Abstract
Distributing government relief efforts after a flood is challenging. In India, the crops are widely affected by floods; therefore, making rapid and accurate crop damage assessment is crucial for effective post-disaster agricultural management. Traditional manual surveys are slow and biased, while current satellite-based methods face challenges like cloud cover and low spatial resolution. Therefore, to bridge this gap, this paper introduced FLNet, a novel deep learning based architecture that used super-resolution to enhance the 10 m spatial resolution of Sentinel-2 satellite images into 3 m resolution before classifying damage. We tested our model on the Bihar Flood Impacted Croplands Dataset (BFCD-22), and the results showed an improved critical "Full Damage" F1-score from 0.83 to 0.89, nearly matching the 0.89 score of commercial high-resolution imagery. This work presented a cost-effective and scalable solution, paving the way for a nationwide shift from manual to automated, high-fidelity damage assessment.
Abstract (translated)
洪水后的政府救援工作分配是一个挑战。在印度，农作物受到洪灾的影响广泛；因此，进行快速且准确的作物损失评估对于有效的灾后农业管理至关重要。传统的手工调查速度慢且存在偏见，而当前基于卫星的方法则面临云层遮挡和空间分辨率低的问题。为此，本文引入了FLNet，这是一种新型深度学习架构，使用超分辨率技术将Sentinel-2卫星图像的空间分辨率从10米提升至3米，然后再进行损失分类。我们在比哈尔邦洪水影响农作物数据集（BFCD-22）上测试了我们的模型，并且结果显示关键的“完全损毁”F1分数从0.83提高到了0.89，几乎与商用高分辨率影像所获得的0.89得分持平。这项工作提供了一种成本效益高且可扩展的解决方案，为全国范围内从手工评估向自动化、高保真度损失评估转变铺平了道路。
URL
https://arxiv.org/abs/2601.03884
