# Latitude, Longitude for any pixel in a GeoTiff File

How to generate the latitude and longitude for a pixel at any given position in a GeoTiff file.

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In [2]:

from osgeo import ogr, osr, gdal

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In [3]:

# opening the geotiff file
ds = gdal.Open('G:\BTP\Satellite\Data\Test2\LE07_L1GT_147040_20050506_20170116_01_T2\LE07_L1GT_147040_20050506_20170116_01_T2_B1.TIF')

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In [4]:

col, row, band = ds.RasterXSize, ds.RasterYSize, ds.RasterCount
print(col, row, band)

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(7801, 7071, 1)

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In [8]:

xoff, a, b, yoff, d, e = ds.GetGeoTransform()
print(xoff, a, b, yoff, d, e)

# details about the params: GDAL affine transform parameters
# xoff,yoff = left corner
# a,e = weight,height of pixels
# b,d = rotation of the image (zero if image is north up)

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(514485.0, 30.0, 0.0, 3300915.0, 0.0, -30.0)

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In [9]:

def pixel2coord(x, y):
"""Returns global coordinates from coordinates x,y of the pixel"""
xp = a * x + b * y + xoff
yp = d * x + e * y + yoff
return(xp, yp)

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In [10]:

x,y = pixel2coord(col/2,row/2)
print (x, y)

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(631485.0, 3194865.0)

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#### To convert these into latitude and longitude, we need to convert these coordinates into geographic coordinate system.

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In [11]:

# get the existing coordinate system
old_cs= osr.SpatialReference()
old_cs.ImportFromWkt(ds.GetProjectionRef())

# create the new coordinate system
wgs84_wkt = """
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.01745329251994328,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]]"""
new_cs = osr.SpatialReference()
new_cs.ImportFromWkt(wgs84_wkt)

# create a transform object to convert between coordinate systems
transform = osr.CoordinateTransformation(old_cs,new_cs)

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In [12]:

# converting into geographic coordinate system
lonx, latx, z = transform.TransformPoint(x,y)

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In [13]:

print (latx, lonx, z)

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(28.874828865052745, 76.34826151423796, 0.0)

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In [10]:

# rb = ds.GetRasterBand(1)
px,py = col/2,row/2                   # the pixel location
print pix[0][0]                        # pixel value

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11647

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# Reverse Geocoding

Converting a lat/long to a physical address or location.

We want the name of the DISTRICT.

## --------------------------------------------------------------------------------

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In [11]:

coordinates = (latx,lonx)
results = rg.search(coordinates)
print results
print type(results)
print type(results[0])
results[0]

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[{'name': 'Jaisalmer', 'cc': 'IN', 'lon': '70.90387', 'admin1': 'Rajasthan', 'admin2': 'Jaisalmer', 'lat': '26.91763'}]
<type 'list'>
<type 'dict'>

Out[11]:

'cc': 'IN',
'lat': '26.91763',
'lon': '70.90387',
'name': 'Jaisalmer'}

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In [12]:

k = 4  # If we want k*k pixels in total from the image

for i in range(0,col,col/k):
for j in range(0,row,row/k):

# fetching the lat and lon coordinates
x,y = pixel2coord(i,j)
lonx, latx, z = transform.TransformPoint(x,y)

# fetching the name of district
coordinates = (latx,lonx)
results = rg.search(coordinates)

# The pixel value for that location
px,py = i,j
pix = pix[0][0]

# printing
s = "The pixel value for the location Lat: {0:5.1f}, Long: {1:5.1f} ({2:15}) is {3:7}".format(latx,lonx,results[0]["name"],pix)
print (s)

