Title: Fire detection in nano-satellite imagery using Mask R-CNN

Authors: Aditi Jahagirdar; Neha Sathe; Sneh Thorat; Saloni Saxena

Addresses: School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, India ' School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, India ' School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, India ' School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, India

Abstract: Increasing availability of satellite imagery has made it possible to detect forest fires through satellite imagery. This research aims at investigating early forest detection approaches using deep learning and satellite image segmentation. The algorithms implemented in this work are mask region-based convolutional neural network (Mask R-CNN), UNet and deep residual U-NET (ResUNet). The experimentation is carried out on publically available satellite image data having challenges like the presence of clouds, snow, rivers and sand, which gets confused with the smoke from the fire. The methods implemented here can successfully distinguish between these natural entities and the smoke emitted from the fire. It is seen that Mask R-CNN has an IoU of 0.925, whereas UNet and Res-UNet have IoUs of 0.30 and 0.35, respectively. The results indicate that Mask RCNN is both more time effective and precise and can be used in forest fire detection systems.

Keywords: satellite images; image segmentation; Mask R-CNN; ResUNet; UNet; deep learning.

DOI: 10.1504/IJSISE.2024.139980

International Journal of Signal and Imaging Systems Engineering, 2024 Vol.13 No.1, pp.19 - 26

Received: 15 Feb 2023
Accepted: 09 Oct 2023
Published online: 15 Jul 2024 *

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