# data[].attributes.attachment_link.title
How to Determine the Distance Between Geographic Coordinates?

# data[].attributes.body
Παιδιά πολλά συγχαρητήρια!!!! Πήραμε "FLYING STATUS"

# data[].attributes.attachment_link.readable_text
def convert(angle):
    # Convert a `skyfield` Angle to an Exif-appropriate
    # representation (positive rationals)
    # e.g. 98�� 34' 58.7 to "98/1,34/1,587/10"
    # Return a tuple containing a Boolean and the converted angle,
    # with the Boolean indicating if the angle is negative
    sign, degrees, minutes, seconds = angle.signed_dms()
    exif_angle = f'{degrees:.0f}/1,{minutes:.0f}/1,{seconds*10:.0f}/10'
    return sign < 0, exif_angle

# data[].attributes.body
Για custom capture...

# data[].attributes.attachment_link.readable_text
def custom_capture(iss, camera, base_folder, image):
    # Use `camera` to capture an `image` file with lat/long Exif data
    point = iss.coordinates()
    # Convert the latitude and longitude to Exif-appropriate
    # representations
    south, exif_latitude = convert(point.latitude)
    west, exif_longitude = convert(point.longitude)
    # Set the Exif tags specifying the current location
    camera.exif_tags['GPS.GPSLatitude'] = exif_latitude
    camera.exif_tags['GPS.GPSLatitudeRef'] = "S" if south else "N"
    camera.exif_tags['GPS.GPSLongitude'] = exif_longitude
    camera.exif_tags['GPS.GPSLongitudeRef'] = "W" if west else "E"
    # Capture the image
    camera.capture(str(base_folder) + "/" + image)
    
#How to call custom_capture
custom_capture(ISS(), cam, base_folder, f"image{idx}.jpg")

# data[].attributes.body
Στην έκδοση αυτή θα χρειαστεί να αλλάξετε τον κώδικα στην γραμμή 3 ώστε το path να δείχνει στο σωστό αρχείο.

# data[].attributes.attachment_link.readable_text
import os
import sys
libPath = 'C:/Users/michali/AppData/Roaming/Python/Python310/site-packages/astro_pi_replay'
if not libPath in sys.path: sys.path.append(libPath)
import pandas as pd
import math
from datetime import datetime
from datetime import timedelta
from pathlib import Path
#import matplotlib.pyplot as plt
import numpy as np
#from picamzero import Camera
from picamera import PiCamera
from orbit import ISS
from time import sleep
from sense_hat import SenseHat
import csv

base_folder = Path(__file__).parent.resolve()
data_file1 = base_folder / "result.txt"
data_file2 = base_folder / "log_file.txt"
data_file3 = base_folder / "data.csv"
file2 = open(data_file2, "w", buffering=1)
sense = SenseHat()

def covert_degrees_radians(degrees):
    return degrees*math.pi/180

def get_gps_coordinates(iss):
    point = iss.coordinates()
    return (point.latitude.signed_dms(), point.longitude.signed_dms())

def create_csv_file(data_file):
    """Create a new CSV file and add the header row"""
    with open(data_file, 'w', newline='') as f:
        writer = csv.writer(f)
        header = ("Counter", "Date/time", "Latitude", "Longitude", "Temperature", "Humidity", "Pressure", "Compass")
        writer.writerow(header)

def add_csv_data(data_file, data):
    """Add a row of data to the data_file CSV"""
    with open(data_file, 'a', newline='') as f:
        writer = csv.writer(f)
        writer.writerow(data)
        
earth_rad = 3440.1 #miles
earth_rad = 6378 #kms
pythLst = [0]
harvLst = [0]
wait_time = 20 #seconds
velocityList =[]
iss = ISS()
cam = PiCamera()
#for i in range(len(data_set)-1):
# Create a variable to store the start time
start_time = datetime.now()
# Create a variable to store the current time
# (these will be almost the same at the start)
now_time = datetime.now()
i = 1
create_csv_file(data_file3)
while (now_time < start_time + timedelta(minutes=2)):
    try:
        if i < 40:
            imgStr = str(base_folder) + "/" f"image{i}.jpg"
            cam.capture(imgStr)
            #cam.take_photo(imgStr, gps_coordinates=get_gps_coordinates(iss))

