curve (AUROC) and the AUPRC for incidence (whether there was conflict or not in a given country year) as well as onset (new conflict that year) and termination (conflict ended that year). Moreover, we report true and false positive rates (respectively, TPR and FPR) at two cut-off points for categorizing a probability as a positive prediction, both with respect to incidence of conflict.

We first compare the predictive performance for the true future (2010-18) with the out-of-sample performance originally reported. For incidence, AUROC is 0.919 for 2010-2018, as compared to 0.944 in the original out-of-sample evaluation. This drop in performance is noticeable, but not very large-the drop is within the 95 percent confidence interval for the original metric, and much smaller than the drop reported in Bowlsby et al. (2019) when reevaluating the study of Goldstone et al. (2010). This is also borne out in Figure 1 which shows the receiver operator curves (left) and the precision-recall curves (right) for the original 2001-2009 and the new 2010-2018 period. In both cases, the two solid lines for incidence of conflict for the two periods are close to each other, indicating that the model performs just as well in the two periods.

The differences are somewhat larger when we turn to onset and termination. For onset, it is clear both from Table 1 and Figure 1, dotted lines, that the out-of-sample evaluation reported in the original study suggested somewhat better performance than what achieved over the 2010-2018 period. This pattern also holds for the precision-recall curve for onset, although here the difference is very small. Table 1 indicates the same pattern holds for termination.

Similarly visible, but not dramatic differences appear when we look at the TPR, especially for the higher, 0.50 cut-off rule. The TPR for the p > 0.30 cut-off was 0.662 for the 2001-2009 window vs 0.657 for the 2010-2018 window. For the p > 0.50 cut-off, performance declined, respectively, from 0.369 to 0.275. This small decline in performance in terms of the TPR is, however, partially offset by a reduction, that is, an improvement, in the rate at which the model produces false positives.

Why is the performance of the model somewhat poorer than originally claimed? Bowlsby et al. (2019, 12) suggest that the " drivers of political instability are time variant." The differences shown here do not indicate that the model performs very much worse over the 2010-2018 period than during the period evaluated in the original study. In this case, the results indicate that the drivers of armed conflict are fairly stable over time.

Predictive Accuracy in General

Beyond that the original out-of-sample evaluation gave a good indication of future performance, how good were the predictions? For incidence of conflict, the predictive performance is quite decent. The model separates well between conflict and non-conflict cases, as shown in separation and model criticism plots in Appendix A.2. It does well even in terms of precision-recall which is the most appropriate metrics set for unbalanced data as is the case here. AUPRCs range from 0.74 to 0.80. To gain some intuition for what these figures mean, we can inspect some points in the precision-recall curves ( Figure 1). If the target is a recall of 0.8, estimated precision is 60 percent. In other words, if we require that the model correctly predicts as conflict 80 percent of actual conflict years, 60 percent of these alerts will be correct. For onset and termination, performance is much less impressive. At an 80 percent recall target, the model predicted conflict in a given country in a given year correctly in only 10 percent of the country years.

For policy purposes, predicting onset correctly may seem much more important than predicting incidence. However, when forecasting years and decades into the future, the difference is less important. The majority of conflicts that will be active in 2030 are unlikely to have started yet, so a good incidence model must also do well in terms of onset. Moreover, the duration of a conflict is of crucial importance. The onset of war in Syria in 2011 was a shocking disaster, but the tragedy is completed by the fact that it still goes on 10 years after. To assess possible humanitarian consequences, there are good reasons to consider the forecasts of conflict incidence in addition to onset.

Important "Hits" and "Misses"

How could the Hegre et al. (2013) model be improved? A closer look at where the Hegre et al. (2013) study hits the spot and where it misses is instructive.

The AUROC and AUPRC metrics respond to the model's ability to rank countries correctly in terms of risk. In Appendix Table A-2 we list the fifty countries with the highest predicted probabilities. The table shows that 26 of the 35 countries with conflict in 2018 are in the top-30 list of predicted probabilities. Three of the four major conflicts are in the same list.

Figure 2 indicates where the models were least precise. The colors represent the difference between the average predicted probability of conflict and the proportion of years in conflict over the 2010-2018 period. Countries plotted with red color had much more conflict than predicted, and those in blue had considerably less. The top map shows the comparison for all UCDP conflicts, the bottom for major conflicts only.

When colors are light, the difference between prediction and reality was slight. The map and Appendix Table A-2 indicates the Hegre et al. (2013) model performed well in predicting the continuation of a series of both minor and major armed conflicts, such as the long-running conflicts in for instance India, Afghanistan, and Somalia. The model also does well in predicting the escalation of previously low-intensity conflicts that have since become higher-intensity ones, such as for instance the conflict in Nigeria, Niger, or the Central African Republic. Even Burkina Faso, a country that had not seen state-based conflict since 1987 according to UCDP when the forecasts were made, is in the top quarter of the risk rankings.

