A Multimodal Data Fusion and Deep Learning Framework for Large-Scale Wildfire Surface Fuel Mapping

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on...

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Vydáno v:	Fire (Basel, Switzerland) Ročník 6; číslo 2; s. 36
Hlavní autoři:	Alipour, Mohamad, La Puma, Inga, Picotte, Joshua, Shamsaei, Kasra, Rowell, Eric, Watts, Adam, Kosovic, Branko, Ebrahimian, Hamed, Taciroglu, Ertugrul
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.01.2023
Témata:	Accuracy Active learning artificial intelligence Artificial neural networks California Classification climate Climate change Data integration Decision making Deep learning Feature extraction Forest & brush fires Forest fires forests fuel mapping Fuels Geospatial data High resolution Identification Image classification Image resolution Information processing Land cover Land management landscapes Machine learning Mapping Mental task performance Multilayers Neural networks prediction Quality control Remote sensing Simulation Vegetation Visual system Wildfires wildland fire United States United States > US California
ISSN:	2571-6255, 2571-6255
On-line přístup:	Získat plný text
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