2020 land cover data for all 2010 census urban areas in the State of Wisconsin, as well as adjacent lands. Pixels have a 1-m2 resolution and use the below values: TreeTurf/grassImperviousWaterIndeterminableThe classification was derived from imagery collected by the U.S. Department of Agriculture’s National Agriculture Imagery Program in 2020 using a supervised machine learning classifier. The land cover data was produced by ensembling two models that classified each pixel based on its spectral signature. The full landcover model was designed to capture all landcover classes whereas the second model was specifically tuned to identify only trees. The second model was created to improve the full landcover model accuracy because it tended to overpredict trees. The ensemble model classified ‘indeterminable’ pixels where the tree model predicted trees not included in the tree-specific model. A statewide training and test dataset composed of tree, turf, building, road, and water data was utilized to build the model. Quality analysis of the results showed that there were no systematic biases based on geography or community size.
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