However, the model under-predicted severely for Syria, Libya, Yemen, and Ukraine, for both major and minor conflict. The model ranked Yemen highly ( Appendix Table A-2), with an annual probability of either conflict above 25 percent, but, as any other forecasts made in 2010, missed the other three.

The model over-predicted conflict for Peru, Angola, Sri Lanka, and Ethiopia. All of these countries were in conflict in 2009, the last year with data in the model. Among countries with ongoing conflict, Peru and Sri Lanka were assessed as considerably more likely to end the conflicts than continue them, though. For major conflict, it assigned a too high probability to India and Russia. Again, the predicted probabilities of major conflict were relatively low (see Appendix Table A-2). On the other hand, the model suggested a 88-percent chance of conflict in Ethiopia, given its long-standing conflict. In 2018, that prediction was a false positive, but in 2020 this case returned to a true positive, despite the 2019 Nobel peace prize to Abiy Ahmed Ali.

Ability to Predict Correctly in the Aggregate

The relatively poor performance of the fixed-threshold metrics TPR and FPR ( Table 1) as compared to the rank-based metrics, suggests that the model, and the predicted probabilities it produces, are not properly calibrated. The metrics indicate that Hegre et al. (2013) under-predict conflict. This is clear in Figure 3 which plots the observed incidence (measured in terms of percent of countries) of conflicts in the world from 1970 to 2018 (solid line) along with predicted incidence for the 2010-2050 period (dashed line). The two vertical lines mark 2009 and 2018, the final years of the two prediction periods we examine. The model performs well in predicting the proportion of countries in major armed conflict. The model did not anticipate the strong increase from 2013 to 2014, but after the decline of major conflict from 2017 to 2018 ( Pettersson, Högbladh and Öberg 2019), the actual proportion of countries dropped to the forecasted level.

Now consider the line for "Either"-minor or major conflict incidence combined. Here, Hegre et al. (2013) predicted a pronounced and continuous decline in the number of conflicts from the 2009 level. In contrast to this, the world in this period saw a substantial increase in the number of armed conflicts. Indeed, in absolute numbers, there were more active armed conflicts in 2015 than registered at any time since 1946 ( Allansson, Melander and Themnér 2017). From the above, we know that the model does well at predicting major conflict, implying that this divergence is driven by a failure of the model to predict more low-level, minor, conflicts. We discuss potential reasons for this below.

Regional Performance

Some regions, in particular the Middle East and North Africa (MENA) region, has seen especially pronounced differences in their conflict trajectories across the old and the new evaluation window. Indeed, the old evaluation window ended right before the onset of the "Arab spring." To delve deeper into this and to see to what extent the decrease in performance is region-specific, Table 2 breaks down the AUROC and AUPRC by region, again comparing the regional performance now to what was reported in the original study. At the regional level, we do see a decrease in both metrics for the MENA region. The performance for 2010-2018 for West Africa is actually better than in the original out-of-sample evaluation, whereas Southern Africa is worse. Beyond these regions, differences are small or negligible.

What West Africa and MENA have in common is important, and points to three important limitations of the model. First, no authoritative projections for democratic institutions exist and Hegre et al. (2013) were therefore unable to include democracy, changes to democratic institutions, or other regime characteristics in their model. Apart from conflict history, the most important driver of conflict in the model is lack of socio-economic development. Since Syria and Libya are (or were) middle-income countries, the model did not assign a high risk to them. Studies indicate that democratization processes are associated with a heightened risk of internal armed conflict ( Hegre et al. 2001). The lack of features covering political institutions, especially democracy, in the model meant that it would be hard pressed to predict at least the timing of the "Arab spring" dynamics with a high degree of accuracy. As we elaborate on below, developing forecasts of democratization processes is of high importance to improve conflict forecasts.

Second, conflict history is among the most important predictors in the Hegre et al. (2013) model. West Africa and MENA both saw substantial regional-level changes in their conflict trajectories between the different windows. West Africa had seen a number of conflicts throughout the 1990s and early 2000s. This, however, changed substantially as the wars in Liberia and Sierra Leone ended in 2002 and 2003. Consequently, West Africa was much more peaceful in the evaluation period of the original study then it had been over the last decade. Similarly, the MENA region had seen relatively few armed conflicts in the decade before the Hegre et al. (2013) prediction period started in 2010, something which then changed dramatically with the onset of armed conflicts in Yemen, Libya, and Syria. To some extent, these conflict history variables appear to make the model, at a regional level, somewhat too static-both in locking larger regions in conflict and in peace-and very cautious about predicting broader, regional level, changes to conflict incidence.

Third, and related to the last point, the Hegre et al. (2013) model does include regional-level and neighborhood factors as predictors, but these appear to be unable to account for what is arguably regionally correlated changes, such as those seen in West Africa and the MENA. Very likely, the error terms exhibit regional-level patterns of between-country correlation. If the model fails to predict one country then this prediction error increases the likelihood of a mis-prediction for another country in the same region-prediction errors are correlated. Because of this, the model is partly blind to regional conflict dynamics that can lead to broader shifts both in a more peaceful and more violent direction, such as those seen in, respectively, West Africa in the early 2000s and in MENA in the early 2010s. We discuss some possible solutions to these issues below.