        file2.write("================================================================================\n")
        file2.write(f"Iteration = {i}\n")
        location = ISS().coordinates()


        radLongA = location.longitude.radians
        radLatA = location.latitude.radians
        degLongA = location.longitude.degrees
        degLatA = location.latitude.degrees
        elevA = location.elevation.km

        file2.write(f'LongitudeA degrees = {degLongA}\n')
        file2.write(f'LatitudeA degrees = {degLatA}\n')
        
        #Taking measurements from the sensors
        humidity = round(sense.humidity, 1)
        temperature = round(sense.temperature, 1)
        pressure = round(sense.pressure, 1)
        compass = round(sense.compass, 1)
        data = i, datetime.now(), degLatA, degLongA, temperature, humidity, pressure, compass
        add_csv_data(data_file3, data)
        
        sleep(wait_time)
        
        location = ISS().coordinates()

        radLongB = location.longitude.radians
        radLatB = location.latitude.radians
        degLongB = location.longitude.degrees
        degLatB = location.latitude.degrees
        elevB = location.elevation.km
        file2.write(f'LongitudeB degress = {degLongB}\n')
        file2.write(f'LatitudeB degrees = {degLatB}\n')
        file2.write(f"Altitude = {location.elevation.km:.2f} km\n")
        distance = (earth_rad + elevB)*math.acos((math.sin(radLatA)*math.sin(radLatB))+
                                    math.cos(radLatA)*math.cos(radLatB)*math.cos(radLongA-radLongB))
        distance = distance + (1670/3600) * wait_time  + abs(elevB-elevA)
        velocity = distance / wait_time
        file2.write("distance =  %2.2f km, velocity =  %.5f km/sec latB - latA = %.5f\n" %(distance, velocity, (radLatB - radLatA)))
        velocityList.append(velocity)
        file2.write("================================================================================\n")
        
        #Taking measurements from the sensors
        humidity = round(sense.humidity, 1)
        temperature = round(sense.temperature, 1)
        pressure = round(sense.pressure, 1)
        compass = round(sense.compass, 1)
        data = i, datetime.now(), degLatA, degLongA, temperature, humidity, pressure, compass
        add_csv_data(data_file3, data)
        
        i += 1
        now_time = datetime.now()
    except Exception as e:
        msg = f"exception occured!!!!! ({e})\n"
        print(msg)
        file2.write(msg)
        quit()

        

velocityList.sort()
estimate_kmps = velocityList[len(velocityList)//2]
print(f'Estimates Velocity = {estimate_kmps:.4f} km/sec')

with open(data_file1, "w", buffering=1) as file1:
    file1.write(f"{estimate_kmps:.4f}\n")

print("data written to", data_file1)
file2.write(f"execution time {datetime.now()-start_time}\n")
print(f"execution time {datetime.now()-start_time}")
file2.close()

# data[].attributes.attachment_link.description
Contribute to ManosFps1pt/astro-pi-mission development by creating an account on GitHub.

# data[].attributes.body
Αφού κατεβάσετε το αρχείο μετονομάστε το σε main.py και τρέξτε το στο https://missions.astro-pi.org/msl/replay-tool

# data[].attributes.attachment_link.readable_text
import os
import sys
libPath = 'C:/Users/michali/AppData/Roaming/Python/Python310/site-packages/astro_pi_replay'
if not libPath in sys.path: sys.path.append(libPath)
import pandas as pd
import math
from datetime import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
#from picamzero import Camera
from orbit import ISS
from time import sleep

def covert_degrees_radians(degrees):
    return degrees*math.pi/180

def get_gps_coordinates(iss):
    point = iss.coordinates()
    return (point.latitude.signed_dms(), point.longitude.signed_dms())

