FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
This provides a cost-effective and scalable solution for post-disaster agricultural management in India, shifting from manual to automated damage assessment.
The paper tackled the problem of rapid and accurate crop damage assessment after floods in India by introducing FLNet, a deep learning architecture that uses super-resolution to enhance Sentinel-2 satellite images from 10 m to 3 m resolution before classification, resulting in an improved F1-score for 'Full Damage' from 0.83 to 0.89, nearly matching commercial high-resolution imagery.
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.