ABSTRACT
Recently, deep learning has been widely applied in pattern recognition with satellite images. Deep learning techniques like Convolutional Neural Network and Deep Belief Network have shown outstanding performance in detecting ground objects like buildings and roads, and the learned deep features are further applied in some prediction tasks like poverty and population mapping. On the other hand, such deep learning techniques usually rely on a large set of labeled training samples (i.e., human knowledge) for supervision. Volunteered Geographic Information (VGI) like the OpenStreetMap provides a way to easily get a large set of such training data. Meanwhile, utilizing VGI for deep learning brings new technical challenges like (1) how to deal with the noise in VGI data which are usually contributed by common people instead of experts, and (2) how to transfer learned models from area to area and from time to time, as there is usually a gap between the volunteer labeled targets and the unknown targets waiting for prediction. This chapter introduces the current work in this field, including satellite image classification with deep learning, challenges and solutions in utilizing VGI data, domain adaptation and feature transferring, and applications.
