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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Veröffentlicht in:	Fire (Basel, Switzerland) Jg. 6; H. 2; S. 36
Hauptverfasser:	Alipour, Mohamad, La Puma, Inga, Picotte, Joshua, Shamsaei, Kasra, Rowell, Eric, Watts, Adam, Kosovic, Branko, Ebrahimian, Hamed, Taciroglu, Ertugrul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2023
Schlagworte:	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
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Abstract	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 surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.
AbstractList	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 surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.
Audience	Academic
Author	Kosovic, Branko Watts, Adam Alipour, Mohamad Shamsaei, Kasra La Puma, Inga Rowell, Eric Picotte, Joshua Taciroglu, Ertugrul Ebrahimian, Hamed
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Snippet	Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have...
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SubjectTerms	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
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Title	A Multimodal Data Fusion and Deep Learning Framework for Large-Scale Wildfire Surface Fuel Mapping
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