earth_rad = 3440.1 #miles
pythLst = [0]
harvLst = [0]
wait_time = 5 #seconds
velocityList =[]
iss = ISS()
#cam = Camera()
#for i in range(len(data_set)-1):
for i in range(10):
    location = ISS().coordinates()
    print(f'LongitudeA degrees = {location.longitude.degrees}')
    print(f'LatitudeA degrees = {location.latitude.degrees}')
    print(f'LongitudeA radians = {location.longitude.radians}')
    print(f'LatitudeA radians = {location.latitude.radians}')
    radLongA = location.longitude.radians
    radLatA = location.latitude.radians
    elevA = location.elevation.km
    sleep(wait_time)
    location = ISS().coordinates()
    print(f'LongitudeB degress = {location.longitude.degrees}')
    print(f'LatitudeB degrees = {location.latitude.degrees}')
    print(f'LongitudeB radians = {location.longitude.radians}')
    print(f'LatitudeB radians = {location.latitude.radians}')
    radLongB = location.longitude.radians
    radLatB = location.latitude.radians
    elevB = location.elevation.km
    distance = (earth_rad)*math.acos((math.sin(radLatA)*math.sin(radLatB))+
                                math.cos(radLatA)*math.cos(radLatB)*math.cos(radLongA-radLongB))
    distance = distance*1.853 + (1670/3600) * wait_time  + abs(elevB-elevA)
    velocity = distance / wait_time
    print("distance =  %2.2f km, velocity =  %.5f km/sec latB - latA = %.5f" %(distance, velocity, (radLatB - radLatA)))
    velocityList.append(velocity)
    print("====================================================================")

velocityList.sort()
estimate_kmps = velocityList[len(velocityList)//2]
print(f'Estimates Velocity = {estimate_kmps:.5f} km/sec')

# data[].attributes.headline
haversine formula - pythagorean - graphs - speed estimation

# data[].attributes.subject
haversine formula - pythagorean - graphs - speed estimation

# data[].attributes.attachment_link.readable_text
import pandas as pd
import math
from datetime import datetime as dt
import matplotlib.pyplot as plt


data_path = "iss-data.csv"
data_set = pd.read_csv(data_path)
print(f"Row Count = {len(data_set):5d}")
#print("Row Count = {:5d}".format(len(data_set)))
earth_rad = 3440.1 # nautical miles

haversine_velocity_list = []
pythagorean_velocity_list = []
latitude_list = []
longitude_list = []
times_list = []
inaccuracy_rate = []
iss_speed = 7.66
i = 0

def covert_degrees_radians(degrees):
    return degrees*math.pi/180

def haversine_formula(long1: float, lat1: float, long2: float, lat2: float) -> float:
    return (
        3440.1
        * math.acos (
            (
                math.sin (
                    lat1
                ) * math.sin (
                    lat2
                )
            ) + math.cos (
                    lat1
                ) * math.cos (
                    lat2
                ) * math.cos (
                long1 - long2
            )
        )
    )

def hf(radLongA, radLatA, radLongB, radLatB):
    distance = earth_rad * math.acos((math.sin(radLatA) * math.sin(radLatB)) +
                                     math.cos(radLatA) * math.cos(radLatB) * math.cos(radLongA - radLongB))
    return distance


while i < len(data_set) - 1: 
    print(f"----- ITERATION {i} -----")
    latA = data_set.iloc[i,1]
    longA = data_set.iloc[i,2]
    latB = data_set.iloc[i+1,1]
    longB = data_set.iloc[i+1,2]
    timeA = data_set.iloc[i,0]
    timeB = data_set.iloc[i+1,0]

    timeA = dt.strptime(timeA, '%Y-%m-%d %H:%M:%S')
    timeB = dt.strptime(timeB, '%Y-%m-%d %H:%M:%S')
    deltaT = (timeB-timeA).seconds

    latA_radians = covert_degrees_radians(latA)
    latB_radians = covert_degrees_radians(latB)
    longA_radians = covert_degrees_radians(longA)
    longB_radians = covert_degrees_radians(longB)