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The pixel value for the location Lat:  28.5, Long:  70.2 (Sadiqabad      ) is       0
The pixel value for the location Lat:  28.0, Long:  70.2 (Sadiqabad      ) is       0
The pixel value for the location Lat:  27.4, Long:  70.2 (Khairpur       ) is       0
The pixel value for the location Lat:  26.9, Long:  70.2 (Jaisalmer      ) is       0
The pixel value for the location Lat:  26.4, Long:  70.2 (Jaisalmer      ) is       0
The pixel value for the location Lat:  28.5, Long:  70.8 (Khanpur        ) is       0
The pixel value for the location Lat:  28.0, Long:  70.8 (Kot Samaba     ) is   11992
The pixel value for the location Lat:  27.4, Long:  70.8 (Jaisalmer      ) is   11661
The pixel value for the location Lat:  26.9, Long:  70.8 (Jaisalmer      ) is   11701
The pixel value for the location Lat:  26.4, Long:  70.8 (Jaisalmer      ) is       0
The pixel value for the location Lat:  28.5, Long:  71.4 (Ahmadpur East  ) is       0
The pixel value for the location Lat:  27.9, Long:  71.4 (Khanpur        ) is   11247
The pixel value for the location Lat:  27.4, Long:  71.4 (Jaisalmer      ) is   11653
The pixel value for the location Lat:  26.9, Long:  71.3 (Jaisalmer      ) is   11307
The pixel value for the location Lat:  26.4, Long:  71.3 (Barmer         ) is       0
The pixel value for the location Lat:  28.5, Long:  72.0 (Yazman         ) is       0
The pixel value for the location Lat:  27.9, Long:  71.9 (Phalodi        ) is   11547
The pixel value for the location Lat:  27.4, Long:  71.9 (Pokaran        ) is   11637
The pixel value for the location Lat:  26.9, Long:  71.9 (Pokaran        ) is   11881
The pixel value for the location Lat:  26.4, Long:  71.9 (Pokaran        ) is       0
The pixel value for the location Lat:  28.4, Long:  72.5 (Fort Abbas     ) is       0
The pixel value for the location Lat:  27.9, Long:  72.5 (Bikaner        ) is       0
The pixel value for the location Lat:  27.4, Long:  72.5 (Phalodi        ) is       0
The pixel value for the location Lat:  26.9, Long:  72.5 (Phalodi        ) is       0
The pixel value for the location Lat:  26.4, Long:  72.5 (Jodhpur        ) is       0

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## --------------------------------------------------------------------------------

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In [13]:

print type(g)
print g
print g.city
print g.state
print g.state_long
print g.country
print g.country_long

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<[OK] Google - Reverse #6 results>
Amrit Nagar
RJ
Rajasthan
IN
India
Unnamed Road, Amrit Nagar, Rajasthan 342023, India

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##### Lets try another python library available for the Google Geo API
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In [14]:

results = Geocoder.reverse_geocode(latx, lonx)
print results.city
print results.country
print results.administrative_area_level_2  ## THIS GIVES THE DISTRICT !! <----------------

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Amrit Nagar
India
None
Rajasthan
Barmer
None

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##### This is what we need, we are getting the district name for given lat,lon coordinates
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In [15]:

## Converting the unicode string to ascii string
v = results.country
print type(v)
v = v.encode("ascii")
print type(v)
print v

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<type 'unicode'>
<type 'str'>
India

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##### Now lets check for an image from Rajasthan
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In [16]:

k = 4  # If we want k*k pixels in total from the image

for i in range(0,col,col/k):
for j in range(0,row,row/k):

# fetching the lat and lon coordinates
x,y = pixel2coord(i,j)
lonx, latx, z = transform.TransformPoint(x,y)

# fetching the name of district
results = Geocoder.reverse_geocode(latx, lonx)

# The pixel value for that location
px,py = i,j
pix = pix[0][0]

# printing
if results.country.encode('ascii') == 'India':
s = "Lat: {0:5.1f}, Long: {1:5.1f}, District: {2:12}, Pixel Val: {3:7}".format(latx,lonx,results.administrative_area_level_2,pix)
print (s)

