Finding Fields with Random Forests #MachineLearning #Agriculture #DataScience #RandomForest #ArtificialIntelligence @azavea @ldemaz @SpatialCollect

Pixel based predictions of growing fields in Southern Ghana from “Mapping Africa: Crowdsourced Machine Learning”.


Access to data isn’t always the bottleneck for researchers, sometimes, it’s the analysis. That was the case when Lyndon Estes (@ldemaz) at the Agricultural Impacts Research Group teamed up with Azavea to identify smallholder agricultural fields in Ghana. Going through this data by hand would be an enormous task. Machine learning (ML) can be used to reduce the burden of analysis.

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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