Long-Term Forecasting of Armed Conflict: the Way Forward

Have attempts at predicting armed conflict been excessively ambitious? Cederman and Weidmann (2017) remind us that especially for long-run predictions it is important to take into account "massive historical complexity and contingency in human systems" as well the likelihood of systemic shocks that may both alter the relationship between conflict and key predictors as well as what the key predictors are themselves. One crucially important approach to learn about the role and importance of such dynamics, however, is to build and assess theoretically motivated long-term forecasting models based on insights that are widely accepted ( Colaresi and Mahmood 2017). Only then will we be able to say something meaningful about the role and importance of "historical complexity" and systemic shocks. To cite the great H. G. Wells: "Science is not an account of facts but a criticism and analysis of facts ... It is therefore not merely legitimate ... to attempt forecasts of general trend of events, but to not attempt them is a frank confession of the futility of the science" ( Wells 1925, 1). Here we have evaluated the first 9 years of such an attempt to produce 40 years of such predictions. The general conclusion, we argue, is that creating meaningful, useful, and reasonable predictions does appear to be possible. In contrast to Bowlsby et al. (2019) we also find that the predictive accuracy is quite stable over time and that predictive performance has not declined much in the ten years since the study we focus on here was published.

This, of course, does not at all imply that the predictions published by Hegre et al. (2013) always hit the mark. Far from it. Three weaknesses in general appear. First, in term of forecasting the aggregate, global, level of conflict, the model performs worst for low-intensity, "minor," conflict. This suggests that the model is not optimally calibrated, in terms of predictors, for such low-level conflict. To some extent this may be because low-intensity conflict are more stochastic than higher-intensity major conflicts. The shock of the "Arab spring" in combination with the failure to mount effective international response to incipient violence, led to a number of conflicts that the model failed to predict. Three of them, Syria, Yemen, and Libya, descended into major conflagrations. The rest of these cases (e.g., Tunisia and Jordan) remained minor. Possibly, the structural variables that dominate the Hegre et al. (2013) model largely constrain countries from major violence but not from minor incidences.

The model seems to be quite good at predicting escalation from low to high levels of violence, but considerably less impressive at predicting escalation from no conflict to minor conflict-the model does not perform particularly well for the onset of armed conflict. The latter is intrinsically a more difficult problem, and requires a different set of predictors than the ones included here. However, such "early-warning signals" that change quickly and alert to alarming recent events are almost by definition not available when seeking to forecast decades into the future. A model might do better by including such predictors for the first few years only, and use structural variables for the longer-term forecasts. Such a construction, however, would add complexity and lead to some losses in interpretability. It might be better to treat short- and long-term prediction problems separately, and assess studies like Hegre et al. (2013) in terms of their performance for the long-term incidence of conflict rather than for onset in the short term.

Second, not surprisingly given the lack of variables measuring aspects of the political system, the model fails to predict dynamics that are driven by changes to political systems. The prediction failures related to the Arab spring that we have discussed are cases in point. The termination of conflict in Ethiopia is also related to a positive political development the model was not set up to incorporate. Over the long run, political changes would be partially covered by aspects of socio-economic development included in the model, since socio-economic development is a powerful predictor of political development ( Przeworski et al. 2000). Over shorter periods, however, the model will miss such dynamics, and the turbulence in the transition phase will not be well modeled. A more comprehensive solution is to forecast changes to political systems and the onset and termination of armed conflict jointly.

Third, the model appears to be too conservative in the face of broader regional-level shifts. The model clearly underestimated the extent to which conflict spreads from one country to another. This indicates that the model should have better accounted for the plausibly correlated error terms between countries in a region. In the case of the Arab spring, the unrest spread not only through geographical adjacency, but also across the Arab cultural sphere. The model would do better if it had modeled spread of conflict within the same language or religion group in addition to through geographical neighbors. With a better diffusion model, the spike in 2014-2018 in Figure 3 might have been covered by the prediction interval. Alternatively, models that capture systemic shocks to the relations between variables (such as Mitchell, Gates and Hegre 1999; Cunen, Hjort and Nygård 2020) might be necessary.

These are important technical learning points as the discipline continues its attempts to build conflict-forecasting models. But they also represent theoretical insights. The difference in predictive accuracy between minor and major conflict indicates that there is more heterogenity between these types of conflict then we typically account for in models of civil war. This should be addressed in future research. The regional clustering also represents an avenue for future theorizing. Of course, a large literature does exist that examines spatial diffusion of conflict, but our exercise indicates that more work is needed.

Predicting conflict 40 years into the future obviously entails pushing a model to its extreme. However, it may be the most effective way to learn something enduring about the state of theory and our understanding of civil war dynamics. If we had been completely unable to predict armed conflict 10 years into the future based on variables that the discipline essentially agree are critical, how much could we really say that we knew about civil war? Luckily, this exercise shows that we know a lot-and that we still have a lot to learn.

