中西亚及中国西北地区作为“一带一路”建设的重要组成部分，正面临洪水灾害对区域可持续发展的严峻挑战。鉴于该地区现有洪水灾害信息单一、分析提取不足等问题，本研究以2017–2022年“一带一路”中西亚及中国西北地区典型洪水的雷达影像为基础，通过面向对象法提取灾害前后的水体信息，采用变化监测确定洪水淹没范围，并结合光学影像对结果进行验证，最终得到洪水遥感影像及解译数据集，以弥补现有洪水灾害信息不全面、不详细的不足。本数据集包括历史洪水灾害数据集和洪水解译数据集：（1）历史洪水灾害数据集包括“一带一路”中西亚及中国西北地区典型历史洪水灾害数据、对应的Excel数据、DEM数据。（2）洪水解译数据集包括涵盖洪水淹没范围的SAR影像数据、光学影像数据、解译标注数据。洪水总体分布范围为22°42′25″–47°22′52″N，39°21′54″–101°17′24″E，包含10处典型洪水灾害样本。通过引入Landsat光学影像辅助验证与人工判读反馈优化，洪水淹没范围提取结果的准确性得到有效提升，正确分类样本占比稳定达到85%以上，有效减少了信息遗漏与误判，验证了数据集的可靠性与完整性。本数据集数据源明确，数据质量把控严格，能够为中西亚及中国西北相关区域的水文研究、灾害损失以及灾害风险评估提供丰富的样本数据，作为洪水监测工作的数据支撑。
", "ds_source": "数据集包括洪水点数据集和洪水解译数据集，洪水点数据集包括洪水点数据、对应的Excel数据、DEM数据，洪水解译数据集包括SAR影像数据、光学影像数据、解译标注数据。（1）洪水点数据集：洪水点的原始数据来源于全球灾害数据平台，根据洪水灾害发生时间、死亡人数等关键信息，对点数据进行筛选，最终选取10处典型洪水灾害点参与样本制作，每条数据的属性表包括灾害发生时间、经纬度位置、死亡人数等信息。数字高程模型(Digital Elevation Model,DEM)数据来源于美国国家航空航天局(NASA)发布的SRTMDEM；（2）洪水解译数据集：样本数据基于Sentinel-1雷达影像，选用干涉宽幅模式(Interferometric Wide, IW)下的20景升轨单视复数(Single Look Complex,SLC)影像，极化方式为VV，空间分辨率为5m×20m，影像时间范围覆盖2017-2022年，数据来源为ASF Data Search网站，高空间分辨率Landsat光学遥感数据获取自美国地质勘探数据中心(USGS)，基于上述数据，本研究利用ENVI和ArcGIS软件对洪水灾害点实现解译标注。
", "ds_process_way": "数据加工主要分为两部分：（1）洪水点数据集：洪水点的原始数据来源于全球灾害数据平台，选取了具有坐标信息的数据，整理为Excel文件。根据洪水灾害发生时间、死亡人数等关键信息，选取10处典型洪水灾害点，每条数据的属性表包括灾害发生时间、经纬度位置、死亡人数等信息；（2）洪水解译数据集：基于Sentinel-1雷达影像数据，利用ENVI软件，采用面向对象法分别对灾前、灾后影像进行水体提取。使用ArcGIS软件进行变化监测，剔除灾害前后变化不显著的区域，获得洪水淹没范围。随后，结合对应洪水范围的光学遥感影像，对结果进行验证，最终制作了10处洪水灾害的解译数据集。
", "ds_quality": "本研究严格按照设计的技术路线进行产品生产，数据质量控制主要从数据源质量和解译数据检验两个方面入手：(1)数据源质量控制：洪水点位置信息来源于全球灾害数据平台，数据准确可靠，且经过严格筛选后投入数据集的制作，洪水灾害发生前后的影像均来自NASA官网，同一组灾害数据严格按照具有相同成像几何参数和传感器配置的数据标准进行选取，避免了参数不同对解译结果的影响；(2)解译数据检验：在洪灾区域检测中，光学遥感能够提供丰富的光谱信息和较高的图像分辨率，而雷达遥感则具备全天候的洪灾区域检测能力。考虑到光学影像丰富的光谱信息，在依据面向对象法与变化监测得到的解译结果基础上，选取了洪水灾害发生后的光学影像进行结果检验，光学影像受云量影响较大，本研究通过筛选得到云量最少的影像，满足云量条件的同时选择时间最接近灾害发生后的影像，经过处理后对解译数据进行检验，验证了结果的可靠性。综上所述，本研究通过层层筛选确保了数据源质量，依托大量文献作为理论基础，确定了科学的研究方法，选取图像分辨率较高的光学影像对研究结果进行验证分析，提高了提取精度，为洪水灾害监测工作提供了重要的数据支持。
", "ds_acq_start_time": "2017-11-21 00:00:00", "ds_acq_end_time": "2022-01-19 00:00:00", "ds_acq_place": "一带一路地区", "ds_acq_lon_east": 39.365, "ds_acq_lat_south": 47.38111111111111, "ds_acq_lon_west": 101.28999999999999, "ds_acq_lat_north": 22.706944444444442, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 13667546705, "ds_files_count": 2, "ds_format": "*.shp,*.tif,*.img,*.xls", "ds_space_res": "5m×20m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "532ef2b7-6a28-493e-a7fa-26356b8cf9fc.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "952adb3f-3ede-4a94-942a-7de772f1bfc5", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170", "170.45" ], "quality_level": 3, "publish_time": "2025-08-07 15:34:35", "last_updated": "2026-06-01 11:45:56", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6940.2025", "i18n": { "en": { "title": "Remote sensing images and interpretation datasets of flood in typical areas of the \"Belt and Road\" Central, West Asia and Northwest China", "ds_format": "*.shp,*.tif,*.img,*.xls", "ds_source": "The data set includes a flood point data set and a flood interpretation data set. The flood point data set includes flood point data, corresponding Excel data, and DEM data. The flood interpretation data set includes SAR image data, optical