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.

翻译：洪水过后政府救灾资源的分配面临挑战。在印度，农作物受洪水影响广泛，因此快速准确的作物损害评估对有效的灾后农业管理至关重要。传统人工调查方式缓慢且存在偏差，而当前基于卫星的方法面临云层遮挡和空间分辨率低等挑战。为此，为弥补这一差距，本文提出了FLNet——一种基于深度学习的新型架构，该架构在损害分类前利用超分辨率技术将Sentinel-2卫星图像的空间分辨率从10米提升至3米。我们在比哈尔邦洪水影响农田数据集（BFCD-22）上测试了模型，结果显示关键的"完全损害"F1分数从0.83提升至0.89，几乎与商业高分辨率影像0.89的得分持平。这项工作提出了一种经济高效且可扩展的解决方案，为全国范围内从人工评估向自动化高保真损害评估的转变铺平了道路。