# Through-Foliage Surface-Temperature Reconstruction for Early Wildfire Detection

arXiv: https://arxiv.org/abs/2511.12572 · HTML: https://mlsanity.com/paper/2511.12572/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 115 references that are in the map.
- First posted on arXiv 16 Nov 2025.

## Builds on (most cited first, 24 of 24)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Finite Scalar Quantization: VQ-VAE Made Simple](https://mlsanity.com/paper/2309.15505/) (2023)
- [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://mlsanity.com/paper/2305.08891/) (2023)
- [Efficient Diffusion Training via Min-SNR Weighting Strategy](https://mlsanity.com/paper/2303.09556/) (2023)
- [MambaOut: Do We Really Need Mamba for Vision?](https://mlsanity.com/paper/2405.07992/) (2024)
- [VmambaIR: Visual State Space Model for Image Restoration](https://mlsanity.com/paper/2403.11423/) (2024)
- [Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset](https://mlsanity.com/paper/2012.14036/) (2020)
- [An Autonomous Drone for Search and Rescue in Forests using Airborne Optical Sectioning](https://mlsanity.com/paper/2105.04328/) (2021)
- [Search and Rescue with Airborne Optical Sectioning](https://mlsanity.com/paper/2009.08835/) (2020)
- [A Statistical View on Synthetic Aperture Imaging for Occlusion Removal](https://mlsanity.com/paper/1906.06600/) (2019)
- [Fast Automatic Visibility Optimization for Thermal Synthetic Aperture Visualization](https://mlsanity.com/paper/2005.04065/) (2020)
- [Pose Error Reduction for Focus Enhancement in Thermal Synthetic Aperture Visualization](https://mlsanity.com/paper/2012.08606/) (2020)
- [Combined Person Classification with Airborne Optical Sectioning](https://mlsanity.com/paper/2106.10077/) (2021)
- [Through-Foliage Tracking with Airborne Optical Sectioning](https://mlsanity.com/paper/2111.06959/) (2021)
- [Inverse Airborne Optical Sectioning](https://mlsanity.com/paper/2207.13344/) (2022)
- [Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing](https://mlsanity.com/paper/2211.04293/) (2022)
- [FIRe-GAN: A novel Deep Learning-based infrared-visible fusion method for wildfire imagery](https://mlsanity.com/paper/2101.11745/) (2021)
- [Fusion of Single and Integral Multispectral Aerial Images](https://mlsanity.com/paper/2311.17515/) (2023)
- [Stereoscopic Depth Perception Through Foliage](https://mlsanity.com/paper/2310.16120/) (2023)
- [An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation](https://mlsanity.com/paper/2407.10754/) (2024)
- [DeepForest: Sensing Into Self-Occluding Volumes of Vegetation With Aerial Imaging](https://mlsanity.com/paper/2502.02171/) (2025)

## Built on by (most cited first, 2 of 2)

- [How Robot Dogs See the Unseeable: Improving Visual Interpretability via Peering for Exploratory Robots](https://mlsanity.com/paper/2511.16262/) (2025)
- [Vision-Reasoning-Guided Occlusion Removal from Light Fields](https://mlsanity.com/paper/2606.19985/) (2026)

## Abstract (arXiv)

We present a method to reconstruct surface temperatures through forest vegetation by combining signal processing and machine learning, enabling fully automated aerial wildfire monitoring with drones for early fire detection. Synthetic aperture (SA) sensing reduces canopy occlusion but introduces thermal blur. To overcome this, we train a visual state space model to recover subtle thermal signals of partially occluded soil and fire hotspots from blurred data. To address limited real-world training data, we generate realistic surface temperature simulations using a latent diffusion model, temperature augmentation, and procedural thermal forest modeling. On simulated datasets, our method reduces RMSE by 2-2.5 versus conventional thermal and uncorrected SA imaging; in field experiments on hotspots, RMSE improved by 12.8-fold and 2.6-fold, respectively. Our approach also generalizes to other thermal signals, including human signatures, capturing morphology and extent -- critical where simple thresholding fails -- while conventional imaging struggles with partial occlusion.