A Fire Event Tracker (FET) algorithm performs spatio-temporal clustering on MTG-FCI active fire detections to enable consistent near-real-time and retrospective fire event monitoring.
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2 Pith papers cite this work, alongside 227 external citations. Polarity classification is still indexing.
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Multi-head attention neural network predicts grapevine leaf reflectance from 16 traits with average R² 0.84 and NRMSE 1.52%, showing lower MAE than PROSPECT-PRO especially in NIR and SWIR.
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Leveraging MTG-FCI fire observations for event-based fire behavior monitoring from near-real-time operation to seasonal analysis
A Fire Event Tracker (FET) algorithm performs spatio-temporal clustering on MTG-FCI active fire detections to enable consistent near-real-time and retrospective fire event monitoring.
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Leaf Spectral Reflectance Prediction Using Multi-Head Attention Neural Networks
Multi-head attention neural network predicts grapevine leaf reflectance from 16 traits with average R² 0.84 and NRMSE 1.52%, showing lower MAE than PROSPECT-PRO especially in NIR and SWIR.