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江西九江地区洪涝无人机高分辨率影像数据集

李雪林 岳焕印 戴琪 郝丽娜 贺洪波 杜冰 肖祥

中国科学数据(中英文网络版)2026,Vol.11Issue(1):19-30,12.
中国科学数据(中英文网络版)2026,Vol.11Issue(1):19-30,12.DOI:10.11922/11-6035.noda.2025.0188.zh

江西九江地区洪涝无人机高分辨率影像数据集

A dataset of high-resolution UAV images of floods and waterlogging in Jiujiang,Jiangxi Province

李雪林 1岳焕印 1戴琪 1郝丽娜 1贺洪波 1杜冰 1肖祥1

作者信息

  • 1. 中国科学院地理科学与资源研究所,北京 100101||中国科学院大学,北京 100101
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摘要

Abstract

Flood disasters severely threaten socio-economic development and the safety of lives and property,making the accurate acquisition of disaster information crucial for effective response.Traditional satellite remote sensing is limited by spatial resolution and revisit frequency,and often fails to capture dynamic flood details.Unmanned Aerial Vehicles(UAVs),leveraging high mobility,high-resolution imaging,and flexible deployment,can quickly access disaster-affected areas to acquire fine-grained imagery,compensating for the shortcomings of traditional methods.However,existing UAV image datasets for flood research generally suffer from limited sample sizes and lack of scenario diversity.This study focuses on the typical flood event in Jiujiang,Jiangxi Province in 2022.UAV field surveys were conducted to collect 412 high-resolution images during the disaster event.Through a rigorous data processing workflow,12,080 training-ready samples with a resolution of 512×512 pixels were generated,covering six semantic categories:water,building,road,flooded building,flooded road,and background.To evaluate dataset quality,multiple mainstream semantic segmentation models were tested.The experimental results fully validate the reliability of the proposed dataset,as well as its challenging nature in evaluating segmentation algorithms for complex scenes.This dataset provides crucial data support for scientific decision-making by local disaster management authorities and holds significant value for enhancing flood prevention and mitigation capabilities in affected regions.

关键词

无人机/洪涝灾害/样本标注/数据集/遥感监测/深度学习

Key words

UAV/flood disaster/sample annotation/dataset/remote sensing monitoring/deep learning

引用本文复制引用

李雪林,岳焕印,戴琪,郝丽娜,贺洪波,杜冰,肖祥..江西九江地区洪涝无人机高分辨率影像数据集[J].中国科学数据(中英文网络版),2026,11(1):19-30,12.

基金项目

中国科学院B类战略先导专项(XDB0740100) (XDB0740100)

广西重点研发计划(桂科AB25069501) (桂科AB25069501)

国家重点研发计划(2023YFB3905705). Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB0740100) (2023YFB3905705)

Key Research and Development Program of Guangxi(GuikeAB25069501) (GuikeAB25069501)

National Key Research and Development Program of China(No.2023YFB3905705). (No.2023YFB3905705)

中国科学数据(中英文网络版)

2096-2223

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