1,255 research outputs found
SciTech News Volume 71, No. 1 (2017)
Columns and Reports From the Editor 3
Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11
Reviews Sci-Tech Book News Reviews 12
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A review of machine learning applications in wildfire science and management
Artificial intelligence has been applied in wildfire science and management
since the 1990s, with early applications including neural networks and expert
systems. Since then the field has rapidly progressed congruently with the wide
adoption of machine learning (ML) in the environmental sciences. Here, we
present a scoping review of ML in wildfire science and management. Our
objective is to improve awareness of ML among wildfire scientists and managers,
as well as illustrate the challenging range of problems in wildfire science
available to data scientists. We first present an overview of popular ML
approaches used in wildfire science to date, and then review their use in
wildfire science within six problem domains: 1) fuels characterization, fire
detection, and mapping; 2) fire weather and climate change; 3) fire occurrence,
susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6)
fire management. We also discuss the advantages and limitations of various ML
approaches and identify opportunities for future advances in wildfire science
and management within a data science context. We identified 298 relevant
publications, where the most frequently used ML methods included random
forests, MaxEnt, artificial neural networks, decision trees, support vector
machines, and genetic algorithms. There exists opportunities to apply more
current ML methods (e.g., deep learning and agent based learning) in wildfire
science. However, despite the ability of ML models to learn on their own,
expertise in wildfire science is necessary to ensure realistic modelling of
fire processes across multiple scales, while the complexity of some ML methods
requires sophisticated knowledge for their application. Finally, we stress that
the wildfire research and management community plays an active role in
providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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