2020/08/11 by Ephrem Admasu Yekun, Yekun, Ephrem Admasu, Petros Reda Samsom +1
Computer Science · Earth and Planetary Sciences · Engineering · #Artificial intelligence #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Convolutional neural network #Data mining #Deep learning #FOS: Computer and information sciences #FOS: Electrical engineering #Geography #Image and Video Processing (eess.IV) #Measure (data warehouse) #Pattern recognition (psychology) #Precision and recall #Remote Sensing and Land Use #Remote sensing #Remote-Sensing Image Classification #Urbanization #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.04829
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/08/11 · openalex publication_date 2020/08/11 · arxiv updated 2020/08/12 · openalex created_date 2020/08/18 · openalex updated_date 2026/07/28
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.