Anomaly Detection in Satellite Videos Using Diffusion Models

Detecting anomalies in videos is a fundamental challenge in machine learning, particularly for applications like disaster management. Leveraging satellite data, with its high frequency and wide coverage, proves invaluable for promptly identifying extreme events such as wildfires, cyclones, or floods. Geostationary satellites, providing data streams at frequent intervals, effectively create a continuous video feed of Earth from space. This study focuses on detecting anomalies, specifically wildfires and smoke, in these high-frequency satellite videos. In contrast to prior endeavors in anomaly detection within surveillance videos, this study introduces a system tailored for high-frequency satellite videos, placing particular emphasis on two anomalies. Unlike the majority of existing Convolution Neural Network-based methods for wildfire detection that rely on labeled images or videos, our unsupervised approach addresses the challenges posed by high-frequency satellite videos with a high intensity of clouds. These Convolution Neural Network-based methods can only identify fires once they have reached a certain size and are susceptible to false positives. We frame the challenge of wildfire detection as a general anomaly detection problem. Introducing an innovative unsupervised approach involving diffusion models, which are state-of-the-art generative models for anomaly detection in satellite videos, we adopt a “generating-to-detecting” strategy. Performance evaluation, measured through AUC-ROC, underscores the superior efficacy of the diffusion model over CNN and Generative Adversarial Networks-based methods in detecting anomalies in these high-frequency satellite videos characterized by a high intensity of clouds. The dataset utilized can be accessed at this location.