Finetuning Geospatial Foundation Model for Wildfire Scars in Himalayan Region

The application of Artificial Intelligence (AI) in Earth Science and Remote Sensing has transformed environmental monitoring. Foundation models, pre-trained on massive unlabeled datasets, are particularly effective for geospatial tasks, offering adaptability and efficiency. In this study, we fine-tune Prithvi 100M, a transformer-based geospatial foundation model pre-trained on over 1TB of multispectral imagery from the Harmonized Landsat-Sentinel 2 (HLS) dataset, for burn scar mapping in the challenging Himalayan terrain. Burn scar mapping is crucial for monitoring post-fire landscapes, assessing ecosystem recovery, and disaster risk management. However, the complex topography and environmental conditions of the Himalayas pose significant challenges for traditional remote sensing methods. To address these challenges, Prithvi 100M was calibrated with region-specific burn scar labels and analyzed its performance using 2-band, 3-band, and 6-band spectral inputs. Our results revealed that 2-band and 3-band configurations achieved accuracy comparable to the 6-band setup, demonstrating the potential for band selection or information reduction without significant performance loss. However, the 6-band configuration showed slightly better precision and robustness, underscoring the value of additional spectral information. This study highlights the flexibility and effectiveness of geospatial foundation models for tasks in regions with limited training data. The ability to utilize fewer spectral bands without sacrificing performance is particularly advantageous for resource-constrained environments. Our findings demonstrate the promise of foundation models in remote sensing and pave the way for further applications in environmental monitoring and disaster management.