Pratyush Tripathy PratyushTripathy

execute:

$ jupyter notebook --no-browser --port=8889 --NotebookApp.allow_origin="*" --ip=serverip --NotebookApp.token=''

	import pandas as pd

	# define the link to the CSV file on Microsoft's GitHub repo
	csv_path = r'https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/dataset-links.csv'

	# read the file
	df = pd.read_csv(csv_path)

	# check basic details
	print('Columns in CSV:', df.columns)

	// define a function to pull Landsat data for a given footprint
	var band_names = ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'Thermal', 'SWIR2'];
	function get_landsat_for_aoi(aoi){
	return ee.ImageCollection("LANDSAT/LT05/C02/T1_L2").filterBounds(aoi)
	// fetching scenes for Jan-May of 2010 and 2011
	.filter(ee.Filter.calendarRange(2010, 2011,'year')).filter(ee.Filter.calendarRange(1,5,'month'))
	.select(['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'ST_B6', 'SR_B7'],
	band_names)
	.filter(ee.Filter.lte('CLOUD_COVER', 30))
	.reduce(ee.Reducer.percentile([25]))

	// extract the unique row numbers from the landsat footprint collection
	var unique_paths = landsat_footprints.aggregate_array('path').distinct();
	print('Unique paths:', unique_paths)

	// seggregate the landsat footprints by path
	var path_wise_footprints = unique_paths.map(function a(element){
	return landsat_footprints.filter(ee.Filter.eq('path', element));
	});
	print('Path wise footprints', path_wise_footprints)

	// pull landsat footprint and filter for your area of interest
	var landsat_footprints = ee.FeatureCollection("users/pratyusht/public_assets/landsat_footprints")
	.filter(ee.Filter.and(
	ee.Filter.lte('path', 148), ee.Filter.gte('path', 140),
	ee.Filter.lte('row', 46), ee.Filter.gte('row', 42)
	))

	Map.addLayer(landsat_footprints, {}, 'landsat_footprints')
	Map.centerObject(landsat_footprints, 6)

	// call the function to match the histogram of slave
	var hist = require('users/pratyusht/PublicFunctions:seamless_mosaic/histogram');

	var matched_slave = hist.match_histogram(master, slave, overlap, band_names, 30);

	Map.addLayer(matched_slave, l8_vis_params, 'Matched Slave');

	// perform negative buffer of 80 km to the polygons
	landsat_footprints = landsat_footprints.map(function (x){return x.buffer(-8e4, 5)})

	Map.addLayer(landsat_footprints, {}, 'landsat_footprints')

	// Get footprint of the landsat scenes
	var master_fp = master.geometry();
	var slave_fp = slave.geometry();

	// Perform intersection to extarct overlap area
	var overlap = master_fp.intersection(slave_fp);

	// Add all the three layers to the map
	Map.addLayer(master_fp, {}, 'Master Footprint');
	Map.addLayer(slave_fp, {}, 'Slave Footprint');

	// query landsat 8 collection for given AOI
	var band_names = ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'Thermal', 'SWIR2'];
	var landsat8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
	.filterBounds(geometry)
	.filterDate('2020-03-15', '2020-03-25')
	.filter(ee.Filter.lte('CLOUD_COVER', 5))
	.select(
	['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5', 'SR_B6', 'ST_B10', 'SR_B7'],
	band_names
	);

	# define the output file names
	mx_outfile = mx_file.replace('.tif', '_byte_deflate.tif')
	sb_outfile = sb_file.replace('.tif', '_byte_deflate.tif')

	# export the files
	raster.export(arr_mx, ds_mx, filename=mx_outfile, dtype='uint8', compress='DEFLATE')
	raster.export(arr_sb, ds_sb, filename=sb_outfile, dtype='uint8', compress='DEFLATE')