The Way Forward

We are encouraged by the fact that the original study does show that long-term forecasts are feasible. Nonetheless, the weaknesses of the study discussed here suggest a set of key lessons for future research:

Forecasting the predictors of conflict. Human systems are complex and contingent ( Cederman and Weidmann 2017). The Hegre et al. (2013) model failed to predict the war in Syria in part because it ignored the importance of political systems. To incorporate such variables in long-term forecasts, it is necessary to include forecasts of changes to political institutions in the model. Some attempts to do so now exist ( Morgan, Beger and Glynn 2019; Andrijevic et al. 2020) although these still do not have the status of 'authoritative'. The same applies to 'oil dependence' and any other factor that scholars see as essential to predict conflict.

Model conflict and other predictors jointly. The model in Hegre et al. (2013) relies on the projections for population and infant mortality that were available in 2010. Since then, several new projections have become available that build on the "Shared Socioeconomic Pathways" ( O'Neill et al. 2014). These are much improved as they represent a set of future scenarios that come close to spanning the vast uncertainties regarding the remainder of this century. Armed conflict, however, is largely ignored in these projections. The GDP per capita forecasts in Dellink et al. (2017), for instance, that underpin much climate change research, are certainly overly optimistic for all scenarios ( Buhaug and Vestby 2019). The governance projections in Andrijevic et al. (2020) suffer from the same problem. Joint modeling of armed conflict and predictors is necessary to improve forecasts of both. Possibly, the over-optimistism in Hegre et al. (2013) is partly due to an over-optimism in the projections for exogenous drivers of conflict that it relies on. The population projections in United Nations (2007), for instance, are vague on the basis for the projected reduction in infant mortality, and may well be over-optimistic with respect to that particular indicator. Some projects do joint modeling (e.g., the International Futures project; Hughes 2019), but much more effort is required to solve this challenging problem.

Improved modeling. Logistic regression models still belong in any forecaster's toolbox, but should now be complemented by new approaches from the machine-learning literature. There are two constraints that restrict the menu of methods, however. Since conflict data at the country-year level are very sparse, methods such as random forests and penalized regression models that safeguard against overfitting are most promising. Moreover, since the forecasting horizon-for instance, to the end of the 21st century-is longer than the period for which we have high-quality data, models need to be fit into some sort of dynamic simulation framework to be useful.

Combine levels of analysis. Modeling conflict spillover is another shortcoming of Hegre et al. (2013). This is partly due to a lack of data-conflict is fortunately quite rare at the country level, and spillovers even more so. In addition, spillovers are often local-conflicts in the Caucasus are unlikely to affect the far East of Russia, for instance. Models pitched at detailed geographical locations are much more likely to succeed ( Witmer et al. 2017). However, since several crucial processes work at the government level (e.g contests over the nature of political systems), effective long-term modeling must combine insights from multiple levels of analysis. Some ideas for how to achieve this are suggested in Hegre et al. (2019). Since data at more fine-grained geographical levels are less sparse, data-hungry models such as various recurrent neural network models as well as boosted tree models can be applied effectively.

Evaluation. Considerably more conceptual work is needed on how to evaluate to most effectively evaluate forecasts. This include foundational issues such as what metrics are most appropriate and what windows should be considered. We have shown that the addition of AUPRC qualifies the conclusions to be drawn from the original study. The mismatch between performance at the country-year level and the global aggregate also suggests that evaluation should look at performance from multiple angles. This has implications for how we train models-most algorithms optimize on only one evaluation criterion, but good models should weigh different metrics against each other.

Maintain running forecasting systems. A strength of the Hegre et al. (2013) study is that they produced and made openly available a long timer series of future forecasts that could be evaluated. To move this field forward, we need more systems that are set up to produce regular forecasts, forecasts that then over time can be evaluated across different metrics. One such system is the Violence Early Warning System ( Hegre et al. 2019).

Better data. Finally, richer, more precise data with greater temporal and spatial coverage is most important of all. Over the past decade, the UCDP-GED ( Sundberg and Melander 2013) time series have become 30 percent longer and the V-Dem project has established the most comprehensive democracy data ever produced ( Coppedge et al. 2020). New efforts to improve on the forecasts reviews here can start from a much richer selection and data, and substantial gains in precision are to be expected.

Funding

The authors acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 694640, ViEWS), and the Research Council of Norway (Grant No. 275400).

Håvard Hegre (Dr Philos, University of Oslo, 2004) is Dag Hammarskiöld Professor in Peace and Conflict Research, Uppsala University and Research Professor at Peace Research Institute Oslo (PRIO). Current research interests: democracy, development, and armed conflict; forecasting of armed conflict.

Håvard MokleivNygård (PhD, University of Oslo, 2014) is Research Director at PRIO. His research on the political economy of violence and social order, conflict escalation, forecasting of armed conflict, and modelling regime shifts has appeared in, inter alia, the American Journal of Political Science, Journal of Politics, Annual Review of Political Science, and Journal of Peace Research.

