To identify agricultural fields with ML, the group needed annotated data sets. To do this, the SpatialCollective in Kenya trained a team to analyze satellite imagery for evidence of agricultural fields. Once the data was annotated, it was used to train and test a Random Forest classifier. Areas the model had trouble classifying were sent back to mappers to be labeled and then added back into training data. The team utilized a number of tools to implement the data processing and modeling including: Geotrellis, RasterFrames, rasterio and GeoPySpark. The image above shows some of the results of the project. If you’d like to learn more about this project checkout the teams GitHub repo!
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