Aug 2026· 2026 2nd International Conference on Electronic Information, Computer and Aerospace Remote Sensing (EICARS)· pp. 109-113· 0 citations· 9 references
Abstract
Digital elevation models (DEMs) are essential for terrain-related remote sensing applications, but their accuracy often decreases in complex mountainous terrain. This study investigates the use of the original Mamba model for DEM elevation error correction in a selected mountainous region of the Alps. The Copernicus Digital Elevation Model (COPDEM), Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) ground elevations, and ETH2020 canopy height data were integrated to construct patch-based samples for error modeling. Experimental results show that the patch-based dataset construction outperforms tabular-data-based modeling by incorporating local terrain context, indicating that neighborhood topographic information is important for DEM error correction in mountainous areas. The original Mamba model achieves the best overall correction performance among the compared methods and further improves COPDEM accuracy relative to other DEM products, demonstrating its effectiveness in modeling spatially varying elevation errors in complex mountainous terrain.
Hydrologic analysis in regions with complex terrain is essential for understanding and mitigating the risks associated with extreme events, such as floods and landslides. This study evaluates the accuracy of hydrologic analyses using freely available Global Digital Elevation Models (GDEMs) in comparison to a nationally...
A. al-Areeq· Proceedings of International...· 0 citations
Digital Elevation Models (DEMs) are essential for terrain analysis, hydrological modeling, disaster assessment, and environmental monitoring. However, freely available DEMs often contain significant vertical errors due to the terrain complexity, vegetation cover, and sensor-related limitations. Although Machine Learnin...
T. Jayathilaka, G. N. H. Wanigaratne· Journal of Multidisciplinary...· 0 citations
This study utilizes the Google Earth Engine (GEE) platform, integrating Landsat and Sentinel-2 imagery from 1987 to 2022 alongside terrain data to develop a technical system for “vegetation cover inversion–classification–vertical belt spectrum analysis.” The Fractional Vegetation Cover (FVC) is estimated using a pixel...
Rong-Rong Wang, Wen-Gang Cui, Sui-Hua Liu et al.· PLoS ONE· 0 citations
This study takes Zhaozhou County in the Songnen Plain (a plain area with sparse borehole data) as the study area, and aims to address the issue of elevation data sources' impact on groundwater numerical simulation by comparatively analyzing the application effects of the Shuttle Radar Topography Mission Digital Ele...
Yun Bai, Zhi-Jun Li, Qing Ru et al.· Journal of groundwater scien...· 0 citations
Spaceborne LiDAR provides essential observations of forest vertical structure, yet canopy height retrievals remain vulnerable to terrain-related errors in steep mountainous forests, where terrain heterogeneity complicates attribution of error to footprint geolocation and ground elevation representation. To distinguish...
