基于GEDTM120米地形数据，经过掩膜处理得到的新疆、哈萨克斯坦、塔吉克斯坦、吉尔吉斯斯坦、乌兹别克斯坦和土库曼斯坦6个区域的地形数据，数据分辨率120米。
", "ds_source": "该数据集是荷兰OpenGeoHub机构的何玉峰团队于2025年2月发布在Zenodo网站上的数据，自今年2月以来已更新了4个版本。该数据集简称为GEDTM30。由于30米数据集目前还未正式出版，数据论文正在Research Square平台的评审之中，数据作者仅开放了120米的DEM地形数据，30米地形数据需等评审完成后才能获取。
", "ds_process_way": "论文作者提出全球-局部迁移学习框架，生成1arcsec（约30m）分辨率的无空隙全球DEM（GEDTM30），并提取坡度、曲率、汇水面积等15类地表形态与水文参数。研究整合多源异构数据（光学影像、雷达、激光雷达），通过中位数滤波与异常值剔除生成初始DEM，结合ICESat-2（20亿点）和GEDI（10亿点）激光雷达数据构建训练样本，利用GNSS站数据验证模型精度。针对数据稀疏区域（如太平洋岛屿），创新性地采用全球-局部迁移学习策略：全局模型基于10%随机样本训练随机森林（RF）捕捉共性特征，局部模型按5°×5°分块，融入高程异常与植被覆盖等本地化样本微调参数，显著提升区域适应性。地表参数化阶段，通过白盒工具（WhiteboxTools）结合Equi7投影系统实现多尺度参数高效计算（30–960m），优化水文连通性与计算效率，最终生成兼具垂直精度（森林区RMSE23.2m）与区域适应性（沿海误差降低50%）的全球地形基准数据集。
", "ds_quality": "森林区RMSE23.2m，沿海误差降低50%。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 39133930707, "ds_files_count": 32, "ds_format": "", "ds_space_res": "120米", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "GCS_WGS84", "ds_thumbnail": "d77f793a-94c3-428c-a282-b7a82920cba1.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "Ho, Y., & Hengl, T. (2025). Global Ensemble Digital Terrain Model 30m (GEDTM30) (v1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15689805", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.40", "170.45" ], "quality_level": 1, "publish_time": "2025-08-06 16:35:45", "first_publish_time": null, "last_updated": "2025-08-06 16:35:45", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "license": null, "extra": null, "files_shape": [ { "name": "GEDTM_120m.rar", "size": 37658708677, "is_dir": false }, { "name": "v1_covered_f59649fa-39aa-4fbd-8c86-91f4966f0d34.pdf", "size": 25272802, "is_dir": false }, { "name": "Clip", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "GEDTM 120m Topographic Data for Xinjiang and Five Central Asian Countries", "ds_abstract": "Based on GEDTM120-meter terrain data, terrain data of six regions: Xinjiang, Kazakhstan, Tajikistan, Kyrgyzstan, Uzbekistan and Turkmenistan are obtained after mask processing, with a data resolution of 120 meters.
", "ds_source": "This dataset was released by He Yufeng's team from OpenGeoHub in the Netherlands on the Zenodo website in February 2025. It has been updated in four versions since February this year. This dataset is simply referred to as GEDTM30. Since the 30-meter dataset has not yet been officially published, the data papers are being reviewed on the Research Square platform. The data author has only opened up the 120-meter DEM terrain data, and the 30-meter terrain data will not be available until the review is completed.
", "ds_process_way": "The author of the paper proposed a global-local migration learning framework to generate a gap-free global DEM (GEDTM30) with a resolution of 1 arcsec (about 30m), and extracted 15 types of surface morphology and hydrological parameters such as slope, curvature, and catchment area. Research integrates multi-source heterogeneous data (optical imaging, radar, lidar), generates initial DEM through median filtering and outlier removal, and combines ICESat-2 (2 billion points) and GEDI (1 billion points) lidar data to build training samples, and uses GNSS station data to verify model accuracy. For areas with sparse data (such as Pacific islands), innovatively adopt a global-local migration learning strategy: the global model is based on 10% random samples to train random forests (RF) to capture common characteristics, and the local model is segmented by 5°×5°, integrating localized samples such as elevation anomalies and vegetation coverage fine-tune parameters to significantly improve regional adaptability. In the surface parameterization stage, Whitebox Tools combined with the Equi7 projection system is used to realize efficient calculation of multi-scale parameters (30 - 960m), optimize hydrological connectivity and calculation efficiency, and finally generate a global terrain benchmark dataset with both vertical accuracy (forest area RMSE 23.2 m) and regional adaptability (50% reduction in coastal error).
", "ds_quality": "The RMSE in forest area is 23.2 m, and the coastal error is reduced by 50%.
", "ds_ref_instruction": "Ho, Y., & Hengl, T. (2025). Global Ensemble Digital Terrain Model 30m (GEDTM30) (v1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15689805", "ds_projection": "GCS_WGS84", "ds_space_res": "120 meters" } }, "license_type": null, "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "集成数字地形模型", "DEM", "随机森林", "DTM" ], "ds_subject_tags": [ "地图学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球", "中国新疆", "哈萨克斯坦", "乌兹别克斯坦", "土库曼斯坦", "吉尔吉斯斯坦", "塔吉克斯坦" ], "ds_time_tags": [ 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 ], "ds_contributors": [ "李锦" ], "ds_meta_authors": [ "李锦" ], "ds_managers": [ "李锦" ], "category": "DEM数字高程" }