Unsupervised Change Detection Using Sentinel-1 and Sentinel-2 Imagery with the Clay Foundation Model: A Case Study of Flood-Affected Areas in Valencia Spain

The detection of environmental changes caused by natural disasters is critical for rapid response and effective management. In this study, we present a methodology for unsupervised change detection that leverges optical Sentinel-2 [1] and Synthetic Aperture Radar (SAR) Sentinel-1 [2] accessed through public SpatioTemporal Asset Catalogs (STAC) [3] along with Earth Observation (EO) foundation model, namely, Clay [4]. The analysis was conducted independently for each dataset to capitalize on the unique properties of these satellite sensors. Sentinel-1 offers robust surface texture sensitivity with its all-weather, day-and-night imaging capability, while Sentinel-2 provides detailed spectral and spatial information critical for vegetation and land-use analysis.

Clay foundation model, a large-scale pretrained Vision Transformer trained on EO data from various missions (Sentinel-1, Sentinel-2, Landsat, Planet, NAIP, LINZ, and MODIS), was used to extract spatially and spectrally rich embedding from Sentinel-1 and Sentinel-2 images. The model takes as an input the satellite imagery along with information about location and time and outputs mathematical representations of a given area at a certain time on Earth’s surface. The images were fed to the model as patches of size 256×256 along with the timestap of the scene, the spatial location and other metadata of the input image to estimate the embeddings that can be rearranged to be of size (1024×32×32). These embeddings were then analyzed using pixel-wise distance metrics to quantify changes between pre- and post-even imagery and the resulting distance image was then spatially interpolated to the size of the input image.

The approach was validated on satellite imagery of the Valencia region in Spain, an area significantly impacted by recent flooding on the 29th October 2024. For Sentinel-1, the method effectively highlighted surface water changes and structure affected by the floods in two scenes acquired on the 7th October and the 12th November 2024, while Sentinel-2 data captured variations in vegetation areas that was impacted by the floods using two scenes acquired on the 1st October and the 10 November 2024. By analyzing the datasets independently, this framework demonstrates the complementary insights offered by radar and optical imagery in assessing disaster impacts.

This study highlights the potential of leveraging open satellite data available via STAC catalogs and EO foundation models for unsupervised change detection in disaster monitoring, enabling rapid response without relying on specialized models tailored to specific regions. Unlike traditional approaches that require retraining for new areas due to geographical variability, this methodology is both scalable and adaptable, providing a generalizable framework for environmental monitoring, disaster response, and resilience planning. The results emphasize the value of integrating multi-sensor satellite imagery to enhance understanding of disaster impacts, facilitating more informed and timely decision-making.

References:

[1] https://earth-search.aws.element84.com/v1

[2] https://planetarycomputer.microsoft.com/api/stac/v1

[3] https://stacspec.org/en

[4] https://clay-foundation.github.io/model/index.html