Peder G. Landsverk (MA, University of Oslo 2019) is a research assistant at PRIO. His research interests are focused around applied computer science within the social sciences, with a special emphasis on conflict forecasting.

Notes

Author's note: The data underlying this article are available on the ISQ Dataverse, at https://dataverse.harvard.edu/dataverse/isq. as well as at https://www.prio.org/Data/Replication-Data/.

References

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivslicence ( https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

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Can We Predict Armed Conflict? How the First 9 Years of Published Forecasts Stand Up to Reality

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Abstract. Can we predict civil war? This article sheds light on this question by evaluating 9 years of, at the time, future predictions made by Hegre et al. (20

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Our guest today is Ron Hagensieker Ph.D., the founder, and CEO of OSIR.IO, an artificial intelligence and earth observation company. Ron has had an interest in remote sensing in the beginning, starting with his BA in geography, all the way to his PhD in remote sensing. All along the way, he has found ways to incorporate machine learning. One project, in particular, was thiscitydoesnotexist.com, a site that uses AI to randomly generate a false Landsat-style image of, you guessed it, a city that does not actually exist.

How is Fake Aerial Imagery Created?

In order to create fake imagery, first, you must obtain a great deal of real imagery. These images will be used to train your neural network to recognize the unique landscape characteristics that make a city a city. The machine learning models learn to identify and recreate the patterns we associate with small-scale views of cities (i.e Landsat or Sentinel imagery) mimicking the transitions from city center, to suburbs, to farmlands. They can even be trained to create fake DEMs.

ThisCityDoesNotExist.com 's algorithm actually involves creating two competing machine learning algorithms, the generator, and the discriminator.

The generator algorithm generates images, attempting to fool the discriminator model which dubs each image as a 'real' or 'fake' city.

Based on the responses of the discriminator model, the generator will adapt its next image, and try again. In programming, this technique is called a Generative adversarial network (GAN). GANs are especially powerful algorithms, and can even be pit against other algorithms to expose their weaknesses.

The positive feedback loop the generation and discrimination process creates can hypothetically go on forever, but will ultimately be limited by computer resources, or be influenced by the finite number of images originally used to train the model.

These algorithms use unsupervised classification, which is when the computer groups like pixels into a prescribed number of classes.

Unsupervised classification distinctly does not require drawing bounding boxes around features, and therefore may be faster to prepare than supervised methods.

It is possible to add some influence to the process with supervised classification. This involves inputting a dataset of city center points, telling the model "here, these are cities", potentially improving results.

Why Create Fake Imagery?

In a data rich world with so much access to all varieties of raster data, why would we want to add fake imagery into the mix? Well, the truth is we aren't quite sure yet. When plenty of real data exists, there is little desire to inject energy into simulating data. While there are no especially compelling uses for large amounts of fake imagery, doctored imagery is a different story.

There may be military applications for faking parts of images, but not necessarily whole landscapes. This could be identifying and disguising sensitive military operations in satellite imagery, or maybe even simulating the result of damage to landscape elements.

Randomly generated AI landscapes don't have much use on their own, but once you add more channels to the data (Elevation models, population density, etc), and start adding constraints, things can get really interesting.

The trick here is the channels must be coregistered to be meaningful. Coregistering means that the images have all been lined up with each other spatially.

By combining information from many channels, you can create a living modeling environment. It gets even more exciting if you can pull off adding time. This allows predictive simulation of urban planning scenarios, deforestation patterns, and even of events resulting from climate change. By simulating different population counts and densities, we can begin to identify common patterns in how people grow into their cities.

Fun Fact : If you drop oat flakes into slime mold at the positions of train stations in Tokyo, the resulting fungal network grows to look just like the actual Tokyo Rail System.

The reason landscape modeling does not qualify as a compelling application of fake imagery is that it is incredibly costly in terms of computing resources. Without adequate financial backing, it does not make sense to create a program like this. For now, coders are just having fun without funding.

What Are the Risks of Fake Satellite Imagery?

The most obvious risk associated with creation of imagery from machine learning models is the risk of people using this technology maliciously. Deep faking images may allow you to represent extreme scenarios on a landscape. One popular one is predicting the same landscape in all four seasons, but you could also simulate flooding, or wildfires.

Luckily there is not much genuine risk of misinformation (amongst free media at least) about events as these things are so easily verifiable.

If someone actually takes the time to simulate media coverage of a catastrophic event somewhere using deep-faked images, fact-checkers can verify from half a dozen public imagery sources if the event actually occurred (or even simply just call someone there).

So if you come across a fake image in the wild, how would you be able to tell it was fake? Well, one characteristic is that fake imagery often looks downscaled. Depending on the base logic in the algorithm used, sometimes you can pick out a fake image by the lack of truly straight road segments. This also applies to the lack of natural angles on building corners, or the presence of especially blurry building footprints.

Characteristics like haze or odd colors in areas are not reliable tells of fake imagery as these elements are normal in most real imagery as well due to atmospheric effects.

