A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping
文献类型：期刊论文
| 作者 | Lu, Wei1,2; Hu, Yunfeng1,2; Peng, Feifei3,4; Feng, Zhiming1,2; Yang, Yanzhao1,2; Kwan, Chiman |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 16期号:2页码:19 |
| 关键词 | slum mapping deep learning geoscience-aware paradigm semantic segmentation remote sensing |
| DOI | 10.3390/rs16020260 |
| 通讯作者 | Hu, Yunfeng(huyf@lreis.ac.cn) |
| 英文摘要 | Approximately 1 billion people worldwide currently inhabit slum areas. The UN Sustainable Development Goal (SDG 11.1) underscores the imperative of upgrading all slums by 2030 to ensure adequate housing for everyone. Geo-locations of slums help local governments with upgrading slums and alleviating urban poverty. Remote sensing (RS) technology, with its excellent Earth observation capabilities, can play an important role in slum mapping. Deep learning (DL)-based RS information extraction methods have attracted a lot of attention. Currently, DL-based slum mapping studies typically uses three optical bands to adapt to existing models, neglecting essential geo-scientific information, such as spectral and textural characteristics, which are beneficial for slum mapping. Inspired by the geoscience-aware DL paradigm, we propose the Geoscience-Aware Network for slum mapping (GASlumNet), aiming to improve slum mapping accuracies via incorporating the DL model with geoscientific prior knowledge. GASlumNet employs a two-stream architecture, combining ConvNeXt and UNet. One stream concentrates on optical feature representation, while the other emphasizes geo-scientific features. Further, the feature-level and decision-level fusion mechanisms are applied to optimize deep features and enhance model performance. We used Jilin-1 Spectrum 01 and Sentinel-2 images to perform experiments in Mumbai, India. The results demonstrate that GASlumNet achieves higher slum mapping accuracy than the comparison models, with an intersection over union (IoU) of 58.41%. Specifically, GASlumNet improves the IoU by 4.60 similar to 5.97% over the baseline models, i.e., UNet and ConvNeXt-UNet, which exclusively utilize optical bands. Furthermore, GASlumNet enhances the IoU by 10.97% compared to FuseNet, a model that combines optical bands and geo-scientific features. Our method presents a new technical solution to achieve accurate slum mapping, offering potential benefits for regional and global slum mapping and upgrading initiatives. |
| WOS关键词 | URBAN ; EXTRACTION ; AREAS |
| 资助项目 | Network Security and Information Program of the Chinese Academy of Sciences |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001152823300001 |
| 资助机构 | Network Security and Information Program of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202526]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Hu, Yunfeng |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China 4.Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Wei,Hu, Yunfeng,Peng, Feifei,et al. A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping[J]. REMOTE SENSING,2024,16(2):19. |
| APA | Lu, Wei,Hu, Yunfeng,Peng, Feifei,Feng, Zhiming,Yang, Yanzhao,&Kwan, Chiman.(2024).A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping.REMOTE SENSING,16(2),19. |
| MLA | Lu, Wei,et al."A Geoscience-Aware Network (GASlumNet) Combining UNet and ConvNeXt for Slum Mapping".REMOTE SENSING 16.2(2024):19. |
入库方式： OAI收割
来源：地理科学与资源研究所
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