Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting
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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/21/3922 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850062437392318464 |
|---|---|
| author | Kittisak Maneepong Ryota Yamanotera Yuki Akiyama Hiroyuki Miyazaki Satoshi Miyazawa Chiaki Mizutani Akiyama |
| author_facet | Kittisak Maneepong Ryota Yamanotera Yuki Akiyama Hiroyuki Miyazaki Satoshi Miyazawa Chiaki Mizutani Akiyama |
| author_sort | Kittisak Maneepong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-448dd0415be04be894eea68b62afa6fe |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-448dd0415be04be894eea68b62afa6fe2025-08-20T02:49:55ZengMDPI AGRemote Sensing2072-42922024-10-011621392210.3390/rs16213922Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited SettingKittisak Maneepong0Ryota Yamanotera1Yuki Akiyama2Hiroyuki Miyazaki3Satoshi Miyazawa4Chiaki Mizutani Akiyama5Graduate School of Integrative Science and Engineering, Tokyo City University, Tokyo 158-0087, JapanGraduate School of Integrative Science and Engineering, Tokyo City University, Tokyo 158-0087, JapanFaculty of Architecture and Urban Design, Tokyo City University, Tokyo 158-0087, JapanGLODAL, Inc., Yokohama 231-0062, JapanLocationMind Inc., Tokyo 101-0048, JapanReitaku University, Chiba 277-0065, JapanUrban 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.https://www.mdpi.com/2072-4292/16/21/3922urban population mappingbuilding height estimationbuilding use classificationmachine learning |
| spellingShingle | Kittisak Maneepong Ryota Yamanotera Yuki Akiyama Hiroyuki Miyazaki Satoshi Miyazawa Chiaki Mizutani Akiyama Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting Remote Sensing urban population mapping building height estimation building use classification machine learning |
| title | Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting |
| title_full | Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting |
| title_fullStr | Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting |
| title_full_unstemmed | Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting |
| title_short | Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting |
| title_sort | open data driven 3d building models for micro population mapping in a data limited setting |
| topic | urban population mapping building height estimation building use classification machine learning |
| url | https://www.mdpi.com/2072-4292/16/21/3922 |
| work_keys_str_mv | AT kittisakmaneepong opendatadriven3dbuildingmodelsformicropopulationmappinginadatalimitedsetting AT ryotayamanotera opendatadriven3dbuildingmodelsformicropopulationmappinginadatalimitedsetting AT yukiakiyama opendatadriven3dbuildingmodelsformicropopulationmappinginadatalimitedsetting AT hiroyukimiyazaki opendatadriven3dbuildingmodelsformicropopulationmappinginadatalimitedsetting AT satoshimiyazawa opendatadriven3dbuildingmodelsformicropopulationmappinginadatalimitedsetting AT chiakimizutaniakiyama opendatadriven3dbuildingmodelsformicropopulationmappinginadatalimitedsetting |