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Lat:  27.4, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  26.9, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.4, Long:  70.8, District: Jaisalmer   , Pixel Val:   11661
Lat:  26.9, Long:  70.8, District: Jaisalmer   , Pixel Val:   11701
Lat:  26.4, Long:  70.8, District: Jaisalmer   , Pixel Val:       0
Lat:  27.4, Long:  71.4, District: Jaisalmer   , Pixel Val:   11653
Lat:  26.9, Long:  71.3, District: Jaisalmer   , Pixel Val:   11307
Lat:  26.4, Long:  71.3, District: Barmer      , Pixel Val:       0
Lat:  27.9, Long:  71.9, District: Jaisalmer   , Pixel Val:   11547
Lat:  27.4, Long:  71.9, District: Jodhpur     , Pixel Val:   11637
Lat:  26.9, Long:  71.9, District: Jaisalmer   , Pixel Val:   11881
Lat:  26.4, Long:  71.9, District: Barmer      , Pixel Val:       0
Lat:  28.4, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  27.9, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  27.4, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  26.9, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  26.4, Long:  72.5, District: Barmer      , Pixel Val:       0

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# Bing Maps REST API

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In [17]:

import requests # To make the REST API Call
import json

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In [18]:

(latx,lonx)

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Out[18]:

(26.362894876071923, 72.46299451955348)

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In [19]:

url = "http://dev.virtualearth.net/REST/v1/Locations/"
point = str(latx)+","+str(lonx)
key = "Aktjg1X8bLQ_KhLQbVueYMhXDEMo7OaTweIkBvFojInYE4tVxoTp1bGKWbtU_OPJ"
response = requests.get(url+point+"?key="+key)
print(response.status_code)

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In [22]:

data = response.json()
print(type(data))

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<type 'dict'>

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In [23]:

data

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Out[23]:

{u'authenticationResultCode': u'ValidCredentials',
u'brandLogoUri': u'http://dev.virtualearth.net/Branding/logo_powered_by.png',
u'copyright': u'Copyright \xa9 2017 Microsoft and its suppliers. All rights reserved. This API cannot be accessed and the content and any results may not be used, reproduced or transmitted in any manner without express written permission from Microsoft Corporation.',
u'resourceSets': [{u'estimatedTotal': 1,
u'resources': [{u'__type': u'Location:http://schemas.microsoft.com/search/local/ws/rest/v1',
u'countryRegion': u'India',
u'locality': u'Shergarh',
u'postalCode': u'342028'},
u'bbox': [26.18815, 72.02842, 26.8617, 72.77681],
u'confidence': u'Medium',
u'entityType': u'PopulatedPlace',
u'geocodePoints': [{u'calculationMethod': u'Rooftop',
u'coordinates': [26.46525, 72.40963],
u'type': u'Point',
u'usageTypes': [u'Display', u'Route']}],
u'matchCodes': [u'Good', u'UpHierarchy'],
u'name': u'Shergarh, RJ, India',
u'point': {u'coordinates': [26.46525, 72.40963], u'type': u'Point'}}]}],
u'statusCode': 200,
u'statusDescription': u'OK',
u'traceId': u'6b6742bcfe4a45e7a40575ac7c554d55|HK20300663|7.7.0.0|HK2SCH010250442, i-0ec338f04ce8e61e8.ap-southeast-1a'}

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In [24]:

s = s.encode("ascii")
s

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Out[24]:

'Jodhpur'

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In [25]:

url = "http://dev.virtualearth.net/REST/v1/Locations/"
key = "Aktjg1X8bLQ_KhLQbVueYMhXDEMo7OaTweIkBvFojInYE4tVxoTp1bGKWbtU_OPJ"

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## Bing API Test

#### For 100 pixel locations

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In [33]:

k = 10  # If we want k*k pixels in total from the image

for i in range(0,col,col/k):
for j in range(0,row,row/k):

############### fetching the lat and lon coordinates #######################################
x,y = pixel2coord(i,j)
lonx, latx, z = transform.TransformPoint(x,y)

############### fetching the name of district ##############################################
point = str(latx)+","+str(lonx)
response = requests.get(url+point+"?key="+key)
data = response.json()
if s["countryRegion"].encode("ascii") != "India":
print ("Outside Indian Territory")
continue

