# TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting

arXiv: https://arxiv.org/abs/2306.10940 · HTML: https://mlsanity.com/paper/2306.10940/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 8 of 8)

- [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)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [Earthformer: Exploring Space-Time Transformers for Earth System Forecasting](https://mlsanity.com/paper/2207.05833/) (2022)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Deep Learning for Global Wildfire Forecasting](https://mlsanity.com/paper/2211.00534/) (2022)

## Built on by (most cited first, 11 of 11)

- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Causal Graph Neural Networks for Wildfire Danger Prediction](https://mlsanity.com/paper/2403.08414/) (2024)
- [FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction](https://mlsanity.com/paper/2502.01550/) (2025)
- [SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction](https://mlsanity.com/paper/2308.03586/) (2023)
- [SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics](https://mlsanity.com/paper/2312.07199/) (2023)
- [Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting](https://mlsanity.com/paper/2509.25017/) (2025)
- [From Black Box to Insight: Explainable AI for Extreme Event Preparedness](https://mlsanity.com/paper/2511.13712/) (2025)
- [Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks](https://mlsanity.com/paper/2404.06437/) (2024)
- [TeleViT1.0: Teleconnection-aware Vision Transformers for Subseasonal to Seasonal Wildfire Pattern Forecasts](https://mlsanity.com/paper/2512.00089/) (2025)
- [Efficient Subseasonal Weather Forecast using Teleconnection-informed Transformers](https://mlsanity.com/paper/2401.17870/) (2024)
- [CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities](https://mlsanity.com/paper/2506.08690/) (2025)

## Abstract (arXiv)

Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires weeks and months in advance to plan forest fuel management, resource procurement and allocation. To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system's inherent spatio-temporal interactions, such as memory effects and teleconnections. We propose a teleconnection-driven vision transformer (TeleViT), capable of treating the Earth as one interconnected system, integrating fine-grained local-scale inputs with global-scale inputs, such as climate indices and coarse-grained global variables. Through comprehensive experimentation, we demonstrate the superiority of TeleViT in accurately predicting global burned area patterns for various forecasting windows, up to four months in advance. The gain is especially pronounced in larger forecasting windows, demonstrating the improved ability of deep learning models that exploit teleconnections to capture Earth system dynamics. Code available at https://github.com/Orion-Ai-Lab/TeleViT.