    y_difference = (abs(latB) - abs(latA)) * 111_111   #To convert degrees to meters we multiply by 111111#
    x_difference = (abs(longB) - abs(longA)) * 111_111 #To convert degrees to meters we multiply by 111111#
    haversine_distance = hf(longA_radians, latA_radians, longB_radians, latB_radians)
    pythagorean_distance = math.hypot(x_difference, y_difference)
    haversine_velocity_km_per_sec = ((haversine_distance * 1.853) + (1670/3600) * deltaT)  / deltaT
    pythagorean_velocity_km_per_sec = pythagorean_distance / deltaT / 1000

    haversine_velocity_list.append(haversine_velocity_km_per_sec)
    latitude_list.append(latA)
    times_list.append(timeA)
    longitude_list.append(longA)
    inaccuracy_rate.append(abs(haversine_velocity_km_per_sec - iss_speed))
    pythagorean_velocity_list.append(pythagorean_velocity_km_per_sec)
    i += 1
    # print(f"latitudeA = {latA:.3f} degrees longitudeA = {longA:.3f} degrees")
    # print("latitudeB = {:.3f} degrees longitudeB = {:.3f} degrees".format(latB, longB))
    # print(f"timeA = {timeA} timeB = {timeB} DeltaT = {deltaT} seconds")
    # print(f"velocity = {velocity_km_per_sec:.2f} km/sec")
print(f"median speed = {haversine_velocity_list[len(haversine_velocity_list) // 2]:.2f} km/sec")
print(f"average speed = {sum(haversine_velocity_list)/len(haversine_velocity_list):.2f} km/sec")
print(max(latitude_list))
plt.plot(times_list, latitude_list, c="red", label="latitude", linewidth=.5)
plt.plot(times_list, haversine_velocity_list, c="green", label="haversine velocity estimation", linewidth=.5)
plt.plot(times_list, longitude_list, c="blue", label="longitude", linewidth=.5)
plt.plot(times_list, [iss_speed for _ in range(len(times_list))], c="black", label="iss velocity", linewidth=2)
plt.plot(times_list, pythagorean_velocity_list, c="purple", label="Pythagorean velocity estimation", linewidth=.5)
# plt.plot(times_list, inaccuracy_rate, c="yellow", label="inaccuracy rate", linewidth=.5)
plt.ylabel("latitude-longitude-speed")
plt.xlabel("time")
plt.title("data visualization")
plt.legend()
plt.show()


# data[].attributes.headline
Υπολογισμός ταχύτητας από γεωγραφικές συντεταγμένες και Πυθαγόρειο Θεώρημα

# data[].attributes.subject
Υπολογισμός ταχύτητας από γεωγραφικές συντεταγμένες και Πυθαγόρειο Θεώρημα

# data[].attributes.attachment_link.readable_text
import pandas as pd
import math
from datetime import datetime as dt

data_set = pd.read_csv('iss-data.csv')
print(f"Row Count = {len(data_set):5d}")
#print("Row Count = {:5d}".format(len(data_set)))
earth_rad = 3440.1 #miles

latA = data_set.iloc[0,1]
longA = data_set.iloc[0,2]
latB = data_set.iloc[1,1]
longB = data_set.iloc[1,2]
timeA = data_set.iloc[0,0]
timeB = data_set.iloc[1,0]

timeA = dt.strptime(timeA, '%Y-%m-%d %H:%M:%S')
timeB = dt.strptime(timeB, '%Y-%m-%d %H:%M:%S')
deltaT = (timeB-timeA).seconds

y_difference = (latB - latA) * 111111   #To convert degrees to meters we multiply by 111111#
x_difference = (longB - longA) * 111111 #To convert degrees to meters we multiply by 111111#
distance = math.hypot(x_difference, y_difference)
velocity_km_per_sec = distance/deltaT/1000

print(f"latitudeA = {latA:.3f} degress longitudeA = {longA:.3f} degrees")
print("latituteB = {:.3f} degrees longitudeB = {:.3f} degrees".format(latB, longB))
print(f"timeA = {timeA} timeB = {timeB} DeltaT = {deltaT} seconds")
print(f"velocity = {velocity_km_per_sec:.2f} km/sec")