image data, and interpretation annotation data. (1) Flood point data set: The original data of flood points comes from the global disaster data platform. According to key information such as flood disaster occurrence time and death toll, the point data is filtered, and finally 10 typical flood disaster points are selected to participate in sample production. The attribute table of each piece of data includes information such as disaster occurrence time, latitude and longitude location, and death toll. digital elevation model (Digital Elevation Model (DEM) data is derived from the SRTMDEM released by NASA;(2) Flood interpretation dataset: The sample data is based on Sentinel-1 radar images and adopts interference wide format Single-view complex number of 20 scenes elevated track under (Interferometric Wide, IW)(Single Look Complex (SLC) image, polarization method is VV, spatial resolution is 5m×20m, image time range covers 2017-2022, data source is ASF Data Search website, high spatial resolution Landsat optical remote sensing data obtained from the U.S. Geological Survey Data Center (USGS), based on the above data, In this study, ENVI and ArcGIS software were used to interpret and label flood disaster points.
", "ds_quality": "This research strictly follows the designed technical route for product production. Data quality control mainly starts from two aspects: data source quality and interpretation data verification: (1) Data source quality control: The location information of flood points comes from the global disaster data platform. The data is accurate and reliable, and is put into the production of data sets after strict screening. The images before and after the flood disaster are all from the NASA official website. The same set of disaster data strictly follows the same imaging geometric parameters and sensors. The data standards of the configuration are selected to avoid the impact of different parameters on the interpretation results;(2) Interpretation data verification: In flood area detection, optical remote sensing can provide rich spectral information and high image resolution, while radar remote sensing has all-weather flood area detection capabilities. Taking into account the rich spectral information of optical images, based on the interpretation results obtained based on the object-oriented method and change monitoring, the optical images after the flood disaster were selected for result verification. The optical images were greatly affected by cloud cover. In this study, the image with the least cloud cover was obtained by screening, and the image with the time closest to the disaster occurred was selected while meeting the cloud cover conditions. After processing, the interpretation data was tested to verify the reliability of the results. To sum up, this study ensured the quality of data sources through layer screening, relied on a large number of documents as a theoretical basis, determined scientific research methods, selected optical images with high image resolution to verify and analyze the research results, and improved the extraction accuracy., providing important data support for flood disaster monitoring.
