Elevation Error Prediction Dataset Using Global Open-source Digital Elevation Model

YU Cuilin; WANG Qingsong; ZHONG Zixuan; ZHANG Junhao; LAI Tao; HUANG Haifeng

doi:10.11999/JEIT240062

Volume 46 Issue 9

Sep. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(9): 3445-3455

YU Cuilin, WANG Qingsong, ZHONG Zixuan, ZHANG Junhao, LAI Tao, HUANG Haifeng. Elevation Error Prediction Dataset Using Global Open-source Digital Elevation Model[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3445-3455. doi: 10.11999/JEIT240062

Citation:

YU Cuilin, WANG Qingsong, ZHONG Zixuan, ZHANG Junhao, LAI Tao, HUANG Haifeng. Elevation Error Prediction Dataset Using Global Open-source Digital Elevation Model[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3445-3455. doi: 10.11999/JEIT240062

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Elevation Error Prediction Dataset Using Global Open-source Digital Elevation Model

doi: 10.11999/JEIT240062

School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds: The National Natural Science Foundation of China (62273365, 62071499), Xiaomi Young Talents Program

Received Date: 2024-01-29
Rev Recd Date: 2024-06-18

Available Online: 2024-07-01

Publish Date: 2024-09-26

Abstract

Abstract

The correction in Digital Elevation Models (DEMs) has always been a crucial aspect of remote sensing geoscience research. The burgeoning development of new machine learning methods in recent years has provided novel solutions for the correction of DEM elevation errors. Given the reliance of machine learning and other artificial intelligence methods on extensive training data, and considering the current lack of publicly available, unified, large-scale, and standardized multisource DEM elevation error prediction datasets for large areas, the multi-source DEM Elevation Error Prediction Dataset (DEEP-Dataset) is introduced in this paper. This dataset comprises four sub-datasets, based on the TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) DEM and Advanced land observing satellite World 3D-30 m (AW3D30) DEM in the Guangdong Province study area of China, and the Shuttle Radar Topography Mission (SRTM) DEM and Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) DEM in the Northern Territory study area of Australia. The Guangdong Province sample comprises approximately 40 000 instances, while the Northern Territory sample includes about 1 600 000 instances. Each sample in the dataset consists of ten features, encompassing geographic spatial information, land cover types, and topographic attributes. The effectiveness of the DEEP-Dataset in actual model training and DEM correction has been validated through a series of comparative experiments, including machine learning model testing, DEM correction, and feature importance assessment. These experiments demonstrate the dataset’s rationality, effectiveness, and comprehensiveness.
- Digital Elevation Model (DEM),
- Artificial intelligence,
- Machine learning,
- Predictive datasets

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References(29)

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