Abstract
Detecting surface changes from satellite imagery is critical for rapid disaster response and environmental monitoring, yet remains challenging due to the complex interplay between atmospheric noise, seasonal variations, and sensor artifacts. Here we show that deep learning can leverage the temporal redundancy of satellite time series to detect anomalies at unprecedented sensitivity, by learning to predict what the surface should look like in the absence of change. We train an inpainting model built upon the SATLAS foundation model to reconstruct the last frame of a Sentinel-2 time series from preceding acquisitions, using globally distributed training data spanning diverse climate zones and land cover types. When applied to regions affected by sudden surface changes, the discrepancy between prediction and observation reveals anomalies that traditional change detection methods miss. We validate our approach on earthquake-triggered surface ruptures from the 2023 Turkey-Syria earthquake sequence, demonstrating detection of a rift feature in Tepehan with higher sensitivity and specificity than temporal median or Reed-Xiaoli anomaly detectors. Our method reaches detection thresholds approximately three times lower than baseline approaches, providing a path towards automated, global-scale monitoring of surface changes from freely available multi-spectral satellite data.
Abstract (translated)
从卫星图像中检测地表变化对于快速灾难响应和环境监测至关重要，但由于大气噪声、季节性变化以及传感器伪影之间的复杂相互作用，这一任务仍然具有挑战性。在这里，我们展示了深度学习可以利用卫星时间序列中的时间冗余来以前所未有的灵敏度检测异常情况，通过学习在没有地表变化的情况下表面应该呈现的样子来进行预测。我们使用全球分布的训练数据（涵盖不同的气候区域和土地覆盖类型）对SATLAS基础模型进行训练，以重建Sentinel-2时间序列的最后一帧图像，该图像基于之前的获取。 当应用于受突然地表变化影响的地区时，预测与观测之间的差异揭示了传统变化检测方法未能发现的异常情况。我们通过在2023年土耳其-叙利亚地震序列中由地震引发的地表破裂事件上验证我们的方法，并展示了比时间中位数或刘小莉异常检测器更高的灵敏度和特异性，在Tepehan地区发现了裂谷特征。 我们的方法能够达到大约是基线方法三倍的检测阈值，这为从免费提供的多光谱卫星数据进行自动化、全球规模的地表变化监测提供了一条路径。
URL
https://arxiv.org/abs/2512.23986
