The Wildfire Detection project is a machine learning application developed in Python using the Tensorflow library. The goal of the project was to explore ways to quick detection of wildfires when they break out.
Developed in collaboration with Daniel Bernabe and Christopher Grime.
The Wildfire Early Detection project looks for smoke in images and sends a notification out when smoke is detected. The goal of an early detection project like this is to quickly identify the breakout of wildfires allowing a headstart on mitigating the destruction of the fire.
Our first step was to train an image classifier to recognize smoke. To accomplish this, we used a Convolutional Neural Network to develop the smoke classification model. The dataset used to train the model used a modified version of the aiformankind gridded dataset.
The script for detecting smoke uses the image classifier model for detection. It uses two scripts that were adapted from pyimagesearch to split up large images from a camera and detect smoke in smaller chunks.
The project allowed us to practice software development skills and develop a solution to a real-world problem. It was an excellent opportunity for an introduction to machine learning.