Machine learning for aerial image labeling

Information extracted from aerial photographs has found applications in a wide range of areas including urban planning, crop and forest management, disaster relief, and climate modeling. At present, much of the extraction is still performed by human experts, making the process slow, costly, and error prone. The goal of this thesis is to develop methods for automatically extracting the locations of objects such as roads, buildings, and trees directly from aerial images. We investigate the use of machine learning methods trained on aligned aerial images and possibly outdated maps for labeling the pixels of an aerial image with semantic labels. We show how deep neural networks implemented on modern GPUs can be used to efficiently learn highly discriminative image features. We then introduce new loss functions for training neural networks that are partially robust to incomplete and poorly registered target maps. Finally, we propose two ways of improving the predictions of our system by introducing structure into the outputs of the neural networks. We evaluate our system on the largest and most-challenging road and building detection datasets considered in the literature and show that it works reliably under a wide variety of conditions. Furthermore, we are releasing the first large-scale road and building detection datasets to the public in order to facilitate future comparisons with other methods.