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

Highlights: What are the main findings? The Segment Anything Model can be effectively adapted to Sentinel-1 Synthetic Aperture Radar composites for avalanche segmentation via lightweight domain adaptation modules. Prompt-engineering strategies and an encoder-efficient fine-tuning procedure improve robustness to imprecise prompts while keeping training practical. What are the implications of the main findings? Integrated into a semi-automatic annotation tool, the adapted model reduces expert workload and speeds up the creation of high-quality avalanche inventories. The proposed adaptation recipe provides a transferable path to bring segmentation foundation models beyond RGB and into remote sensing workflows. 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.