# data[].attributes.headline
Υπολογισμός ταχύτητας από πολλές φωτογραφίες

# data[].attributes.subject
Υπολογισμός ταχύτητας από πολλές φωτογραφίες

# data[].attributes.attachment_link.readable_text
from exif import Image
from datetime import datetime
import cv2
import math
import os, sys

def get_time(image):
    with open(image, 'rb') as image_file:
        img = Image(image_file)
        time_str = img.get("datetime_original")
        time = datetime.strptime(time_str, '%Y:%m:%d %H:%M:%S')
    return time
            
def get_time_difference(image_1, image_2):
    time_1 = get_time(image_1)
    time_2 = get_time(image_2)
    time_difference = time_2 - time_1
    return time_difference.seconds
    
def convert_to_cv(image_1, image_2):
    image_1_cv = cv2.imread(image_1, 0)
    image_2_cv = cv2.imread(image_2, 0)
    return image_1_cv, image_2_cv

def calculate_features(image_1, image_2, feature_number):
    orb = cv2.ORB_create(nfeatures = feature_number)
    keypoints_1, descriptors_1 = orb.detectAndCompute(image_1_cv, None)
    keypoints_2, descriptors_2 = orb.detectAndCompute(image_2_cv, None)
    return keypoints_1, keypoints_2, descriptors_1, descriptors_2

def calculate_matches(descriptors_1, descriptors_2):
    brute_force = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
    matches = brute_force.match(descriptors_1, descriptors_2)
    matches = sorted(matches, key=lambda x: x.distance)
    return matches

def display_matches(image_1_cv, keypoints_1, image_2_cv, keypoints_2, matches):
    match_img = cv2.drawMatches(image_1_cv, keypoints_1, image_2_cv, keypoints_2, matches[:100], None)
    resize = cv2.resize(match_img, (1600,600), interpolation = cv2.INTER_AREA)
    cv2.imshow('matches', resize)
    cv2.waitKey(0)
    cv2.destroyWindow('matches')
    
def find_matching_coordinates(keypoints_1, keypoints_2, matches):
    coordinates_1 = []
    coordinates_2 = []
    for match in matches:
        image_1_idx = match.queryIdx
        image_2_idx = match.trainIdx
        (x1,y1) = keypoints_1[image_1_idx].pt
        (x2,y2) = keypoints_2[image_2_idx].pt
        coordinates_1.append((x1,y1))
        coordinates_2.append((x2,y2))
    return coordinates_1, coordinates_2

def calculate_mean_distance(coordinates_1, coordinates_2):
    all_distances = 0
    merged_coordinates = list(zip(coordinates_1, coordinates_2))
    for coordinate in merged_coordinates:
        x_difference = coordinate[0][0] - coordinate[1][0]
        y_difference = coordinate[0][1] - coordinate[1][1]
        distance = math.hypot(x_difference, y_difference)
        all_distances = all_distances + distance
    return all_distances / len(merged_coordinates)

def calculate_speed_in_kmps(feature_distance, GSD, time_difference):
    distance = feature_distance * GSD / 100000
    speed = distance / time_difference
    return speed

def myFunc(e):
    s = "";
    for x in e:
        if x.isdigit():
            s = s + x
    return(int(s))

image_1 = 'photo_0683.jpg'
image_2 = 'photo_0684.jpg'

image_1 = 'photo_01929.jpg'
image_2 = 'photo_01930.jpg'

list_dir = os.listdir("images/")
list_dir.sort(key = myFunc)

for i in range(len(list_dir)):
    file_name = list_dir[i]
    if file_name.endswith(".jpg"):
        print(f"image = {file_name}" )

