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Integrating remote sensing and deep learning for mapping urban housing wealth patterns

Emmanuel Nyandwi Markus Gerke Pedro Achanccaray Christian Lessmann

地球空间信息科学学报（英文版）2025，Vol.28Issue(5)：中插3,2063-2081,20.

地球空间信息科学学报（英文版）2025，Vol.28Issue(5)：中插3,2063-2081,20.DOI:10.1080/10095020.2025.2543496

Integrating remote sensing and deep learning for mapping urban housing wealth patterns

Emmanuel Nyandwi ¹Markus Gerke ¹Pedro Achanccaray ¹Christian Lessmann²

作者信息

1. Institute of Geodesy and Photogrammetry,Technical University of Braunschweig,Braunschweig,Germany
2. International Economics,Faculty of Business and Economics,Technical University of Dresden,Dresden,Germany
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摘要

关键词

Deep learning(DL)/housing wealth/expert annotation/self-training(self-labeling)/multiclass instance segmentation/remote sensing(RS)image

Key words

Deep learning(DL)/housing wealth/expert annotation/self-training(self-labeling)/multiclass instance segmentation/remote sensing(RS)image

引用本文复制引用

Emmanuel Nyandwi,Markus Gerke,Pedro Achanccaray,Christian Lessmann..Integrating remote sensing and deep learning for mapping urban housing wealth patterns[J].地球空间信息科学学报（英文版）,2025,28(5):中插3,2063-2081,20.

基金项目

This work was supported by the German Academic Exchange Service(DAAD)doctoral program funded by the Federal Foreign Office[Grant number 57507871]. （DAAD）

地球空间信息科学学报（英文版）

ISSN：1009-5020

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