SAModified: A Foundation Model-Based Zero-Shot Approach for Refining Noisy Land-Use Land-Cover Maps

• URL: http://arxiv.org/abs/2412.12552v1
• Date: Tue, 17 Dec 2024 05:23:00 GMT
• Title: SAModified: A Foundation Model-Based Zero-Shot Approach for Refining Noisy Land-Use Land-Cover Maps
• Authors: Sparsh Pekhale, Rakshith Sathish, Sathisha Basavaraju, Divya Sharma,
• Abstract summary: Land-use and land cover (LULC) analysis is critical in remote sensing. automating LULC map generation using machine learning is rendered challenging due to noisy labels. We propose a zero-shot approach using the foundation model, Segment Anything Model (SAM) We achieve a significant reduction in label noise and an improvement in the performance of the downstream segmentation model by $approx 5%$ when trained with denoised labels.
• Score: 2.374912052693646
• License:
• Abstract: Land-use and land cover (LULC) analysis is critical in remote sensing, with wide-ranging applications across diverse fields such as agriculture, utilities, and urban planning. However, automating LULC map generation using machine learning is rendered challenging due to noisy labels. Typically, the ground truths (e.g. ESRI LULC, MapBioMass) have noisy labels that hamper the model's ability to learn to accurately classify the pixels. Further, these erroneous labels can significantly distort the performance metrics of a model, leading to misleading evaluations. Traditionally, the ambiguous labels are rectified using unsupervised algorithms. These algorithms struggle not only with scalability but also with generalization across different geographies. To overcome these challenges, we propose a zero-shot approach using the foundation model, Segment Anything Model (SAM), to automatically delineate different land parcels/regions and leverage them to relabel the unsure pixels by using the local label statistics within each detected region. We achieve a significant reduction in label noise and an improvement in the performance of the downstream segmentation model by $\approx 5\%$ when trained with denoised labels.

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