Introduction:
Over the past year, we have witnessed long and challenging battles against record-breaking wildfires worldwide. The wildfire spread over vast areas around the world. Some of these fires burned thousands of acres of land and destroyed hundreds of homes and buildings. Poor air quality developed due to the dense smoke, which raised health concerns for people near the fire and at distances hundreds of kilometers away. The proposed project develops a deep-learning neural network system to detect fire outbreaks quickly.
Objectives
- As a starting point, a dataset of satellite images is available from NASA and classified as wildfire and non-wildfire. The dataset includes 1126 labeled images. The dataset is divided into two subsets: training 60%, validation 20% and 20% testing
- Train a Convolutional Neural Network (CNN) model to predict whether an input picture indicates a wildfire. Different NN structures will be tested to find the best model.
- You may use the transfer learning technique to minimize the computational cost model complexity.
- The ultimate goal is to develop a system and scenario that can be used to detect, predict, and alert the authorities of wildfires.
Conclusion:
Through the utilization of deep learning techniques, this initial study aims to contribute to the early identification and mitigation of wildfires. The project presents an opportunity to explore cutting-edge machine-learning technologies while tackling the escalating challenge posed by wildfires.
Undergraduate
1. A trained deep neural network model that can detect wildfire outbreaks in images.
2. Evaluation insights into the model's performance for further improvement.
3. Documentation of the project's methodologies, discoveries, and recommendations for research and development.
Studnents who took courses in AI such as COMPSYS 306 or similar. Interview with the supervisor is mandatory before submiting the application.
Signal Processin (405.722, Lab)