Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
[HTML][HTML] Fire Detection with Deep Learning: A Comprehensive Review
RN Vasconcelos, WJS Franca Rocha, DP Costa… - Land, 2024 - mdpi.com
Wildfires are a critical driver of landscape transformation on Earth, representing a dynamic
and ephemeral process that poses challenges for accurate early detection. To address this …
and ephemeral process that poses challenges for accurate early detection. To address this …
Live fuel moisture content map** in the Mediterranean Basin using random forests and combining MODIS spectral and thermal data
A Cunill Camprubi, P González-Moreno… - Remote Sensing, 2022 - mdpi.com
Remotely sensed vegetation indices have been widely used to estimate live fuel moisture
content (LFMC). However, marked differences in vegetation structure affect the relationship …
content (LFMC). However, marked differences in vegetation structure affect the relationship …
A cross-resolution transfer learning approach for soil moisture retrieval from Sentinel-1 using limited training samples
Abstract Synthetic Aperture Radar (SAR) data is increasingly popular as a data source for
global near-surface soil moisture map**, but large-scale applications are still challenging …
global near-surface soil moisture map**, but large-scale applications are still challenging …
Improving wildfire occurrence modelling by integrating time-series features of weather and fuel moisture content
Wildfire occurrence is a non-linear process resulting from interactions between weather,
topography, fuel, and anthropogenic factors amongst others. Modelling the probability of …
topography, fuel, and anthropogenic factors amongst others. Modelling the probability of …
Model-driven estimation of closed and open shrublands live fuel moisture content
Live fuel moisture content (LFMC) is a crucial variable affecting the ignition potential of
shrublands. Different remote sensing-based models (either empirical or physical) have been …
shrublands. Different remote sensing-based models (either empirical or physical) have been …
Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth
The moisture content of vegetation canopies controls various ecosystem processes such as
plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here …
plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here …
Retrieval of live fuel moisture content based on multi-source remote sensing data and ensemble deep learning model
J **e, T Qi, W Hu, H Huang, B Chen, J Zhang - Remote Sensing, 2022 - mdpi.com
Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and
fire spread rate. In order to further improve the retrieval accuracy, two ensemble models …
fire spread rate. In order to further improve the retrieval accuracy, two ensemble models …
[HTML][HTML] Vegetation fuel characterization using machine learning approach over southern Portugal
FLM Santos, FT Couto, SS Dias… - Remote Sensing …, 2023 - Elsevier
Understanding the role of fire in the water and carbon cycles is crucial for understanding the
Earth's system. Remote sensing is a valuable tool for this purpose as it covers large areas …
Earth's system. Remote sensing is a valuable tool for this purpose as it covers large areas …
Projecting live fuel moisture content via deep learning
Background Live fuel moisture content (LFMC) is a key environmental indicator used to
monitor for high wildfire risk conditions. Many statistical models have been proposed to …
monitor for high wildfire risk conditions. Many statistical models have been proposed to …