############### The pixel value for that location ##########################################
px,py = i,j
pix = pix[0][0]

# printing
s = "Lat: {0:5.1f}, Long: {1:5.1f}, District: {2:12}, Pixel Val: {3:7}".format(latx,lonx,district,pix)
print (s)

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Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.9, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.7, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.4, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.2, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.0, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  26.8, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  26.6, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.9, Long:  70.4, District: Jaisalmer   , Pixel Val:       0
Lat:  27.6, Long:  70.4, District: Jaisalmer   , Pixel Val:   11591
Lat:  27.4, Long:  70.4, District: Jaisalmer   , Pixel Val:   11860
Lat:  27.2, Long:  70.4, District: Jaisalmer   , Pixel Val:   12666
Lat:  27.0, Long:  70.4, District: Jaisalmer   , Pixel Val:   12044
Lat:  26.8, Long:  70.4, District: Jaisalmer   , Pixel Val:   12579
Lat:  26.6, Long:  70.4, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.4, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.9, Long:  70.7, District: Jaisalmer   , Pixel Val:   11610
Lat:  27.6, Long:  70.7, District: Jaisalmer   , Pixel Val:   11842
Lat:  27.4, Long:  70.7, District: Jaisalmer   , Pixel Val:   11577
Lat:  27.2, Long:  70.7, District: Jaisalmer   , Pixel Val:   12740
Lat:  27.0, Long:  70.7, District: Jaisalmer   , Pixel Val:   12015
Lat:  26.8, Long:  70.7, District: Jaisalmer   , Pixel Val:   11851
Lat:  26.6, Long:  70.7, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.6, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.6, Long:  70.9, District: Jaisalmer   , Pixel Val:   11602
Lat:  27.4, Long:  70.9, District: Jaisalmer   , Pixel Val:   12231
Lat:  27.2, Long:  70.9, District: Jaisalmer   , Pixel Val:   11390
Lat:  27.0, Long:  70.9, District: Jaisalmer   , Pixel Val:   11898
Lat:  26.8, Long:  70.9, District: Jaisalmer   , Pixel Val:   11736
Lat:  26.6, Long:  70.9, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.9, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.6, Long:  71.1, District: Jaisalmer   , Pixel Val:   12236
Lat:  27.4, Long:  71.1, District: Jaisalmer   , Pixel Val:   11572
Lat:  27.2, Long:  71.1, District: Jaisalmer   , Pixel Val:   12053
Lat:  27.0, Long:  71.1, District: Jaisalmer   , Pixel Val:   11647
Lat:  26.8, Long:  71.1, District: Jaisalmer   , Pixel Val:   11882
Lat:  26.6, Long:  71.1, District: Jaisalmer   , Pixel Val:   11974
Lat:  26.4, Long:  71.1, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.8, Long:  71.4, District: Jaisalmer   , Pixel Val:   11996
Lat:  27.6, Long:  71.4, District: Jaisalmer   , Pixel Val:   11815
Lat:  27.4, Long:  71.4, District: Jaisalmer   , Pixel Val:   11647
Lat:  27.2, Long:  71.3, District: Jaisalmer   , Pixel Val:   12070
Lat:  27.0, Long:  71.3, District: Jaisalmer   , Pixel Val:   11955
Lat:  26.8, Long:  71.3, District: Jaisalmer   , Pixel Val:   11199
Lat:  26.6, Long:  71.3, District: Jaisalmer   , Pixel Val:   11723
Lat:  26.4, Long:  71.3, District: Barmer      , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.8, Long:  71.6, District: Jaisalmer   , Pixel Val:   11977
Lat:  27.6, Long:  71.6, District: Jaisalmer   , Pixel Val:   11268
Lat:  27.4, Long:  71.6, District: Jaisalmer   , Pixel Val:   10623
Lat:  27.2, Long:  71.6, District: Jaisalmer   , Pixel Val:   11803
Lat:  27.0, Long:  71.6, District: Jaisalmer   , Pixel Val:   11594
Lat:  26.8, Long:  71.6, District: Jaisalmer   , Pixel Val:   11549
Lat:  26.6, Long:  71.6, District: Jaisalmer   , Pixel Val:   11265
Lat:  26.4, Long:  71.6, District: Barmer      , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.8, Long:  71.8, District: Jaisalmer   , Pixel Val:   11508
Lat:  27.6, Long:  71.8, District: Jaisalmer   , Pixel Val:   11511
Lat:  27.4, Long:  71.8, District: Jaisalmer   , Pixel Val:   11616
Lat:  27.2, Long:  71.8, District: Jaisalmer   , Pixel Val:   11215
Lat:  27.0, Long:  71.8, District: Jaisalmer   , Pixel Val:   11814
Lat:  26.8, Long:  71.8, District: Jaisalmer   , Pixel Val:   11511
Lat:  26.6, Long:  71.8, District: Jaisalmer   , Pixel Val:   11989
Lat:  26.4, Long:  71.8, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Lat:  28.2, Long:  72.1, District: Bikaner     , Pixel Val:       0
Lat:  28.0, Long:  72.1, District: Bikaner     , Pixel Val:   11208
Lat:  27.8, Long:  72.0, District: Jaisalmer   , Pixel Val:   11468
Lat:  27.6, Long:  72.0, District: Jaisalmer   , Pixel Val:    9761
Lat:  27.4, Long:  72.0, District: Jodhpur     , Pixel Val:   11618
Lat:  27.2, Long:  72.0, District: Jodhpur     , Pixel Val:   22059
Lat:  27.0, Long:  72.0, District: Jaisalmer   , Pixel Val:   11562
Lat:  26.8, Long:  72.0, District: Jaisalmer   , Pixel Val:   11302
Lat:  26.6, Long:  72.0, District: Jaisalmer   , Pixel Val:   11163
Lat:  26.4, Long:  72.0, District: Jaisalmer   , Pixel Val:       0
Lat:  28.5, Long:  72.3, District: Bikaner     , Pixel Val:       0
Lat:  28.2, Long:  72.3, District: Bikaner     , Pixel Val:       0
Lat:  28.0, Long:  72.3, District: Bikaner     , Pixel Val:   10845
Lat:  27.8, Long:  72.3, District: Bikaner     , Pixel Val:   10621
Lat:  27.6, Long:  72.3, District: Jaisalmer   , Pixel Val:   11299
Lat:  27.4, Long:  72.3, District: Jodhpur     , Pixel Val:   11236
Lat:  27.2, Long:  72.3, District: Jodhpur     , Pixel Val:   11267
Lat:  27.0, Long:  72.3, District: Jodhpur     , Pixel Val:       0
Lat:  26.8, Long:  72.2, District: Jodhpur     , Pixel Val:       0
Lat:  26.6, Long:  72.2, District: Jodhpur     , Pixel Val:       0
Lat:  26.4, Long:  72.2, District: Jodhpur     , Pixel Val:       0
Lat:  28.4, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  28.2, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  28.0, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  27.8, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  27.6, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  27.4, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  27.2, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  27.0, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  26.8, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  26.6, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  26.4, Long:  72.5, District: Jodhpur     , Pixel Val:       0

