Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data

2025-10-10

AI Summary

Addressing severe smoke/cloud confusion, insufficient real-time capability, and scarce annotated data in wildfire monitoring, this paper proposes a self-supervised deep learning framework that fuses hourly radiometric observations from the GOES-18 and TEMPO geostationary satellites. Without requiring manual annotations, the method leverages temporal consistency modeling and multi-source data协同 reconstruction to achieve pixel-level discrimination and dynamic segmentation of smoke, fire pixels, and clouds. The generated smoke and fire masks exhibit high spatiotemporal coherence and demonstrate strong agreement with multi-source ground and satellite observations across multiple real wildfire events in the western United States. Compared to operational products, the approach achieves significantly improved detection accuracy and reduces response latency to the hourly scale. This work establishes a scalable, annotation-light paradigm for near-real-time wildfire spread tracking and air quality assessment.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This work demonstrates the possibilities for improving wildfire and air quality management in the western United States by leveraging the unprecedented hourly data from NASA's TEMPO satellite mission and advances in self-supervised deep learning. Here we demonstrate the efficacy of deep learning for mapping the near real-time hourly spread of wildfire fronts and smoke plumes using an innovative self-supervised deep learning-system: successfully distinguishing smoke plumes from clouds using GOES-18 and TEMPO data, strong agreement across the smoke and fire masks generated from different sensing modalities as well as significant improvement over operational products for the same cases.

Problem

• Improving wildfire monitoring using self-supervised deep learning
• Distinguishing smoke plumes from clouds with satellite data
• Advancing air quality management through real-time fire detection

Innovation

• Self-supervised deep learning for wildfire monitoring
• Combining GOES and TEMPO satellite radiance data
• Improved smoke and fire front masking techniques