Deep learning frameworks have been widely used in image classification and segmentation tasks. In this paper we outline the methods used to adapt an image segmentation model, U-Net, to identify buildings in geospatial images. The model has been trained and tested on a set of orthophotographic and LiDAR data from the state of Indiana. Its results are compared to the results achieved by a ResNet101 and RefineNet model trained with the same data, excluding the LiDAR data. This tool has a wide range of potential uses in research involving geospatial imagery. We discuss these use cases and some of the challenges and pitfalls in tuning a model for use with geospatial data.
