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

Autor:	Kittisak Maneepong, Ryota Yamanotera, Yuki Akiyama, Hiroyuki Miyazaki, Satoshi Miyazawa, Chiaki Mizutani Akiyama
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	urban population mapping building height estimation building use classification machine learning Science
Zdroj:	Remote Sensing, Vol 16, Iss 21, p 3922 (2024)
Druh dokumentu:	article
ISSN:	2072-4292
DOI:	10.3390/rs16213922
Popis:	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.
Databáze:	Directory of Open Access Journals
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