Large-Scale Avalanche Mapping from SAR Images with Deep Learning-based Change Detection

Abstract

Accurate change detection from satellite imagery is essential for monitoring rapid mass-movement hazards such as snow avalanches, which increasingly threaten human life, infrastructure, and ecosystems due to their rising frequency and intensity. This study presents a systematic investigation of large-scale avalanche mapping through bi-temporal change detection using Sentinel-1 synthetic aperture radar (SAR) imagery. Extensive experiments across multiple alpine ecoregions with manually validated avalanche inventories show that treating the task as a unimodal change detection problem, relying solely on pre- and post-event SAR images, achieves the most consistent performance. The proposed end-to-end pipeline achieves an F1-score of 0.8061 in a conservative (F1-optimized) configuration and attains an F2-score of 0.8414 with 80.36% avalanche-polygon hit rate under a less conservative, recall-oriented (F2-optimized) tuning. These results highlight the trade-off between precision and completeness and demonstrate how threshold adjustment can improve the detection of smaller or marginal avalanches. The release of the annotated multi-region dataset establishes a reproducible benchmark for SAR-based avalanche mapping.

Figures (7)

• Figure 1: Overview of the proposed avalanche mapping workflow. SAR images (VH and VV) are preprocessed (not shown). In the training stage (left), patches and annotations are used to fit a change detection model, followed by validation and threshold tuning. During inference (right), patches are extracted from pre- and post-event SAR images and passed through the model to obtain avalanche probability maps, which are then blended into a continuous map and thresholded to produce the final binary avalanche map.
• Figure 2: Geographic location of the study areas used in this work: Nuuk (a), Tromsø (b), Livigno (c), and Pish (d).
• Figure 3: Example of a $128 \times 128$ input patch used for avalanche mapping. From left to right, the panels show the four SAR backscatter channels, the resampled local incidence angle (LIA) and terrain slope, and the ground-truth segmentation mask. SAR channels are independently normalized using a $2$--$98\%$ percentile stretch. LIA and slope are visualized using the viridis and terrain colormaps, respectively.
• Figure 4: 3D schematic of the proposed architecture. Pre- and post-event SAR images are processed by weight-sharing Swin Transformer encoders to extract temporally aligned features. Auxiliary topographic inputs (slope and local incidence angle) are processed by a separate Swin encoder. The element-wise difference between the deepest pre- and post-event SAR feature maps is fused with topographic features via a residual convolutional module and decoded by a hierarchical Swin Transformer, which progressively upsamples and refines the representation to produce a binary avalanche segmentation mask. This design enables joint reasoning over spatial, temporal, and topographic cues.
• Figure 5: Visual results for selected test patches. From left to right: VV RGB composite (pre-post-pre), VH RGB composite, predicted probability map, thresholded binary mask, and ground truth mask.