This deep learning model is used for extracting windows and doors in textured building data displayed in 3D views. Manually digitizing windows/doors from 3D building data can be a slow process. This model automates the extraction of these objects from a 3D view and can help in speeding up 3D editing and analysis workflows. Using this model, existing building data can be enhanced with additional information on location, size and orientation of windows and doors. The extracted windows and doors can be further used to perform 3D visibility analysis using existing 3D geoprocessing tools in ArcGIS.This model can be useful in many industries and workflows. National Government and state-level law enforcement could use this model in security analysis scenarios. Local governments could use windows and door locations to help with tax assessments with CAMA (computer aided mass appraisal) plus impact-studies for urban planning. Public safety users might be interested in regards to physical or visual access to restricted areas, or the ability to build evacuation plans. The commercial sector, with everyone from real-estate agents to advertisers to office/interior designers, would be able to benefit from knowing where windows and doors are located. Even utilities, especially mobile phone providers, could take advantage of knowing window sizes and positions. To be clear, this model doesn't solve these problems, but it does allow users to extract and collate some of the data they will need to do it.Using the modelThis model is generic and is expected to work well with a variety of building styles and shapes. To use this model, you need to install supported deep learning frameworks packages first. See Install deep learning frameworks for ArcGIS for more information. The model can be used with the Interactive Object Detection tool.A blog on the ArcGIS Pro tool that leverages this model is published on Esri Blogs. We've also published steps on how to retrain this model further using your data.InputThe model is expected to work with any textured building data displayed in 3D views. Example data sources include textured multipatches, 3D object scene layers, and integrated mesh layers. OutputFeature class with polygons representing the detected windows and doors in the input imagery. Model architectureThe model uses the FasterRCNN model architecture implemented using ArcGIS API for Python.Training dataThis model was trained using images from the Open Images Dataset.Sample resultsBelow, are sample results of the windows detected with this model in ArcGIS Pro using the Interactive Object Detection tool, which outputs the detected objects as a symbolized point feature class with size and orientation attributes.