Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints
Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/2/263 |
| _version_ | 1797342680727945216 |
|---|---|
| author | Panli Cai Jingxian Guo Runkui Li Zhen Xiao Haiyu Fu Tongze Guo Xiaoping Zhang Yashuai Li Xianfeng Song |
| author_facet | Panli Cai Jingxian Guo Runkui Li Zhen Xiao Haiyu Fu Tongze Guo Xiaoping Zhang Yashuai Li Xianfeng Song |
| author_sort | Panli Cai |
| collection | DOAJ |
| description | Accurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has shown promise, but it often faces difficulties in distinguishing building photons from other ground objects. To address this challenge, we propose a novel method that incorporates building footprints, relative positions of building and ground photons, and a self-adaptive buffer for building photon selection. We employ the Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) photon-counting LiDAR, specifically the ICESat-2/ATL03 data, along with building footprints obtained from the New York City (NYC) Open Data platform. The proposed approach was applied to estimate the heights of 17,399 buildings in NYC, and the results showed strong consistency with the reference building heights. The root mean square error (RMSE) was 8.1 m, and for 71% of the buildings, the mean absolute error (MAE) was less than 3 m. Furthermore, we conducted an extensive evaluation of the proposed approach and thoroughly investigated the influence of terrain, region, building height, building density, and parameter selection. We also verified the effectiveness of our approach in an experimental area in Beijing and compared it with other existing methods. By leveraging ICESat-2 LiDAR data, building footprints, and advanced selection techniques, the proposed approach demonstrates the potential to accurately estimate building heights over broad areas. |
| first_indexed | 2024-03-08T10:36:46Z |
| format | Article |
| id | doaj.art-c9b7dd15b2014c4fa935e771430c80b6 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T10:36:46Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c9b7dd15b2014c4fa935e771430c80b62024-01-26T18:17:06ZengMDPI AGRemote Sensing2072-42922024-01-0116226310.3390/rs16020263Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building FootprintsPanli Cai0Jingxian Guo1Runkui Li2Zhen Xiao3Haiyu Fu4Tongze Guo5Xiaoping Zhang6Yashuai Li7Xianfeng Song8College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaCollege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Economics and Management, Beihang University, Beijing 100191, China College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaAccurately estimating building heights is crucial for various applications, including urban planning, climate studies, population estimation, and environmental assessment. However, this remains a challenging task, particularly for large areas. Satellite-based Light Detection and Ranging (LiDAR) has shown promise, but it often faces difficulties in distinguishing building photons from other ground objects. To address this challenge, we propose a novel method that incorporates building footprints, relative positions of building and ground photons, and a self-adaptive buffer for building photon selection. We employ the Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) photon-counting LiDAR, specifically the ICESat-2/ATL03 data, along with building footprints obtained from the New York City (NYC) Open Data platform. The proposed approach was applied to estimate the heights of 17,399 buildings in NYC, and the results showed strong consistency with the reference building heights. The root mean square error (RMSE) was 8.1 m, and for 71% of the buildings, the mean absolute error (MAE) was less than 3 m. Furthermore, we conducted an extensive evaluation of the proposed approach and thoroughly investigated the influence of terrain, region, building height, building density, and parameter selection. We also verified the effectiveness of our approach in an experimental area in Beijing and compared it with other existing methods. By leveraging ICESat-2 LiDAR data, building footprints, and advanced selection techniques, the proposed approach demonstrates the potential to accurately estimate building heights over broad areas.https://www.mdpi.com/2072-4292/16/2/263building height estimationICESat-2LiDARbuilding footprintbuilding photon selection |
| spellingShingle | Panli Cai Jingxian Guo Runkui Li Zhen Xiao Haiyu Fu Tongze Guo Xiaoping Zhang Yashuai Li Xianfeng Song Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints Remote Sensing building height estimation ICESat-2 LiDAR building footprint building photon selection |
| title | Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints |
| title_full | Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints |
| title_fullStr | Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints |
| title_full_unstemmed | Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints |
| title_short | Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints |
| title_sort | automated building height estimation using ice cloud and land elevation satellite 2 light detection and ranging data and building footprints |
| topic | building height estimation ICESat-2 LiDAR building footprint building photon selection |
| url | https://www.mdpi.com/2072-4292/16/2/263 |
| work_keys_str_mv | AT panlicai automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints AT jingxianguo automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints AT runkuili automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints AT zhenxiao automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints AT haiyufu automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints AT tongzeguo automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints AT xiaopingzhang automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints AT yashuaili automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints AT xianfengsong automatedbuildingheightestimationusingicecloudandlandelevationsatellite2lightdetectionandrangingdataandbuildingfootprints |