  Change detection is one of the main problems in remote sensing, and is
essential to the accurate processing and understanding of the large scale Earth
observation data available through programs such as Sentinel and Landsat. Most
of the recently proposed change detection methods bring deep learning to this
context, but openly available change detection datasets are still very scarce,
which limits the methods that can be proposed and tested. In this paper we
present the first large scale high resolution semantic change detection (HRSCD)
dataset, which enables the usage of deep learning methods for semantic change
detection. The dataset contains coregistered RGB image pairs, pixel-wise change
information and land cover information. We then propose several methods using
fully convolutional neural networks to perform semantic change detection. Most
notably, we present a network architecture that performs change detection and
land cover mapping simultaneously, while using the predicted land cover
information to help to predict changes. We also describe a sequential training
scheme that allows this network to be trained without setting a hyperparameter
that balances different loss functions and achieves the best overall results.
