Pixel-Level Land Cover Classification Using the Geo AI Data Science Virtual Machine and Batch AI
Last week Microsoft launched the Geo AI Data Science Virtual Machine (DSVM), an Azure VM type specially tailored to data scientists and analysts that manage geospatial data. To support the Geo AI DSVM launch, we are sharing sample code and methods for our joint land cover mapping project with the Chesapeake Conservancy and ESRI. We have used Microsoft’s Cognitive Toolkit (CNTK) to train a deep neural network-based semantic segmentation model that assigns land cover labels from aerial imagery. By reducing cost and speeding up land cover map construction, such models will enable finer-resolution timecourses to track processes like deforestation and urbanization. This blog post describes the motivation behind our work and the approach we’ve taken to land cover mapping. If you prefer to get started right away, please head straight to our GitHub repository to find our instructions and materials.
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