USING 3D RESIDUAL NETWORK FOR SPATIO-TEMPORAL ANALYSIS OF REMOTE SENSING DATA

In this paper, we propose an approach to recognize spatio-temporal changes from remote sensing data. Instead of performing independent analysis on each instance of satellite imagery, we proposed a 3D Convolutional Neural Network (CNN) based on the ResNet architecture. Our approach takes as input a 3D spatio-temporal block comprising of spatial as well as temporal data from multiple years. We predict four key transition classes namely construction, destruction, cultivation and decultivation. In our proposed architecture, we introduced Leaky ReLU instead of ReLU which improves the overall performance as it solves the dying ReLU problem. We also provided dataset and annotations1 for these four classes and have evaluated the efficacy of our approach on data from three different cities.