An integrated pipeline uses CNN-based detection with sensor fusion, Bayesian statistics for flyover updates, and reconfigurable satellite scheduling to enhance wildfire monitoring in simulations based on real locations.
Image and Vision Computing107, 104117 (2021)
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
ED-CCF projects detections into a quad-state error taxonomy and applies class-conditional calibration only when empirically justified, raising mAP50 for a hard class by 22.4% on a 600-image benchmark while keeping global mAP50 stable.
citing papers explorer
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Automating the Wildfire Detection and Scheduling Pipeline with Maneuverable Earth Observation Satellites
An integrated pipeline uses CNN-based detection with sensor fusion, Bayesian statistics for flyover updates, and reconfigurable satellite scheduling to enhance wildfire monitoring in simulations based on real locations.
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Error-Decomposed Class-Conditional Fusion for Statistically Guaranteed Hard-Category Robust Perception
ED-CCF projects detections into a quad-state error taxonomy and applies class-conditional calibration only when empirically justified, raising mAP50 for a hard class by 22.4% on a 600-image benchmark while keeping global mAP50 stable.