An Efficient Wildfire Detection System for AI-Embedded Applications Using Satellite Imagery

Fire

article

Sanaa S. Al Samahi¹  Rebecca D. Parker³  Joshua M. Cutler³  Rhode V. Gachette³  Bahaa I. Ansaf³  George L. James³  Ryeim B. Ansaf³ 

[1] Al-Khwarizmi College of Engineering, University of Baghdad;EECS Department, University of Missouri;School of Engineering, Colorado State University Pueblo;Industrial Engineering Technology Department, University of Southern Mississippi;Department of Biology, Colorado State University Pueblo

关键词: wildfire;    remote sensing;    machine learning;    satellite imagery;    convolutional neural network;   

DOI  :  10.3390/fire6040169

学科分类：环境科学（综合）

来源: mdpi

【 摘 要 】

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (MobileNet) was trained to identify key features of various satellite images that contained fire or without fire. Then, the trained system is used to classify new satellite imagery and sort them into fire or no fire classes. A cloud-based development studio from Edge Impulse Inc. is used to create a NN model based on the transferred learning algorithm. The effects of four hyperparameters are assessed: input image resolution, depth multiplier, number of neurons in the dense layer, and dropout rate. The computational cost is evaluated based on the simulation of deploying the neural network model on an Arduino Nano 33 BLE device, including Flash usage, peak random access memory (RAM) usage, and network inference time. Results supported that the dropout rate only affects network prediction performance; however, the number of neurons in the dense layer had limited effects on performance and computational cost. Additionally, hyperparameters such as image size and network depth significantly impact the network model performance and the computational cost. According to the developed benchmark network analysis, the network model MobileNetV2, with 160 × 160 pixels image size and 50% depth reduction, shows a good classification accuracy and is about 70% computationally lighter than a full-depth network. Therefore, the proposed methodology can effectively design an ML application that instantly and efficiently analyses imagery from a spacecraft/weather balloon for the detection of wildfires without the need of an earth control centre.

【 授权许可】

CC BY