Detecting Urban Dynamics Using Deep Siamese Convolutional Neural Networks

2020/08/11 by Ephrem Admasu Yekun, Yekun, Ephrem Admasu, Petros Reda Samsom +1
Earth and Planetary Sciences · Engineering · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Remote Sensing and Land Use #Remote-Sensing Image Classification #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2008.04829

openalex publication_date 2020/08/11 · openalex created_date 2020/08/18 · openalex updated_date 2026/07/28

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.

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