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# We have another player in the ground!

Can Reverse Geocode by using the python libraries `shapely` and `fiona` with a shapefile for all the district boundaries of India

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In [11]:

import fiona
from shapely.geometry import Point, shape

# Change this for Win7
base = "/Users/macbook/Documents/BTP/Satellite/Data/Maps/Districts/Census_2011"
fc = fiona.open(base+"/2011_Dist.shp")

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In [12]:

def reverse_geocode(pt):
for feature in fc:
if shape(feature['geometry']).contains(pt):
return feature['properties']['DISTRICT']
return "NRI"

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In [14]:

k = 10  # If we want k*k pixels in total from the image

for i in range(0,col,col/k):
for j in range(0,row,row/k):

############### fetching the lat and lon coordinates #######################################
x,y = pixel2coord(i,j)
lonx, latx, z = transform.TransformPoint(x,y)

############### fetching the name of district ##############################################
point = Point(lonx,latx)
district = reverse_geocode(point)
if district=="NRI":
print ("Outside Indian Territory")
continue

############### The pixel value for that location ##########################################
px,py = i,j
pix = pix[0][0]

# printing
s = "Lat: {0:5.1f}, Long: {1:5.1f}, District: {2:12}, Pixel Val: {3:7}".format(latx,lonx,district,pix)
print (s)

