Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints
文献类型：期刊论文
| 作者 | Cai, Panli1; Guo, Jingxian1; Li, Runkui1,2,3; Xiao, Zhen1; Fu, Haiyu1; Guo, Tongze1; Zhang, Xiaoping1; Li, Yashuai4; Song, Xianfeng1,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 16期号:2页码:24 |
| 关键词 | building height estimation ICESat-2 LiDAR building footprint building photon selection |
| DOI | 10.3390/rs16020263 |
| 通讯作者 | Li, Runkui(lirk@ucas.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | MODELS ; POPULATION ; CHINA ; LIDAR |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001151349400001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202319]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Runkui |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Binzhou Inst Technol, Binzhou 256606, Peoples R China 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Panli,Guo, Jingxian,Li, Runkui,et al. Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints[J]. REMOTE SENSING,2024,16(2):24. |
| APA | Cai, Panli.,Guo, Jingxian.,Li, Runkui.,Xiao, Zhen.,Fu, Haiyu.,...&Song, Xianfeng.(2024).Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints.REMOTE SENSING,16(2),24. |
| MLA | Cai, Panli,et al."Automated Building Height Estimation Using Ice, Cloud, and Land Elevation Satellite 2 Light Detection and Ranging Data and Building Footprints".REMOTE SENSING 16.2(2024):24. |
入库方式： OAI收割
来源：地理科学与资源研究所
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