Roof measurement via gis software11/6/2023 ![]() ![]() Geographic information system (GIS) data are openly available for a variety of applications. Networks trained from roof data in Witten, Germany and Manhattan (New York City) are evaluated on independent data from these cities and Ann Arbor, Michigan. Model confidence thresholds are adjusted leading to significant increases in models precision. Satellite image and LiDAR data fusion is shown to provide greater classification accuracy than using either data type alone. ![]() Multiple convolutional neural network (CNN) architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite imagery and airborne LiDAR data are processed and manually labeled to create a diverse annotated roof image dataset for small to large urban cities. This paper proposes a method to automatically label building roof shape from publicly available GIS data. However, building roof structure is not fully provided in maps. Aircraft, particularly small unmanned aircraft systems (UAS), can exploit this and additional information such as building roof structure to improve navigation accuracy and safely perform contingency landings particularly in urban regions. Geographic information systems (GIS) provide accurate maps of terrain, roads, waterways, and building footprints and heights. ![]()
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