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Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.9, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.7, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.4, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.2, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  27.0, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  26.8, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  26.6, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.2, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.9, Long:  70.4, District: Jaisalmer   , Pixel Val:       0
Lat:  27.6, Long:  70.4, District: Jaisalmer   , Pixel Val:   11591
Lat:  27.4, Long:  70.4, District: Jaisalmer   , Pixel Val:   11860
Lat:  27.2, Long:  70.4, District: Jaisalmer   , Pixel Val:   12666
Lat:  27.0, Long:  70.4, District: Jaisalmer   , Pixel Val:   12044
Lat:  26.8, Long:  70.4, District: Jaisalmer   , Pixel Val:   12579
Lat:  26.6, Long:  70.4, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.4, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.9, Long:  70.7, District: Jaisalmer   , Pixel Val:   11610
Lat:  27.6, Long:  70.7, District: Jaisalmer   , Pixel Val:   11842
Lat:  27.4, Long:  70.7, District: Jaisalmer   , Pixel Val:   11577
Lat:  27.2, Long:  70.7, District: Jaisalmer   , Pixel Val:   12740
Lat:  27.0, Long:  70.7, District: Jaisalmer   , Pixel Val:   12015
Lat:  26.8, Long:  70.7, District: Jaisalmer   , Pixel Val:   11851
Lat:  26.6, Long:  70.7, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.6, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.6, Long:  70.9, District: Jaisalmer   , Pixel Val:   11602
Lat:  27.4, Long:  70.9, District: Jaisalmer   , Pixel Val:   12231
Lat:  27.2, Long:  70.9, District: Jaisalmer   , Pixel Val:   11390
Lat:  27.0, Long:  70.9, District: Jaisalmer   , Pixel Val:   11898
Lat:  26.8, Long:  70.9, District: Jaisalmer   , Pixel Val:   11736
Lat:  26.6, Long:  70.9, District: Jaisalmer   , Pixel Val:       0
Lat:  26.4, Long:  70.9, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.6, Long:  71.1, District: Jaisalmer   , Pixel Val:   12236
Lat:  27.4, Long:  71.1, District: Jaisalmer   , Pixel Val:   11572
Lat:  27.2, Long:  71.1, District: Jaisalmer   , Pixel Val:   12053
Lat:  27.0, Long:  71.1, District: Jaisalmer   , Pixel Val:   11647
Lat:  26.8, Long:  71.1, District: Jaisalmer   , Pixel Val:   11882
Lat:  26.6, Long:  71.1, District: Jaisalmer   , Pixel Val:   11974
Lat:  26.4, Long:  71.1, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.8, Long:  71.4, District: Jaisalmer   , Pixel Val:   11996
Lat:  27.6, Long:  71.4, District: Jaisalmer   , Pixel Val:   11815
Lat:  27.4, Long:  71.4, District: Jaisalmer   , Pixel Val:   11647
Lat:  27.2, Long:  71.3, District: Jaisalmer   , Pixel Val:   12070
Lat:  27.0, Long:  71.3, District: Jaisalmer   , Pixel Val:   11955
Lat:  26.8, Long:  71.3, District: Jaisalmer   , Pixel Val:   11199
Lat:  26.6, Long:  71.3, District: Jaisalmer   , Pixel Val:   11723
Lat:  26.4, Long:  71.3, District: Barmer      , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.8, Long:  71.6, District: Jaisalmer   , Pixel Val:   11977
