地貌数据集是实现地貌自动分类和加深对地貌形态学认识的重要支撑数据之一。当前缺乏高精度地貌成因类数据集，制约了地貌遥感自动解译的发展。本文在中国东北地区以沟—弧—盆体系为主的天山—兴蒙造山系中，针对强烈的构造运动和新生代以来的火山作用、流水作用形成的地貌成因类型，制作了构造地貌、火山熔岩地貌和流水地貌3类场景数据集（GOS10m）。数据集覆盖面积约5000 km2，包括哨兵2号可见光遥感影像、SRTM1 DEM及基于DEM提取的7个地貌形态参数（山体晕渲图、坡度、DEM局部平均中值、标准偏差、坡向—向北方向偏移量、坡向—向东方向偏移量和相对偏离平均值）。单张样本图为64像素×64像素，空间分辨率为10 m。采用多模态深度学习神经网络对数据进行训练并分类，平均测试精度可达到82.63%，表明构建的数据集具有较高的质量。可为地貌成因遥感自动分类研究以及推动遥感地貌智能解译的向前发展，提供数据集支撑。
", "ds_source": "1∶25万、1∶20万基础地质图、高程DEM数据、中国1∶100万数字地貌图，以及哨兵2号多光谱影像。
", "ds_process_way": "GOS10m数据集制作主要分为3个阶段。首先对遥感影像数据源预处理后裁剪得到遥感场景数据集。其次，对获取的DEM进行成分提取和预处理操作。最后，以遥感场景数据集为空间基准，对预处理后的DEM及其提取成分、解译结果矢量图进行空间上裁剪，得到DEM及其成分数据集及解译标签。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2020-10-26 00:00:00", "ds_acq_end_time": "2020-10-26 00:00:00", "ds_acq_place": "吉林省、黑龙江省交界处", "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": "open-access", "ds_total_size": 1739579103, "ds_files_count": 18, "ds_format": "tiff、txt", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "6794d67f-164f-4ab6-8c5e-a2da473b28b2.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-07-26 17:03:17", "last_updated": "2025-04-23 09:53:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.JRS.DB6678.2024", "i18n": { "en": { "title": "Geomorphological scene classification dataset of high-resolution remote sensing imagery in vegetationcovered areas", "ds_format": "tiff、txt", "ds_source": "1:250000, 1:200000 basic geological maps, elevation DEM data, 1:1 1000000 digital topographic maps of China, and Sentinel-2 multispectral images.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "The landform dataset is one of the important supporting data for achieving automatic classification of landforms and deepening the understanding of landform morphology. The current lack of high-precision geomorphic genesis datasets hinders the development of automatic interpretation of geomorphic remote sensing. This article focuses on the Tianshan Xingmeng orogenic system in northeastern China, which is mainly characterized by the trench arc basin system. Three types of scene datasets (GOS10) were created for the geomorphological genesis types formed by strong tectonic movements, volcanic and fluvial processes since the Cenozoic era, including tectonic geomorphology, volcanic lava geomorphology, and fluvial geomorphology. The dataset covers an area of approximately 5000 km2, including Sentinel-2 visible light remote sensing images, SRTM1 DEM, and 7 geomorphic parameters extracted based on DEM (mountain shading map, slope, DEM local mean, standard deviation, slope to north offset, slope to east offset, and relative deviation average). A single sample image is 64 pixels by 64 pixels, with a spatial resolution of 10 meters. Using multimodal deep learning neural networks to train and classify data, the average testing accuracy can reach 82.63%, indicating that the constructed dataset has high quality. It can provide dataset support for the research of remote sensing automatic classification of landform genesis and promote the development of intelligent interpretation of remote sensing landforms.
", "ds_time_res": "", "ds_acq_place": "At the border of Jilin Province and Heilongjiang Province", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The production of the GOS-10m dataset is mainly divided into three stages. Firstly, preprocess the remote sensing image data source and crop it to obtain a remote sensing scene dataset. Secondly, perform component extraction and preprocessing operations on the obtained DEM. Finally, using the remote sensing scene dataset as a spatial reference, the preprocessed DEM and its extracted components, as well as the interpretation result vector map, were spatially cropped to obtain the DEM and its component dataset and interpretation labels.
", "ds_ref_instruction": "When using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "构造地貌", "火山熔岩地貌", "流水地貌", "场景数据集" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "吉林省", "黑龙江省" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ { "true_name": "欧阳淑冰", "email": "oysb@cug.edu.cn", "work_for": "中国地质大学(武汉) 计算机学院", "country": "中国" }, { "true_name": "陈伟涛", "email": "wtchen@cug.edu.cn", "work_for": "中国地质大学(武汉) 计算机学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "欧阳淑冰", "email": "oysb@cug.edu.cn", "work_for": "中国地质大学(武汉) 计算机学院", "country": "中国" }, { "true_name": "陈伟涛", "email": "wtchen@cug.edu.cn", "work_for": "中国地质大学(武汉) 计算机学院", "country": "中国" } ], "ds_managers": [ { "true_name": "欧阳淑冰", "email": "oysb@cug.edu.cn", "work_for": "中国地质大学(武汉) 计算机学院", "country": "中国" }, { "true_name": "陈伟涛", "email": "wtchen@cug.edu.cn", "work_for": "中国地质大学(武汉) 计算机学院", "country": "中国" } ], "category": "生态" }