Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks

Abstract

Change detection is a fast growing descipline in the areas of computer vison and remote sensing. In this work, we designed and developed a variant of convolutional neural network (CNN), known as Siamese CNN to extract features from pairs of Sentinel-2 temporal images of Mekelle city captured at different times and detect changes due to urbanization: buildings and roads. The detection capability of the proposed was measured in terms of overall accuracy (95.8%), Kappa measure (72.5%), recall (76.5%), precision (77.7%), F1 measure (77.1%). The model has achieved a good performance in terms of most of these measures and can be used to detect changes in Mekelle and other cities at different time horizons undergoing urbanization.