Lat:  27.6, Long:  71.6, District: Jaisalmer   , Pixel Val:   11268
Lat:  27.4, Long:  71.6, District: Jaisalmer   , Pixel Val:   10623
Lat:  27.2, Long:  71.6, District: Jaisalmer   , Pixel Val:   11803
Lat:  27.0, Long:  71.6, District: Jaisalmer   , Pixel Val:   11594
Lat:  26.8, Long:  71.6, District: Jaisalmer   , Pixel Val:   11549
Lat:  26.6, Long:  71.6, District: Jaisalmer   , Pixel Val:   11265
Lat:  26.4, Long:  71.6, District: Barmer      , Pixel Val:       0
Outside Indian Territory
Outside Indian Territory
Outside Indian Territory
Lat:  27.8, Long:  71.8, District: Jaisalmer   , Pixel Val:   11508
Lat:  27.6, Long:  71.8, District: Jaisalmer   , Pixel Val:   11511
Lat:  27.4, Long:  71.8, District: Jaisalmer   , Pixel Val:   11616
Lat:  27.2, Long:  71.8, District: Jaisalmer   , Pixel Val:   11215
Lat:  27.0, Long:  71.8, District: Jaisalmer   , Pixel Val:   11814
Lat:  26.8, Long:  71.8, District: Jaisalmer   , Pixel Val:   11511
Lat:  26.6, Long:  71.8, District: Jaisalmer   , Pixel Val:   11989
Lat:  26.4, Long:  71.8, District: Jaisalmer   , Pixel Val:       0
Outside Indian Territory
Lat:  28.2, Long:  72.1, District: Bikaner     , Pixel Val:       0
Lat:  28.0, Long:  72.1, District: Bikaner     , Pixel Val:   11208
Lat:  27.8, Long:  72.0, District: Jaisalmer   , Pixel Val:   11468
Lat:  27.6, Long:  72.0, District: Jaisalmer   , Pixel Val:    9761
Lat:  27.4, Long:  72.0, District: Jodhpur     , Pixel Val:   11618
Lat:  27.2, Long:  72.0, District: Jodhpur     , Pixel Val:   22059
Lat:  27.0, Long:  72.0, District: Jaisalmer   , Pixel Val:   11562
Lat:  26.8, Long:  72.0, District: Jaisalmer   , Pixel Val:   11302
Lat:  26.6, Long:  72.0, District: Jaisalmer   , Pixel Val:   11163
Lat:  26.4, Long:  72.0, District: Jaisalmer   , Pixel Val:       0
Lat:  28.5, Long:  72.3, District: Bikaner     , Pixel Val:       0
Lat:  28.2, Long:  72.3, District: Bikaner     , Pixel Val:       0
Lat:  28.0, Long:  72.3, District: Bikaner     , Pixel Val:   10845
Lat:  27.8, Long:  72.3, District: Bikaner     , Pixel Val:   10621
Lat:  27.6, Long:  72.3, District: Jaisalmer   , Pixel Val:   11299
Lat:  27.4, Long:  72.3, District: Jodhpur     , Pixel Val:   11236
Lat:  27.2, Long:  72.3, District: Jodhpur     , Pixel Val:   11267
Lat:  27.0, Long:  72.3, District: Jodhpur     , Pixel Val:       0
Lat:  26.8, Long:  72.2, District: Jodhpur     , Pixel Val:       0
Lat:  26.6, Long:  72.2, District: Jodhpur     , Pixel Val:       0
Lat:  26.4, Long:  72.2, District: Jodhpur     , Pixel Val:       0
Lat:  28.4, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  28.2, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  28.0, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  27.8, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  27.6, Long:  72.5, District: Bikaner     , Pixel Val:       0
Lat:  27.4, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  27.2, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  27.0, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  26.8, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  26.6, Long:  72.5, District: Jodhpur     , Pixel Val:       0
Lat:  26.4, Long:  72.5, District: Jodhpur     , Pixel Val:       0

``````