FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images

• URL: http://arxiv.org/abs/2601.03884v1
• Date: Wed, 07 Jan 2026 12:51:28 GMT
• Title: FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
• Authors: Sanidhya Ghosal, Anurag Sharma, Sushil Ghildiyal, Mukesh Saini,
• Abstract summary: In India, the crops are widely affected by floods; making rapid and accurate crop damage assessment is crucial for post-disaster agricultural management.<n>Traditional manual surveys are slow and biased, while current satellite-based methods face challenges like cloud cover and low spatial resolution.<n>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.
• Score: 3.806192383117017
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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