Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks
This work addresses urban monitoring for remote sensing applications, but it is incremental as it adapts an existing method to a specific dataset.
The paper tackled urban change detection by applying a Siamese CNN to Sentinel-2 temporal images of Mekelle city, achieving metrics such as 95.8% overall accuracy and 77.1 F1 score for detecting buildings and roads.
Change detection is a fast-growing discipline in the areas of computer vision 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.