  The interest for change detection in the field of remote sensing has
increased in the last few years. Searching for changes in satellite images has
many useful applications, ranging from land cover and land use analysis to
anomaly detection. In particular, urban change detection provides an efficient
tool to study urban spread and growth through several years of observation. At
the same time, change detection is often a computationally challenging and
time-consuming task, which requires innovative methods to guarantee optimal
results with unquestionable value and within reasonable time. In this paper we
present two different approaches to change detection (semantic segmentation and
classification) that both exploit convolutional neural networks to achieve good
results, which can be further refined and used in a post-processing workflow
for a large variety of applications.
