Fire-Net: A Deep Learning Framework for Active Forest Fire Detection

Abstract

Forest conservation is crucial for the maintenance of a healthy and thriving ecosystem. The field of remote sensing (RS) has been integral with the wide adoption of computer vision and sensor technologies for forest land observation. One critical area of interest is the detection of active forest fires. A forest fire, which occurs naturally or manually induced, can quickly sweep through vast amounts of land, leaving behind unfathomable damage and loss of lives. Automatic detection of active forest fires (and burning biomass) is hence an important area to pursue to avoid unwanted catastrophes. Early fire detection can also be useful for decision makers to plan mitigation strategies as well as extinguishing efforts. In this paper, we present a deep learning framework called Fire-Net, that is trained on Landsat-8 imagery for the detection of active fires and burning biomass. Specifically, we fuse the optical (Red, Green, and Blue) and thermal modalities from the images for a more effective representation. In addition, our network leverages the residual convolution and separable convolution blocks, enabling deeper features from coarse datasets to be extracted. Experimental results show an overall accuracy of 97.35%, while also being able to robustly detect small active fires. The imagery for this study is taken from Australian and North American forests regions, the Amazon rainforest, Central Africa and Chernobyl (Ukraine), where forest fires are actively reported.

Object Tracking Based on Satellite Videos: A Literature Review

“…Similarly, Ref. [126] fused the optical and thermal modalities from the Landsat-8 images for a more effective fire representation. The proposed CNN model combined the residual convolution and separable convolution blocks to enable deeper features of the tracking target.…”

“…In addition, Figure 4 shows a comparison of algorithm structure between two categories. [110] 2021 GAN with deep multi-scale frame prediction method [111] 2022 GAN to predict both the track and intensity of typhoons RNN-based [112] 2017 A convolutional sequence-to-sequence autoencoder [113] 2018 MNNs for typhoon tracking [114] 2018 A CLSTM based model [115] 2021 A CLSTM layer with FCLs [116] 2022 A CLSTM with 3D CNN based on multimodal data [117] 2022 An echo state network-based tracking Fire Traditional [118] 2017 Identify possible fire hotspots from two bands of AHI [119] 2018 A threshold algorithm with visual interpretation [120] 2019 A multi-temporal method of temperature estimation [121] 2020 Temperature dynamics by data assimilation [122] 2022 Wildfire tracking via visible and infrared image series DL-based [123] 2019 3D CNN to capture spatial and spectral patterns [124] 2019 Inception-v3 model with transfer learning [125] 2021 Near-real-time fire smoking prediction [126] 2022 Combine the residual convolution and separable convolution to detect fire [127] 2022 Multiple Kernel learning for various size fire detections…”

Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset

“…For instance, [52] and [7] take advantage of U-net, which is one of the most famous segmentation frameworks. The works [53] and [54] design a network that learns from both optical and thermal domains and performs the flame segmentation. These two works also refer to a topic known as Multi-modality Learning.…”

Early Detection of Forest Fire Using Mixed Learning Techniques and UAV

“…Continuous monitoring of the potential areas that might be prone to fire should be monitored. In this work [22][23][24][25], the design of UAVs has been done based on the advantages of AI. Furthermore, the onboard processing abilities have also been equipped.…”