Deep Learning Models to Count Buildings in High-Resolution Overhead Images

This paper addresses the problem of counting buildings in very high-resolution overhead true color imagery. We study and discuss the relevance of deep-learning based methods to this task. Two architectures and two loss functions are proposed and compared. We show that a model enforcing equivariance to rotations is beneficial for the task of counting in remotely sensed images. We also highlight the importance of robustness to outliers of the loss function when considering remote sensing applications.