Fire Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

18 shown of 18 papers with code (61 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• FireNet: A Specialized Lightweight Fire & Smoke Detection Model for Real-Time IoT Applications
  28 May 2019 4 repositories listed
  Fire disasters typically result in lot of loss to life and property.
• Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection
  17 Oct 2020 3 repositories listed
  Automatic visual fire detection is used to complement traditional fire detection sensor systems (smoke/heat).
• BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
  10 Jun 2015 3 repositories listed
  Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method.
• ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation
  15 Nov 2022 2 repositories listed
  In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas.
• Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection
  10 Jun 2021 2 repositories listed
  We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings.
• Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study
  9 Jan 2021 2 repositories listed
  Active fire detection in satellite imagery is of critical importance to the management of environmental conservation policies, supporting decision-making and law enforcement.
• Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset
  28 Dec 2020 2 repositories listed
  FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop…
• Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System
  1 Mar 2020 2 repositories listed
  This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images.
• MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire Detection
  15 Jul 2025 1 repository listed
  Smoke is the first visible indicator of a wildfire.With the advancement of deep learning, image-based smoke detection has become a crucial method for detecting and preventing forest fires.
• Comparing Next-Day Wildfire Predictability of MODIS and VIIRS Satellite Data
  11 Mar 2025 1 repository listed
  Studies have used one or the other, but there has been no comparison between them to determine which might be more suitable for next-day fire prediction.
• ForestProtector: An IoT Architecture Integrating Machine Vision and Deep Reinforcement Learning for Efficient Wildfire Monitoring
  17 Jan 2025 1 repository listed
  Early detection of forest fires is crucial to minimizing the environmental and socioeconomic damage they cause.
• TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction
  16 Dec 2024 1 repository listed
  We present a comprehensive multi-temporal remote sensing dataset for active fire detection, daily wildfire monitoring, and next-day wildfire prediction.
• Indoor Fire and Smoke Detection Using Soft-Voting Based Deep Ensemble Model
  11 Jun 2024 1 repository listed
  Fire and smoke detection using vision-based technology plays a crucial role in terms of safety for indoor environments.
• SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification
  23 May 2024 1 repository listed
  In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification.
• FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion
  6 Mar 2024 1 repository listed Syntology ran 12 of 16 samples · 4 unverified · 16 pointer-only (licence)
  Specifically, the fusion of Perlin noise in this work significantly improved the quality of synthesized images.
• Modelling wildland fire burn severity in California using a spatial Super Learner approach
  25 Nov 2023 1 repository listed
  We develop a machine learning model to predict post-fire burn severity using pre-fire remotely sensed data.
• Rapid Deforestation and Burned Area Detection using Deep Multimodal Learning on Satellite Imagery
  10 Jul 2023 1 repository listed
  Our method successfully achieves high-precision deforestation estimation and burned area detection on unseen images from the region.
• Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation
  30 Jul 2021 1 repository listed
  However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.