Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 04, March 2021

A Deep Learning Approach to Nightfire Detection based on Low-Light Satellite


Yue Wang, Ye Ni, Xutao Li and Yunming Ye, Harbin Institute of Technology, China


Wildfires are a serious disaster, which often cause severe damages to forests and plants. Without an early detection and suitable control action, a small wildfire could grow into a big and serious one. The problem is especially fatal at night, as firefighters in general miss the chance to detect the wildfires in the very first few hours. Low-light satellites, which take pictures at night, offer an opportunity to detect night fire timely. However, previous studies identify night fires based on threshold methods or conventional machine learning approaches, which are not robust and accurate enough. In this paper, we develop a new deep learning approach, which determines night fire locations by a pixel-level classification on low-light remote sensing image. Experimental results on VIIRS data demonstrate the superiority and effectiveness of the proposed method, which outperforms conventional threshold and machine learning approaches.


Night fire detection, pixel segmentation, low-light satellite image.