Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks

TL;DR

This paper presents a Siamese CNN approach for detecting urban changes from Sentinel-2 images, achieving high accuracy in identifying urbanization features like buildings and roads.

Contribution

The study introduces a Siamese CNN model tailored for change detection in remote sensing images, demonstrating effective urbanization monitoring.

Findings

Overall accuracy of 95.8%

Kappa measure of 72.5

Recall of 76.5%

Abstract

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.