# A transformer boosted UNet for smoke segmentation in complex backgrounds in multispectral LandSat imagery

arXiv: https://arxiv.org/abs/2406.13105 · HTML: https://mlsanity.com/paper/2406.13105/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Multimodal Wildland Fire Smoke Detection](https://mlsanity.com/paper/2212.14143/) (2022)
- [Learning Class-Specific Spectral Patterns to Improve Deep Learning Based Scene-Level Fire Smoke Detection from Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2310.01711/) (2023)
- [Wildfire Smoke Detection System: Model Architecture, Training Mechanism, and Dataset](https://mlsanity.com/paper/2311.10116/) (2023)

## Built on by (most cited first, 3 of 3)

- [SmokeBench: Evaluating Multimodal Large Language Models for Wildfire Smoke Detection](https://mlsanity.com/paper/2512.11215/) (2025)
- [TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock](https://mlsanity.com/paper/2604.09648/) (2026)
- [WISE: A Lightweight, Weakly-Supervised Model for Onboard Fire Smoke Detection and Localization](https://mlsanity.com/paper/2609.18227/) (2026)

## Abstract (arXiv)

Many studies have been done to detect smokes from satellite imagery. However, these prior methods are not still effective in detecting various smokes in complex backgrounds. Smokes present challenges in detection due to variations in density, color, lighting, and backgrounds such as clouds, haze, and/or mist, as well as the contextual nature of thin smoke. This paper addresses these challenges by proposing a new segmentation model called VTrUNet which consists of a virtual band construction module to capture spectral patterns and a transformer boosted UNet to capture long range contextual features. The model takes imagery of six bands: red, green, blue, near infrared, and two shortwave infrared bands as input. To show the advantages of the proposed model, the paper presents extensive results for various possible model architectures improving UNet and draws interesting conclusions including that adding more modules to a model does not always lead to a better performance. The paper also compares the proposed model with very recently proposed and related models for smoke segmentation and shows that the proposed model performs the best and makes significant improvements on prediction performances