- By:
- Song, Hunsoo; Yang, Hsiuhan
- Page Number:
- 8642-8644
- Book Title:
- IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
- Conference Location:
- Athens, Greece
- Conference Sponsor:
- IEEE GRSS
- View DOI Listing:
- https://doi.org/10.1109/IGARSS53475.2024.10641787
Abstract
In this paper, we address the need for extracting two key building elevation attributes—Lowest Adjacent Grade (LAG) and Highest Adjacent Grade (HAG)—which are crucial for effective flood risk management. Conventional methods, involving onsite surveying or the use of optical imagery-derived building footprints combined with Digital Elevation Models (DEMs), often face misalignment and time discrepancy issues due to varied remote sensing sources. We introduce a new, scalable method that exclusively relies on airborne LiDAR data to overcome these challenges. Our approach employs an object-based ground filtering technique, and the results were evaluated using two different DEMs and building footprint sets. The findings demonstrate that our single-source method, utilizing only airborne LiDAR data, significantly improves the accuracy of LAG and HAG calculations compared to traditional methods that use hand-digitized building footprints. The proposed approach offers a solution for comprehensive flood risk management endeavors.
