Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking

Authors

• Anh Tran, University of Arkansas, Fayetteville
• Minh Tran, University of Arkansas, Fayetteville
• Esteban Marti, University of Arkansas, Fayetteville
• Jackson Cothren, Ctr Adv Spatial Technol,
• Chase Rainwater, University of Arkansas, Fayetteville
• Sandra Eksioglu, University of Arkansas, Fayetteville
• Ngan Le, University of Arkansas, FayettevilleFollow

Document Type

Article

Publication Date

8-11-2025

Abstract

Early and accurate wildfire detection is critical for minimizing environmental damage and ensuring a timely response. However, existing satellite-based wildfire datasets suffer from limitations such as coarse ground truth, poor spectral coverage, and class imbalance, which hinder progress in developing robust segmentation models. In this paper, we introduce Land8Fire, a new large-scale wildfire segmentation dataset composed of over 20,000 multispectral image patches derived from Landsat 8 and manually annotated for high-quality fire masks. Building on the ActiveFire dataset, Land8Fire improves ground truth reliability and offers predefined splits for consistent benchmarking. We evaluate a range of state-of-the-art convolutional and transformer-based models, including UNet, DeepLabV3+, SegFormer, and Mask2Former, and investigate the impact of different objective functions (cross-entropy and focal losses) and spectral band combinations (B1-B11). Our results reveal that focal loss, though effective for small object detection, underperforms in scenarios with clustered fires, leading to reduced recall. In contrast, spectral analysis highlights the critical role of short-wave infared 1 (SWIR1) and short-wave infared 2 (SWIR2) bands, with further gains observed when including near infrared (NIR) to penetrate smoke and cloud cover. Land8Fire sets a new benchmark for wildfire segmentation and provides valuable insights for advancing fire detection research in remote sensing.

Comments

Web of Science

MDPI

Citation

Tran, A.; Tran, M.; Marti, E.; Cothren, J.; Rainwater, C.; Eksioglu, S.; Le, N. Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking. Remote Sens. 2025, 17, 2776. https://doi.org/10.3390/rs17162776

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Keywords

remote sensing, fire semantic segmentation, fire dataset, neural network, Landsat-8 imagery