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Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review

Author

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  • Lin Luo

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China)

  • Pengpeng Li

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China
    Wuhan Geomatics Institute, Wuhan 430021, China)

  • Xuesong Yan

    (School of Computer Science, China University of Geosciences, Wuhan 430074, China)

Abstract

Building extraction from remote sensing (RS) images is a fundamental task for geospatial applications, aiming to obtain morphology, location, and other information about buildings from RS images, which is significant for geographic monitoring and construction of human activity areas. In recent years, deep learning (DL) technology has made remarkable progress and breakthroughs in the field of RS and also become a central and state-of-the-art method for building extraction. This paper provides an overview over the developed DL-based building extraction methods from RS images. Firstly, we describe the DL technologies of this field as well as the loss function over semantic segmentation. Next, a description of important publicly available datasets and evaluation metrics directly related to the problem follows. Then, the main DL methods are reviewed, highlighting contributions and significance in the field. After that, comparative results on several publicly available datasets are given for the described methods, following up with a discussion. Finally, we point out a set of promising future works and draw our conclusions about building extraction based on DL techniques.

Suggested Citation

  • Lin Luo & Pengpeng Li & Xuesong Yan, 2021. "Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7982-:d:690993
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    References listed on IDEAS

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    1. Xiaoli Li & Zhiqiang Li & Jiansi Yang & Yaohui Liu & Bo Fu & Wenhua Qi & Xiwei Fan, 2018. "Spatiotemporal characteristics of earthquake disaster losses in China from 1993 to 2016," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 843-865, November.
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    Cited by:

    1. Chunhai Tan & Tao Chen & Jiayu Liu & Xin Deng & Hongfei Wang & Junwei Ma, 2024. "Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet," Sustainability, MDPI, vol. 16(22), pages 1-15, November.
    2. Andreas Braun & Gebhard Warth & Felix Bachofer & Michael Schultz & Volker Hochschild, 2023. "Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities," Land, MDPI, vol. 12(10), pages 1-41, October.
    3. Hang Yu & Weidong Song & Bing Zhang & Hongbo Zhu & Jiguang Dai & Jichao Zhang, 2024. "MMS-EF: A Multi-Scale Modular Extraction Framework for Enhancing Deep Learning Models in Remote Sensing," Land, MDPI, vol. 13(11), pages 1-18, November.
    4. Maria Spyridoula Tzima & Athos Agapiou & Vasiliki Lysandrou & Georgios Artopoulos & Paris Fokaides & Charalambos Chrysostomou, 2023. "An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data for Monitoring Historic Clusters in Cypriot Cities," Energies, MDPI, vol. 16(8), pages 1-20, April.

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