Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent ef-forts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accu-rate automation in detecting such natural anomalies and en-able decision-makers to take actions in a timely manner. Re-cent advances in machine learning algorithms, namely prob-abilistic generative methods, allow researchers and decision- makers to step beyond detection and study “what-if’ scenar-ios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these proba-bilistic generative models is a computationally expensive pro-cess, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research in-vestigating the potential for improved machine learning meth-ods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-to- image segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capa-ble of running “what-if’ scenarios. Our proposed model is compatible with training on quantum computers, which re-sults in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.