Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images
Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/10/2562 |
| _version_ | 1797598492114288640 |
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| author | Zhenfeng Shao Tao Cheng Huyan Fu Deren Li Xiao Huang |
| author_facet | Zhenfeng Shao Tao Cheng Huyan Fu Deren Li Xiao Huang |
| author_sort | Zhenfeng Shao |
| collection | DOAJ |
| description | Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces. |
| first_indexed | 2024-03-11T03:21:51Z |
| format | Article |
| id | doaj.art-e0f3999603bb478da0f10a743ac8eab8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:21:51Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e0f3999603bb478da0f10a743ac8eab82023-11-18T03:06:54ZengMDPI AGRemote Sensing2072-42922023-05-011510256210.3390/rs15102562Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing ImagesZhenfeng Shao0Tao Cheng1Huyan Fu2Deren Li3Xiao Huang4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650500, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaDepartment of Geosciences, University of Arkansas, Fayetteville, AR 72701, USAUrban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.https://www.mdpi.com/2072-4292/15/10/2562impervious surface estimationurban mapping issuesremote sensing |
| spellingShingle | Zhenfeng Shao Tao Cheng Huyan Fu Deren Li Xiao Huang Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images Remote Sensing impervious surface estimation urban mapping issues remote sensing |
| title | Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images |
| title_full | Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images |
| title_fullStr | Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images |
| title_full_unstemmed | Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images |
| title_short | Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images |
| title_sort | emerging issues in mapping urban impervious surfaces using high resolution remote sensing images |
| topic | impervious surface estimation urban mapping issues remote sensing |
| url | https://www.mdpi.com/2072-4292/15/10/2562 |
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