i = 0
n = len(list_dir)
print("Len cur dir = %4d" %(len(list_dir)))
for file in list_dir:
    if file.endswith('.jpg'):
        image_1 = list_dir[i]
        image_2 = list_dir[i+1]
        print("image1 = %s image2 = %s" %(image_1, image_2))
        time_difference = get_time_difference("images/"+image_1, "images/"+image_2) # Get time difference between images
        image_1_cv, image_2_cv = convert_to_cv("images/"+image_1, "images/"+image_2) # Create OpenCV image objects
        keypoints_1, keypoints_2, descriptors_1, descriptors_2 = calculate_features(image_1_cv, image_2_cv, 1000) # Get keypoints and descriptors
        matches = calculate_matches(descriptors_1, descriptors_2) # Match descriptors
        display_matches(image_1_cv, keypoints_1, image_2_cv, keypoints_2, matches) # Display matches
        coordinates_1, coordinates_2 = find_matching_coordinates(keypoints_1, keypoints_2, matches)
        average_feature_distance = calculate_mean_distance(coordinates_1, coordinates_2)
        speed = calculate_speed_in_kmps(average_feature_distance, 12648, time_difference)
        print("speed = %.2fkm/sec" %(speed))
    i = i + 1
    if i == (n - 1):
        break

# data[].attributes.headline
Μετατροπή γωνίας (μοίρες) σε απόσταση

# data[].attributes.subject
Μετατροπή γωνίας (μοίρες) σε απόσταση

# data[].attributes.attachment_link.title
How To Convert Distances From Degrees To Meters

# data[].attributes.attachment_link.description
Linear distance and angular separation are related on the earth’s surface because the earth is a sphere, so you can express the distance between two points as an angle or as a linear distance.

# data[].attributes.attachment_link.title
Projects | Computer coding for kids and teens | Raspberry Pi

# data[].attributes.attachment_link.description
Computer coding for kids, teenagers and young adults. Step-by-step instructions for 250+ free coding projects. Create games, animations, and more with code.

# data[].attributes.headline
Δεδομένα από τον ISS

# data[].attributes.body
Δεδομένα από τον ISS

# data[].attributes.attachment_link.title
Astro PI Mark II commissioning data - IR

# data[].attributes.attachment_link.title
Why is the ISS at 51.6 degrees orbital inclination? What is the rationale behind the decision to have it at 51.6 degrees? Does that angle...

# data[].attributes.attachment_link.description
Answer (1 of 7): Traditionally, NASA vehicles launched from the Cape go into orbits with an inclination of ~28 degrees (the latitude of Cape Canaveral). Russian vehicles typically launch into orbits with an inclination of 51.6 degrees (I'll explain why in a moment). We can get a spacecraft to an...

# data[].attributes.attachment_link.description
For the purposes of planning Earth observing photography or remote sensing, there are four important points about the orbits of the ISS. Particulars of the orbits depend on the exact altitude of the station, and the exact altitude depends on the frequency that the station is reboosted to a higher orbit.

# data[].attributes.attachment_link.title
Projects | Computer coding for kids and teens | Raspberry Pi

# data[].attributes.attachment_link.description
Computer coding for kids, teenagers and young adults. Step-by-step instructions for 250+ free coding projects. Create games, animations, and more with code.

# data[].attributes.attachment_link.provider_name
N2YO.com - real time satellite tracking and predictions

# data[].attributes.attachment_link.description
Select any satellite orbiting the Earth and check where it is located now

# data[].attributes.headline
Η Καμπυλότητα της Γης

# data[].attributes.subject
Η Καμπυλότητα της Γης

# data[].attributes.headline
Γεωγραφικό Σύστημα Συντεταγμένων

# data[].attributes.subject
Γεωγραφικό Σύστημα Συντεταγμένων

# data[].attributes.attachment_link.readable_text
A geographic coordinate system is a system that uses a three-dimensional spherical surface to determine locations on the Earth. Any location on Earth can be referenced by a point with longitude and latitude coordinates. The geographic coordinate system is appropriate for global data sets and applications, such as satellite imagery repositories.

For example, the following illustration shows a geographic coordinate system where a location is represented by the coordinates longitude 80 degree east and latitude 55 degrees north.

