Buildings Classification using Very High Resolution Satellite Imagery

• URL: http://arxiv.org/abs/2111.14650v1
• Date: Mon, 29 Nov 2021 16:07:04 GMT
• Title: Buildings Classification using Very High Resolution Satellite Imagery
• Authors: Mohammad Dimassi, Abed Ellatif Samhat, Mohammad Zaraket, Jamal Haidar, Mustafa Shukor, Ali J. Ghandour
• Abstract summary: We focus on buildings damage assessment (BDA) and buildings type classification (BTC) of residential and non-residential buildings. We propose a 2-stage deep learning-based approach, where first, buildings' footprints are extracted using a semantic segmentation model. We validate the proposed approach on two applications showing excellent accuracy and F1-score metrics.
• Score: 0.769672852567215
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Buildings classification using satellite images is becoming more important for several applications such as damage assessment, resource allocation, and population estimation. We focus, in this work, on buildings damage assessment (BDA) and buildings type classification (BTC) of residential and non-residential buildings. We propose to rely solely on RGB satellite images and follow a 2-stage deep learning-based approach, where first, buildings' footprints are extracted using a semantic segmentation model, followed by classification of the cropped images. Due to the lack of an appropriate dataset for the residential/non-residential building classification, we introduce a new dataset of high-resolution satellite images. We conduct extensive experiments to select the best hyper-parameters, model architecture, and training paradigm, and we propose a new transfer learning-based approach that outperforms classical methods. Finally, we validate the proposed approach on two applications showing excellent accuracy and F1-score metrics.

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