Building Detection with Deep Learning

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.