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

Solemnly declare: If this data set is used in papers, books, academic reports and other works, please quote the following data papers: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/JEIT240062Authors: DYU Cuilin, WANG Qingsong, ZHONG Zixuan, ZHANG Junhao, LAI Tao, HUANG HaifengAuthor: School of Electronics and Communication Engineering, Sun Yat-sen UniversityCorrespondent: WANG Qingsong， [e-mail] link：利用全球开源数字高程模型的高程误差预测数据集Funds:  The National Natural Science Foundation of China (62273365, 62071499), Xiaomi Young Talents ProgramAbstract: 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.