Overall, fake imagery has the potential to be very powerful, but in the current technical landscape, there is not much need for it. Due to the high skill, time, hardware, and financial investment necessary, it is unlikely we will see too much development growth in this area. The one thing we have learned from artificial intelligence, however, is to expect surprises.

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Fake Satellite Imagery - October 17, 2023

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Well if the title does not grab your attention then this episode is not for you! This episode is all about using AI to generate satellite imagery using something called generative adversarial networks ( GANs ). That might sound scary if you have not used...

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Putting vulnerable populations on the map

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Putting vulnerable populations on the map

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A donation to the Humanitarian OpenStreetMap Team (one of the founders of Missing Maps) can help support local people to map their own communities, develop tools and processes so that more people can contribute, and scale this work so that even more responders and humanitarian organisations around the world have maps and open data that enable them to make time-critical, data-driven decisions.

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Putting the World's Vulnerable People on the Map

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The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia

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Abstract. How feasible is violence early-warning prediction? Colombia and Indonesia have unusually fine-grained data. We assemble two decades of local violent events alongside hundreds of annual risk factors. We attempt to predict violence one year ahead with a range of machine learning techniques. Our models reliably identify persistent, high-violence hot spots.

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The Actor Network graph visualizes interactions among the 25 most active actors for the selected event and/or sub-event types for the most recent week of ACLED data. Each node is either a single actor or a group of actors of the same actor type. A connection between icons implies that the actors interacted in the same event(s).

The contents of the graph depend on the selections in the sidebar menu:

If "All" countries and territories are selected, the network shows interactions for the entire world.

If a single country or territory is selected while "All" ADMIN1s are selected within that country/territory, the network shows interactions for that entire country.

If a specific ADMIN1 within a country/territory is selected, only interactions within that ADMIN1 are shown in the graph.

Identifying actors

Actors are colored by their type and sized by the number of events in which they were involved in.

Emerging Actors - denoted by a ◆ - are actors that were involved in an event in the most recent week of data but not active during any other week in the past year.

Hover over a node to reveal an actor's name and associated event count.

Select a specific actor from the network menu for quick identification within the network.

Actor groupings:

Each Emerging Actor is plotted as a single node, regardless of how many events they were involved in.

For the Global Actor Network, non-Emerging Actors are grouped together by actor type.

When an individual country or ADMIN1 is selected, the 25 most active actors, in addition to all Emerging Actors , are plotted as single nodes. All other actors are grouped together by actor type.

Why focus on emergent actors?
The presence of new actors, and those that move from latent to active operations, points to a change in the conflict environment. In areas that are relatively peaceful, it indicates that the incentives and opportunities for violence have shifted. In high activity spaces, new or re-emergent conflict agents can indicate factionalization of existing opposition groups; new attempts by governments to seed the formation of militias; and new interest groups engaging in violence with an agenda to upset the status quo, among other trends. When areas become incubators for new groups, there is a high possibility that more groups may emerge. It is probable that the frequency and intensity of actions will increase, or at best, that more groups will engage in activity. This means that engaging in this environment, or seeking peace, will be complicated by ever more agendas and compromises with armed groups.

Civilians often bear the burden of violent activity involving new and re-emergent groups. Such actors are often weaker than those that are already established, and in order to demonstrate their role without engaging in combat, these groups may focus on civilians as they are easier targets and because attacks on communities can generate significant media coverage and public attention, elevating the presence of that group in a multi-agent environment.

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ACLED's suite of interactive early warning and conflict forecasting tools.

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A University of Cambridge academic is proposing a new model for forecasting conflict, which could predict when outbreaks of violence might escalate and spill into armed conflict.

Forecasting a phenomenon like armed conflict up to a year in advance is difficult, and history has shown conflict might suddenly appear out of a previously long lived and low level dispute.

Dr Christopher Rauh, a University Lecturer at the Faculty of Economics at the University of Cambridge, is part of a team that has investigated the signals that indicate a conflict might be about to turn 'hot', and result in civil commotion turning into either an all out war, or at least a dispute that would result in fatalities.

"A special feature of our model is our reliance on newspaper text. This allows us to capture changes in low level risk of conflict in countries which do not have a recent history of violence," he says. "The risk estimates provided here are ambitious in that they put an emphasis on these cases."

The models provide risk forecasts for two levels of violence:

1. An outbreak of any violence: a country goes from no fatalities to positive fatalities.

2. An outbreak of armed conflict: a country goes from having less than 50 fatalities in a month to more than 50 fatalities in a month.

"Our model has been tested extensively by us and in a forecast competition organized by the Violence Early-Warning System (ViEWS) in which our approach performed extremely well," he adds.

More details of the forecasting methods are at: https://conflict-forecast.vercel.app/about

Tags:
Prediction Risk Estimation Forecasting

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Predicting when conflict breaks out: A hard problem

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A University of Cambridge academic is proposing a new model for forecasting conflict, which could predict when outbreaks of violence might escalate and spill into armed conflict.

