Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting

被引:0
|
作者
Maneepong, Kittisak [1 ]
Yamanotera, Ryota [1 ]
Akiyama, Yuki [2 ]
Miyazaki, Hiroyuki [3 ]
Miyazawa, Satoshi [4 ]
Akiyama, Chiaki Mizutani [5 ]
机构
[1] Tokyo City Univ, Grad Sch Integrat Sci & Engn, Tokyo 1580087, Japan
[2] Tokyo City Univ, Fac Architecture & Urban Design, Tokyo 1580087, Japan
[3] GLODAL Inc, Yokohama 2310062, Japan
[4] LocationMind Inc, Tokyo 1010048, Japan
[5] Reitaku Univ, Chiba, Japan
关键词
urban population mapping; building height estimation; building use classification; machine learning; DENSITY;
D O I
10.3390/rs16213922
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Urban planning and management increasingly depend on accurate building and population data. However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. This research contributes to bridging the gap between data needs and availability in resource-constrained urban environments, offering a scalable solution for global application in urban planning and population mapping.
引用
收藏
页数:25
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