The lines that run east and west each have a constant latitude value and are called parallels. They are equidistant and parallel to one another, and form concentric circles around the Earth. The equator is the largest circle and divides the Earth in half. It is equal in distance from each of the poles, and the value of this latitude line is zero. Locations north of the equator have positive latitudes that range from 0 to +90 degrees, while locations south of the equator have negative latitudes that range from 0 to -90 degrees.

The following illustration shows latitude lines.

The lines that run north and south each have a constant longitude value and are called meridians. They form circles of the same size around the Earth, and intersect at the poles. The prime meridian is the line of longitude that defines the origin (zero degrees) for longitude coordinates. One of the most commonly used prime meridian locations is the line that passes through Greenwich, England. However, other longitude lines, such as those that pass through Bern, Bogota, and Paris, were also as the prime meridian. Locations east of the prime meridian up to its antipodal meridian (the continuation of the prime meridian on the other side of the globe) have positive longitudes that range from 0 to +180 degrees. Locations west of the prime meridian have negative longitudes that range from 0 to -180 degrees.

The following illustration shows longitude lines.

The latitude and longitude lines can cover the globe to form a grid, called a graticule. The point of origin of the graticule is (0,0), where the equator and the prime meridian intersect. The equator is the only place on the graticule where the linear distance corresponding to one degree latitude is approximately equal the distance corresponding to one degree longitude. Because the longitude lines converge at the poles, the distance between two meridians is different at every parallel. Therefore, as you move closer to the poles, the distance corresponding to one degree latitude is much greater than the distance corresponding to one degree longitude.

It is difficult to determine the lengths of the latitude lines using the graticule. The latitude lines are concentric circles that become smaller near the poles. They form a single point at the poles where the meridians begin. At the equator, one degree of longitude is approximately 111.321 kilometers, while at 60 degrees of latitude, one degree of longitude is only 55.802 km (this approximation is based on the Clarke 1866 spheroid). Therefore, because there is no uniform length of degrees of latitude and longitude, the distance between points cannot be measured accurately by using angular units of measure.

The following illustration shows the different dimensions between locations on the graticule.

A coordinate system can be defined by either a sphere or a spheroid approximation of the Earth's shape. Because the Earth is not perfectly round, a spheroid can help maintain accuracy for a map, depending on the location on the Earth. A spheroid is an ellipsoid that is based on an ellipse, whereas a sphere is based on a circle.

The shape of the ellipse is determined by two radii. The longer radius is called the semimajor axis, and the shorter radius is called the semiminor axis. An ellipsoid is a three-dimensional shape that is formed by rotating an ellipse around one of its axes.

The following illustration shows the sphere and spheroid approximations of the Earth and the major and minor axes of an ellipse.

A datum is a set of values that defines the position of the spheroid relative to the center of the Earth. The datum provides a frame of reference for measuring locations and defines the origin and orientation of latitude and longitude lines. Some datums are global and intend to provide good average accuracy around the world. A local datum aligns its spheroid to closely fit the Earth's surface in a particular area. Therefore, the coordinate system's measurements are not accurate if they are used with an area other than the one that they were designed.

The following illustration shows how different datums align with the Earth's surface. The local datum, NAD27, more closely aligns with Earth's surface than the Earth-centered datum, WGS84, at this particular location.

Whenever you change the datum, the geographic coordinate system is altered and the coordinate values change. For example, the coordinates in DMS of a control point in Redlands, California using the North American Datum of 1983 (NAD 1983) are: "-117 12 57.75961 34 01 43.77884" The coordinates of the same point on the North American Datum of 1927 (NAD 1927) are: "-117 12 54.61539 34 01 43.72995".

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A geographic coordinate system is a system that uses a three-dimensional spherical surface to determine locations on the Earth. Any location on Earth can be referenced by a point with longitude and latitude coordinates. The geographic coordinate system is appropriate for global data sets and applications, such as satellite imagery repositories.

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Υπολογισμός Ταχύτητας από Φωτογραφίες

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Υπολογισμός Ταχύτητας από Φωτογραφίες