Promptable Foundation Models for SAR Remote Sensing: Adapting the Segment Anything Model for Snow Avalanche Segmentation

2026-01-03

Remote Sensing

AI Summary

Avalanche remote sensing segmentation relies heavily on large volumes of expert-annotated SAR imagery, which is both costly and inefficient. This work presents the first effective adaptation of the Segment Anything Model (SAM) to multi-channel Sentinel-1 SAR data, systematically addressing four key challenges: domain shift, input modality constraints, prompt robustness, and training efficiency. By integrating a domain adapter, a multi-channel encoder, tailored prompt engineering, and a parameter-efficient training strategy that freezes the original encoder, the proposed method successfully bridges the gap between natural-image pretraining and SAR-specific segmentation. When incorporated into an annotation tool, the approach significantly accelerates avalanche region labeling and demonstrates strong performance in challenging scenarios characterized by low contrast and small target sizes.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Remote sensing solutions for avalanche segmentation and mapping are key to supporting risk forecasting and mitigation in mountain regions. Synthetic Aperture Radar (SAR) imagery from Sentinel-1 can be effectively used for this task, but training an effective detection model requires gathering a large dataset with high-quality annotations from domain experts, which is prohibitively time-consuming. In this work, we aim to facilitate and accelerate the annotation of SAR images for avalanche mapping. We build on the Segment Anything Model (SAM), a segmentation foundation model trained on natural images, and tailor it to Sentinel-1 SAR data. Adapting SAM to our use case requires addressing several domain-specific challenges: (1) domain mismatch, since SAM was not trained on satellite or SAR imagery; (2) input adaptation, because SAR products typically provide more than three channels while the SAM is constrained to RGB images; (3) robustness to imprecise prompts that can affect target identification and degrade the segmentation quality, an issue exacerbated in small, low-contrast avalanches; and (4) training efficiency, since standard fine-tuning is computationally demanding for the SAM. We tackle these challenges through a combination of adapters to mitigate the domain gap, multiple encoders to handle multi-channel SAR inputs, prompt-engineering strategies to improve avalanche localization accuracy, and a training algorithm that limits the training time of the encoder, which is recognized as the major bottleneck. We integrate the resulting model into a segmentation tool and show experimentally that it speeds up the annotation of SAR images.

Problem

• snow avalanche segmentation
• SAR remote sensing
• annotation efficiency
• domain adaptation
• foundation models

Innovation

• Segment Anything Model
• Synthetic Aperture Radar
• domain adaptation
• prompt engineering
• multi-channel input