2024 IEEE GRSS Data Fusion Contest - Flood Rapid Mapping

The Challenge Task  As a result of climate change, extreme hydrometeorological events are becoming increasingly frequent. Flood rapid mapping products play an important role in informing flood emergency response and management. These maps are generated quickly from remote sensing data during or after an event to show the extent of the flooding. They provide important information for emergency response and damage assessment. The aim of this challenge is to develop data fusion algorithms that generate flood maps by processing spatial data from a variety of sources.   The goal of this challenge is to design and develop an algorithm that will combine multi-source data to classify flood surface water extent–that is, water and non-water areas. Provided data sources include optical and Synthetic Aperture Radar (SAR) remote sensing images as well as a digital terrain model, land-use and water occurrence. The output is a gridded flood map where each grid cell is labeled water or non-water.   How to extract water areas from a remote sensing image depends largely on the acquisition technology. This data fusion challenge has two tracks representing this variability.   Track-1: Flood rapid mapping with SAR data   Track-2: Flood rapid mapping with optical data   No guidance is given on the method to be used for data fusion and pixel-wise classification; it could be based on a statistical approach, machine learning, or a combination of different approaches. 

搜集汇总

背景与挑战

背景概述

该数据集是2024年IEEE GRSS数据融合竞赛的一部分，专注于洪水快速测绘，旨在通过融合多源遥感数据（如光学和SAR图像、数字地形模型等）开发算法，以分类水与非水区域，生成网格化洪水地图。竞赛分为两个赛道：基于SAR数据和基于光学数据的洪水测绘，鼓励使用统计或机器学习方法进行数据融合和分类，以支持洪水应急响应和管理。