FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images
Sanidhya Ghosal, Anurag Sharma, Sushil Ghildiyal, Mukesh Saini

TL;DR
This paper presents FLNet, a deep learning architecture that enhances satellite image resolution via super-resolution to improve flood damage assessment of crops, enabling faster and more accurate post-disaster evaluations.
Contribution
Introduces FLNet, a novel super-resolution based deep learning model that significantly improves damage classification accuracy using satellite imagery.
Findings
F1-score for 'Full Damage' increased from 0.83 to 0.89
Super-resolution improved damage detection accuracy
Cost-effective, scalable damage assessment method
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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Flood Risk Assessment and Management · Remote Sensing in Agriculture
