Towards High-Resolution Population Mapping: Leveraging Open Data, Remote Sensing, and AI for Geospatial Analysis in Developing Country Cities—A Case Study of Bangkok DOI Creative Commons
Kittisak Maneepong,

Ryota Yamanotera,

Yuki Akiyama

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1204 - 1204

Published: March 28, 2025

This study develops a globally adaptable and scalable methodology for high-resolution, building-level population mapping, integrating Earth observation techniques, geospatial data acquisition, machine learning to enhance estimation in rapidly urbanizing cities, particularly developing countries. Using Bangkok, Thailand, as case study, this research presents problem-driven approach that leverages open data, including Overture Maps OpenStreetMap (OSM), alongside Digital Elevation Models, overcome limitations availability, granularity, quality. integrates morphological terrain analysis learning-based classification models estimate building ancillary attributes such footprint, height, usage, applying micro-dasymetric mapping techniques refine distribution estimates. The findings reveal notable degree of accuracy within residential zones, whereas performance commercial cultural areas indicates room improvement. Challenges identified mixed-use townhouse types are attributed issues misclassification constraints input data. underscores the importance AI remote sensing resolving urban scarcity challenges. By addressing critical gaps acquisition processing, provides scalable, cost-effective solutions integration multi-source contribute sustainable development, disaster resilience, resource planning. reinforce transformative role open-access applications, supporting real-time decision-making enhanced resilience strategies evolving environments.

Language: Английский

Towards High-Resolution Population Mapping: Leveraging Open Data, Remote Sensing, and AI for Geospatial Analysis in Developing Country Cities—A Case Study of Bangkok DOI Creative Commons
Kittisak Maneepong,

Ryota Yamanotera,

Yuki Akiyama

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1204 - 1204

Published: March 28, 2025

This study develops a globally adaptable and scalable methodology for high-resolution, building-level population mapping, integrating Earth observation techniques, geospatial data acquisition, machine learning to enhance estimation in rapidly urbanizing cities, particularly developing countries. Using Bangkok, Thailand, as case study, this research presents problem-driven approach that leverages open data, including Overture Maps OpenStreetMap (OSM), alongside Digital Elevation Models, overcome limitations availability, granularity, quality. integrates morphological terrain analysis learning-based classification models estimate building ancillary attributes such footprint, height, usage, applying micro-dasymetric mapping techniques refine distribution estimates. The findings reveal notable degree of accuracy within residential zones, whereas performance commercial cultural areas indicates room improvement. Challenges identified mixed-use townhouse types are attributed issues misclassification constraints input data. underscores the importance AI remote sensing resolving urban scarcity challenges. By addressing critical gaps acquisition processing, provides scalable, cost-effective solutions integration multi-source contribute sustainable development, disaster resilience, resource planning. reinforce transformative role open-access applications, supporting real-time decision-making enhanced resilience strategies evolving environments.

Language: Английский

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