", "ds_ref_way": "", "ds_abstract": "As important parts of the \"Belt and Road\" construction, Central and West Asia and Northwest China are facing severe challenges posed by flood disasters to regional sustainable development. In view of the problems of single existing flood disaster information in the region and insufficient analysis and extraction, this study is based on radar images of typical floods in Central, West Asia and Northwest China during the \"Belt and Road\" period from 2017 to 2022. It extracts water body information before and after the disaster through object-oriented method, uses change monitoring to determine the flood inundation scope, and combines the results with optical images to verify the results. Finally, flood remote sensing images and interpretation datasets are obtained to make up for the incomplete existing flood disaster information. Inadequate details. This dataset includes historical flood disaster datasets and flood interpretation datasets: (1) The historical flood disaster datasets include typical historical flood disaster data in Central, West Asia and Northwest China of the Belt and Road Initiative, corresponding Excel data, and DEM data. (2) The flood interpretation data set includes SAR image data, optical image data, and interpretation and annotation data covering the flood inundation area. The overall flood distribution range is 22°42 '25 \"-47°22' 52\" N, 39°21 '54 \"-101°17' 24\" E, including 10 typical flood disaster samples. By introducing Landsat optical image-assisted verification and manual interpretation feedback optimization, the accuracy of flood inundation range extraction results has been effectively improved, and the proportion of correctly classified samples has stabilized to more than 85%, effectively reducing information omissions and misjudgments, and verifying the data set. Reliability and integrity. The data source of this dataset is clear and the data quality is strictly controlled. It can provide rich sample data for hydrological research, disaster loss and disaster risk assessment in relevant areas of Central, West Asia and Northwest China, and serve as data support for flood monitoring work.
", "ds_time_res": "", "ds_acq_place": "Belt and Road regions", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Data processing is mainly divided into two parts: (1) Flood point data set: The original data of flood points comes from the global disaster data platform. Data with coordinate information are selected and organized into Excel files. Based on key information such as flood disaster occurrence time and death toll, 10 typical flood disaster points are selected, and the attribute table of each data includes disaster occurrence time, latitude and longitude location, death toll and other information;(2) Flood interpretation data set: Based on Sentinel-1 radar image data, ENVI software is used to extract water bodies from pre-disaster and post-disaster images respectively using object-oriented methods. Use ArcGIS software to monitor changes, eliminate areas with insignificant changes before and after the disaster, and obtain the flood inundation scope. Subsequently, the results were verified by combining optical remote sensing images corresponding to the flood range, and interpretation data sets of 10 flood disasters were finally produced.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "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, "belong_to_nieer": false, "ds_topic_tags": [ "一带一路", "SAR", "洪水", "面向对象法", "变化监测", "解译数据集" ], "ds_subject_tags": [ "地球科学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中西亚地区", "新疆", "甘肃省" ], "ds_time_tags": [ 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "火久元", "email": "huojy@mail.lzjtu.cn", "work_for": "兰州交通大学电子与信息工程学院，国家冰川冻土沙漠科学数据中心，中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王惠", "email": "2972217960@qq.com", "work_for": "测绘与地理信息学院，兰州交通大学", "country": "中国" }, { "true_name": "王院荣", "email": "2552691092@qq.com", "work_for": "测绘与地理信息学院，兰州交通大学", "country": "中国" }, { "true_name": "敏玉芳", "email": "myf@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "康建芳", "email": "kangjf@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "张耀南", "email": "yaonan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王惠", "email": "2972217960@qq.com", "work_for": "测绘与地理信息学院，兰州交通大学", "country": "中国" } ], "ds_managers": [ { "true_name": "火久元", "email": "huojy@mail.lzjtu.cn", "work_for": "兰州交通大学电子与信息工程学院，国家冰川冻土沙漠科学数据中心，中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "水文" }