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Lauren Leatherby, Karen Yourish, Elena Shao, Eli Murray, Scott Reinhard

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Maps: Tracking the Attacks in Israel and Gaza

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The New York Times is tracking airstrikes and ground fighting between Israelis and Palestinians.

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https://www.nytimes.com/interactive/2023/10/07/world/middleeast/israel-gaza-maps.html

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Conflict forecasting

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Fake news, deep fakes and AI-generated content. The web is rife with these things.

Now generative adversarial networks are being used to create realistic things that don't actually exist as well. There are all sorts of them too, some are incredible, some daft, all certainly interesting.

We've collected a few for your enjoyment.

This dog does not exist

This dog does not exist. Instead, it was created, along with the rest of the photo, using Google's AI known as Imagen.

Imagen uses artificial intelligence to create images using just text input. In this case, that text was:

"A photo of a Corgi dog riding a bike in Times Square. It is wearing sunglasses and a beach hat."

A very specific request with hilarious and brilliant results.

This beach does not exist

This beach does not exist, it was crafted using Nvidia Canvas, a free app that uses artificial intelligence to turn your digital paintings into lifelike images in real time.

We've written about Canvas before and shown what it can do. It's an impressive tool that's remarkably simple to use and the results are impressive as well.

This bacon sandwich doesn't exist

This BLT sandwich sure looks delicious, but sadly it isn't edible because it isn't real. It was crafted by DALL-E 2.

This is AI that's built to create unique images with simple text input. We've previously shown off some of what this artificial intelligence can do and it sure is impressive. The future of AI-generated content sure is going to be interesting.

This Cat Does Not Exist

Cats. You either love them or hate them. How do you feel about a cat that doesn't even exist though?

These AI-generated cats are fairly convincing and they're not likely to cause you any allergy problems, which is a bonus.

This Pony Does Not Exist

This Pony Does Not Exist is a site that displays a colourful grid of AI-generated pony portraits created using Nvidia's StyleGAN2 architecture.

With some clever wizardry, these creatures have been created, cropped, scaled and displayed in all their glory for your viewing pleasure.

This Car Does Not Exist

Here's a site that generates vehicles that never existed. All sorts of weird and wonderful cars that you've never seen on the road and never will.

This certainly isn't the best one on our list though. Click through and refresh the page a bunch of times and you'll see some really odd wheeled machines.

These people do not exist

The people that you see in this collection of images do not exist and never have. They have been created by a generative adversarial network that was originally developed by Nvidia, then later used by Phillip Wang to create This Person Does Not Exist.

That site essentially uses artificial intelligence to randomly generate completely artificial and yet hyper-realistic portraits of people. People who have never graced the planet and yet could quite easily live next door to you.

This technology is quite disturbing when you consider what Inverse noted when interviewing the creator.

"In a society where pictures and images are the standard surrogates for "proof," GANs....will soon allow anyone to furnish "proof" that any imaginable person did any imaginable thing....As an example, a nefarious actor could spread a GANs-generated video or image depicting a bogus event to incite riots, protests, or other potentially violent reactions online."

Scary, but it doesn't take away from how impressive this technology is.

This Meme Does Not Exist

Everyone loves a good meme. So why not apply artificial intelligence to create some new ones? That's just what ImgFlip has done.

This meme does not exist is the result and the images generated by the system are, well, something special.

This City Does Not Exist

This City Does Not Exist is an impressive website showing various aerial images of Cities that don't actually exist. And yet they look like something you'd find by scrolling through Google Maps or peering out of an aeroplane window. Land and sea collide with manmade structures, all created by artificial intelligence.

These Eyes Does Not Exist

This one might seem unusual when compared with the others, but it's actually quite interesting.

The author/creator of This Eye Does Not Exist explains it nicely:

"Imagine that you are a makeup artist of some sort and you need to conceptualize some new eye makeup brand. But you have a problem, you are out of ideas. Generative adversarial networks allow you to create stuff that doesn't exist, let's say, photographs of eyes with makeup. This algorithm essentially allows you to "dream up" new stuff..."

It's nice to think that generative adversarial networks could be used for good. As well as creating fake nonsense out of the ether.

These Beetles Do Not Exist

This is an interesting project which uses machine learning to generate illustrations of beetles that don't actually exist. This was created using machine learning to analyse thousands of illustrations already in the public domain.

Inspired by zoology, botany and archaeology imagery, This Beetle Does Not Exist is certainly fascinating and stands apart from the others on this list that exist as somewhat convincing real-world images.

These Paintings Do Not Exist

This interesting addition to our list takes something that doesn't exist and turns it into a physical thing that does.

These are works of art created using Google's BigGAN (Generative Adversarial Networks) then printed and painted for your enjoyment by Ganvas Studio.

The idea is to use Joel Simon's Ganbreeder system to create your own artwork, then get Ganvas Studio to print it.

Ganbreeder lets you merge images and use AI to create something entirely unique - and a bit weird too.

These Portrait Paintings Do Not Exist

Art Breeder is an oddity. A site which creates fake artwork with simple tweaks to settings to change aspects of it. The results are weird, wonderful and certainly unique.

