TY  - Data
T1  - Remote sensing images and interpretation datasets of flood in typical areas of the "Belt and Road" Central, West Asia and Northwest China
A1  - HUO Jiuyuan
A1  - WANG Hui
A1  - WANG Yuanrong
A1  - MIN Yufang
A1  - Kang Jianfang
A1  - Zhang Yaonan
DO  - 10.12072/ncdc.nieer.db6940.2025
PY  - 2025
DA  - 2025-08-07
PB  - National Cryosphere Desert Data Center
AB  - 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, disaste
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/284c1356-dd48-46ab-9914-f1bb4f47bc9a
ER  -