Difference between revisions of "Digitizing in Python"
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==Purpose== |
==Purpose== |
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The purpose of this tutorial is to automate the digitization of land cover features such as vegetation, waterbodies, and roads, using Sentinel-2 satellite imagery and Python-based geospatial tools. |
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==Objective== |
==Objective== |
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Use Google Earth Engine (GEE) to acquire the satellite images for processing in python. |
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Compute NDVI (vegetation), NDWI (water), NDBI (roads) indices. |
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Export rasters to your Google Drive. |
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Convert rasters to vectors. |
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Visualize and colourize digitized features. |
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==Software== |
==Software== |
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Revision as of 07:32, 17 December 2025
Contents
Purpose
The purpose of this tutorial is to automate the digitization of land cover features such as vegetation, waterbodies, and roads, using Sentinel-2 satellite imagery and Python-based geospatial tools.
Objective
Use Google Earth Engine (GEE) to acquire the satellite images for processing in python. Compute NDVI (vegetation), NDWI (water), NDBI (roads) indices. Export rasters to your Google Drive. Convert rasters to vectors. Visualize and colourize digitized features.
