Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development. They also have use in insurance, taxation, change detection, infrastructure planning, and a variety of other applications.

Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models are highly capable of learning these complex semantics and can produce superior results. Use this deep learning model to automate the tedious manual process of extracting building footprints, reducing time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS. Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (10–40 cm) imagery.OutputFeature class containing building footprints.Applicable geographiesThe model is expected to work well in the United States.Model architectureThe model uses the MaskRCNN model architecture implemented using ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 0.718.Sample resultsHere are a few results from the model. To view more, see this story.