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Title: Wildfire Mapping in Interior Alaska Using Deep Neural Networks on Imbalanced Datasets

Abstract

Wildfires are the dominant disturbance impacting many regions in Alaska and is expected to intensify due to climate change. Accurate tracking and quantification of wildfires are important for climate modeling and ecological studies in this region. Remote sensing platforms (e.g., MODIS, Landsat) are an important tool for mapping wildfires in Alaska at varying spatial and temporal scales. Deep neural networks (DNN) have shown superior performance in many classification problems such as high-dimensional remote sensing data. Detection of wildfires is an imbalanced classification problem where one class containsa much smaller or larger sample size and DNNs performance can decline. We take a known weight-selection strategy during DNN training and apply them to MODIS variables (e.g., NDVI, surface reflectance) for a binary classification (i.e., wildfire or no-wildfire) across Alaska during the 2004 wildfire year, which is one of the largest on record. The method splits the input training data into sets, one for training the DNN to update weights and the other that splits into a validation set that is equally balanced between the wildfire and no-wildfire class. The performance is monitored on the validation set to select the weights based on the best validation-loss score. This approach was applied to twomore » sampled datasets, such as where the no-wildfire class can significantly outweigh the wildfire class. The normal DNN training strategy was unable to map wildfires for the highly imbalanced dataset, however, the weight-selection strategy was able to map wildfires very accurately (0.96 recall score for 78,702 wildfire pixels (500×500 m)).« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1491321
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Data Mining Workshops - Singapore, , Singapore - 11/17/2018 10:00:00 AM-11/20/2018 10:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Langford, Zachary L., Kumar, Jitendra, and Hoffman, Forrest M. Wildfire Mapping in Interior Alaska Using Deep Neural Networks on Imbalanced Datasets. United States: N. p., 2018. Web.
Langford, Zachary L., Kumar, Jitendra, & Hoffman, Forrest M. Wildfire Mapping in Interior Alaska Using Deep Neural Networks on Imbalanced Datasets. United States.
Langford, Zachary L., Kumar, Jitendra, and Hoffman, Forrest M. Thu . "Wildfire Mapping in Interior Alaska Using Deep Neural Networks on Imbalanced Datasets". United States. https://www.osti.gov/servlets/purl/1491321.
@article{osti_1491321,
title = {Wildfire Mapping in Interior Alaska Using Deep Neural Networks on Imbalanced Datasets},
author = {Langford, Zachary L. and Kumar, Jitendra and Hoffman, Forrest M.},
abstractNote = {Wildfires are the dominant disturbance impacting many regions in Alaska and is expected to intensify due to climate change. Accurate tracking and quantification of wildfires are important for climate modeling and ecological studies in this region. Remote sensing platforms (e.g., MODIS, Landsat) are an important tool for mapping wildfires in Alaska at varying spatial and temporal scales. Deep neural networks (DNN) have shown superior performance in many classification problems such as high-dimensional remote sensing data. Detection of wildfires is an imbalanced classification problem where one class containsa much smaller or larger sample size and DNNs performance can decline. We take a known weight-selection strategy during DNN training and apply them to MODIS variables (e.g., NDVI, surface reflectance) for a binary classification (i.e., wildfire or no-wildfire) across Alaska during the 2004 wildfire year, which is one of the largest on record. The method splits the input training data into sets, one for training the DNN to update weights and the other that splits into a validation set that is equally balanced between the wildfire and no-wildfire class. The performance is monitored on the validation set to select the weights based on the best validation-loss score. This approach was applied to two sampled datasets, such as where the no-wildfire class can significantly outweigh the wildfire class. The normal DNN training strategy was unable to map wildfires for the highly imbalanced dataset, however, the weight-selection strategy was able to map wildfires very accurately (0.96 recall score for 78,702 wildfire pixels (500×500 m)).},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

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