A Bibliometric Analysis of Artificial Intelligence-Based Solutions to Challenges in Wildfire Fuel Mapping
Author:
Affiliation:
1. University of California,Civil & Environmental Engineering Dept.,Los Angeles,California,USA
2. University of Illinois,Civil & Environmental Engineering Dept.,Urbana Champaign,Illinois,USA
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282462.pdf?arnumber=10282462
Reference10 articles.
1. Dynamic Wildfire Fuel Mapping Using Sentinel – 2 and Prisma Hyperspectral Imagery
2. A Multimodal Data Fusion and Deep Learning Framework for Large-ScaleWildfire Surface Fuel Mapping;alipour;Fire,2023
3. An Uncertainty Analysis of Wildfire Modeling
4. Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model
5. Mapping wildland fuel for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling;b;Int J Wildland Fire,2001
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2. Estimation of Downed Woody Time-Lag Fuel Loadings with Multimodal Remote Sensing Data and Ensemble Machine Learning Regression Model;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
3. Dual-Frequency Radar Wave-Inversion for Sub-Surface Material Characterization;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
4. An Interdisciplinary Approach to Coordinated Data Collection for Wildland Fire Science: the Fire and Smoke Model Evaluation Experiment (FASMEE);IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
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