Difference between revisions of "Digitizing in Python"
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==Setting up Colab and Google Earth Engine== |
==Setting up Colab and Google Earth Engine== |
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Set up a Google Earth Engine account for free with these similar instructions (note that you do not need to make an account under a student or any paid service, this is free!): https://courses.spatialthoughts.com/gee-sign-up.html |
Set up a Google Earth Engine account for free with these similar instructions (note that you do not need to make an account under a student requirement or the sort or any paid service, this is free!): https://courses.spatialthoughts.com/gee-sign-up.html |
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==What is Digitizing?== |
==What is Digitizing?== |
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Revision as of 07:49, 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.
Software Used
Google Earth Engine Google Colab or any other Python language system or notebooks.
Python libraries:
earthengine-api, geemap, rasterio, geopandas, shapely, matplotlib
Setting up Colab and Google Earth Engine
Set up a Google Earth Engine account for free with these similar instructions (note that you do not need to make an account under a student requirement or the sort or any paid service, this is free!): https://courses.spatialthoughts.com/gee-sign-up.html