These are examples of portraits that were created by this technology but otherwise would not exist.

These Landscape Paintings Do Not Exist

Like the portraits, these painted scenes of colourful and beautiful landscapes do not actually exist but instead have been created using GANS and human interaction.

This is not a Rembrandt

This might look like a painting by master painter Rembrandt, but it's actually been crafted by technology.

400 years have passed since Rembrandt's last painting, yet art historians and technicians have managed to use a mix of data and facial recognition technology to analyse 346 different paintings by Rembrandt.

That data was then used to identify the typical geometric patterns used by the painter with a focus on certain human facial features. A 3D printer was then used to paint 13 layers of UV-ink to create a realistic painting that would form The Next Rembrandt.

This Vessel Does Not Exist

Derek Philip Au, creator of This Vessel Does Not Exist explains the logic of this one nicely:
"The history of ceramics is one of imitation and reproduction. The apprentice obtains mastery of the craft through repetition, gradually improving their technique. Guided by a lifetime of working in the craft, the master examines each piece made by the student and throws away those deemed unsuitable. The forger creates replicas and tests them in the marketplace. The connoisseur, informed by decades of experience dealing with antiques, judges the replicas. Those that are mistaken as authentic are sold, and the forger goes on to create even more convincing copies."
Here GANs have been used to create fake vessels that are then generated, judged and improved upon. The result is a seemingly endless collection of vases and vessels all created by artificial intelligence.

This beach does not exist

Like other things on this list, this beach does not exist but was instead created using a trained AI network. The StyleGAN2-ADA was fed 20,000 images of beaches to allow it to create some curious beach views. Often of beautiful and idyllic beaches (free from people) that you'll sadly never be able to visit.

This art does not exist

It seems like AI might be pretty good at making artwork. Especially abstract works of art that cause you to be puzzled when you look at them anyway.

This chair does not exist

Like a number of other things on this list, the chair was created using generative adversarial networks that were trained. In this case, trained on a dataset of different chairs.

Interestingly the site lets you generate and change chairs on the fly. The best thing about this is you can tweak the weirdness scale to make the chairs "normal" or completely bonkers. Get some inspiration for a new chair design or just marvel at the bonkers seating.

This cereal does not exist

This cereal does not exist. Which is good news because it does not look healthy.

It was created using Dalle-2 with the request for "A box of fruity pebbles on a grocery store shelf". Perhaps AI image generation could be used to come up with new cereal ideas or at least the design of the boxes.

This cat does not exist

Here's an interesting creation. A site that lets you create your images of things based on your drawings. You can draw cats, shoes or handbags and the system will try to create images based on what you drew.

This uses AI trained on images of your chosen item. The results are weird and wonderful, depending on your drawing abilities.

This news does not exist

A team of scientists have created an artificial intelligence that's capable of writing worryingly convincing fake news.

Fake news is a big problem on the web and the idea that an AI could write believable news with relative ease is troubling.

According to the research though, GROVER is able to also analyse and recognise fake news posted elsewhere. At the time it was said to be able to detect AI-written fake news more effectively than any other such tool.

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All these things were made by AI and don't actually exist

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Fake news, deep fakes and AI-generated content. The web is rife with it. Now generative adversarial networks are being used to create realistic things that don't actually exist.

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data sources:

* reports from each country, updated weekly
* must be verified by multiple sources (triangulated)
* sources: media, vetted social media accounts, government and NGO reports, and partner organizations
* interesting: local media sources are more reliable than international sources

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Indonesia
- District level violence data in Indonesia (SNPK)
- PODES (violence measure within Indonesian census)
- Data on armed confrontation (Conflict analysis resource center, CERAC)

Colombia
- Center for Research and Popular Education/Peace Program
- Data on armed confrontation (Conflict analysis resource center, CERAC)

Other variables:
- population
- geography
- remoteness
- poverty, inequality
- other socio-economic factors

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predictive factors:
-socioeconomic indicators for infant mortality
-oil dependence
-education rates
-demographic indicators for ethnic composition
-youth bulges
-population size
-regional markers

dataset:
-UCDP/PRIO Armed Conflict Database

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The Uppsala Conflict Data Program (UCDP) has recorded ongoing violent conflicts since the 1970s. The data provided is one of the most accurate and well-used data-sources on global armed conflicts and its definition of armed conflict is becoming a standard in how conflicts are systematically defined…

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Conflict Forecast Map & Conflict Forecast Resource Mapper

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Conflict Forecast Map & Conflict Forecast Resource Mapper

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- Two sets of Machine Learning (Random Forest & LSTM)
- Input data are survey data results from various websites (United Nations, journal papers…)

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American Association for the Advancement of Science (AAAS)

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Human Rights Applications of Remote Sensing

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Since the creation of the Geospatial Technologies and Human Rights Project in 2005, the Project has worked to expand the applications of geospatial technologies to human rights. Geospatial technologies include a range of modern tools, such as remote sensing, geographic information systems (GIS), and…