  The Copernicus Sentinel-2 program now provides multispectral images at a
global scale with a high revisit rate. In this paper we explore the usage of
convolutional neural networks for urban change detection using such
multispectral images. We first present the new change detection dataset that
was used for training the proposed networks, which will be openly available to
serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is
composed of pairs of multispectral aerial images, and the changes were manually
annotated at pixel level. We then propose two architectures to detect changes,
Siamese and Early Fusion, and compare the impact of using different numbers of
spectral channels as inputs. These architectures are trained from scratch using
the provided dataset.
