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Domain Adaptation of Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India
Version 1
: Received: 3 September 2024 / Approved: 3 September 2024 / Online: 4 September 2024 (16:08:06 CEST)
How to cite:
Anand, A.; Imasu, R.; Dhaka, S. K.; Patra, P. Domain Adaptation of Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India. Preprints2024, 2024090333. https://doi.org/10.20944/preprints202409.0333.v1
Anand, A.; Imasu, R.; Dhaka, S. K.; Patra, P. Domain Adaptation of Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India. Preprints 2024, 2024090333. https://doi.org/10.20944/preprints202409.0333.v1
Anand, A.; Imasu, R.; Dhaka, S. K.; Patra, P. Domain Adaptation of Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India. Preprints2024, 2024090333. https://doi.org/10.20944/preprints202409.0333.v1
APA Style
Anand, A., Imasu, R., Dhaka, S. K., & Patra, P. (2024). Domain Adaptation of Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India. Preprints. https://doi.org/10.20944/preprints202409.0333.v1
Chicago/Turabian Style
Anand, A., Surendra Kumar Dhaka and Prabir Patra. 2024 "Domain Adaptation of Deep Learning Segmentation Model of Small Agricultural Burn Area Detection Using Hi-Resolution Sentinel-2 Observations: A Case Study of Punjab, India" Preprints. https://doi.org/10.20944/preprints202409.0333.v1
Abstract
This study investigates the capabilities of high-resolution Sentinel-2 observations and deep learning (DL) segmentation models, specifically convolutional neural networks (CNNs), for accurate mapping of small and fragmented agricultural burn areas in Punjab, India. Initially, the model was trained using ICNF burn area data from Portugal to capture large fire and burn area delineation, achieving moderate accuracy. Subsequent fine-tuning using annotated data from Punjab improved the model’s ability to detect small burn patches, demonstrating higher accuracy and precision than the baseline Normalised Burn Ratio (NBR) Index method. On-ground validation using buffer zone analysis and crop field images confirms the DL approach effectiveness. Cloud interference and temporal gaps in satellite data posed some challenges. Despite these limitations, the study highlights the methodological advancements and potential of using DL models for small burn area detection in agricultural settings. The model achieved overall testing accuracy of 0.64 in Dice score assessment. This report presents a promising approach for improved mapping of the burn area, which might engender better estimation of emissions and the management practices in regions with little available ground truth data.
Keywords
burn areas; crop residue burning; deep learning; neural networks; transfer learning
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
