Using U-Net Model to Detect Wildfire from Satellite Imagery
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Updated
Feb 25, 2024 - HTML
Using U-Net Model to Detect Wildfire from Satellite Imagery
Computer vision library for wildfire detection 🌲 Deep learning models in PyTorch & ONNX for inference on edge devices (e.g. Raspberry Pi)
A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach
Simulation of wildfire using cellular automaton and used mpi4py to parallel the program. Final Project for High Performance Computing and Parallel Computing Spring 2018@GWU
An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations using Multi Independent Agent Reinforcement Learning
Primary code repository for WFNEWS 2.0
A personal trial to understand and simulate forest wildfire spreading from satellite data using Deep Learning (a model with a ConvLSTM layer).
caliver: CALIbration and VERification of gridded fire danger models
visualize active wildfire activities in ArcGIS's firefly style
opensearch related code
Texting service to receive current air quality conditions and maps, powered by AirNow, Twilio, and AWS
Python wrappers for accessing Forest Observatory data via the Salo API
Wildfire Predictive Services to support decision making in prevention, preparedness, response and recovery
Predicting Fuel Load from earth observation data using Machine Learning
Wildfire detection on edge devices
Wildfire Modeling in Yosemite National Park
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