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Found 6 papers, 2 papers with code
Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior
While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management.
Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data
To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread.
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Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information
This method is applied to a case study using the Tennessee Eastman process data set of a chemical plant, and we discuss implications of our findings for operation, distribution, and decision-making of sensors in plant emergency and resilience scenarios.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
Correction Networks: Meta-Learning for Zero-Shot Learning
We propose a model that learns to perform zero-shot classification using a meta-learner that is trained to produce a correction to the output of a previously trained learner.
Image Segmentation to Distinguish Between Overlapping Human Chromosomes
We apply neural network-based image segmentation to the problem of distinguishing between partially overlapping DNA chromosomes.
