# Enhancing deep learning performance on burned area delineation from SPOT-6/7 imagery for emergency management

arXiv: https://arxiv.org/abs/2512.12056 · HTML: https://mlsanity.com/paper/2512.12056/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data](https://mlsanity.com/paper/2104.07778/) (2021)
- [Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision](https://mlsanity.com/paper/2002.09049/) (2020)
- [Robust Burned Area Delineation through Multitask Learning](https://mlsanity.com/paper/2309.08368/) (2023)
- [Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data](https://mlsanity.com/paper/2311.04040/) (2023)
- [Burnt area extraction from high-resolution satellite images based on anomaly detection](https://mlsanity.com/paper/2308.13367/) (2023)

## Abstract (arXiv)

After a wildfire, delineating burned areas (BAs) is crucial for quantifying damages and supporting ecosystem recovery. Current BA mapping approaches rely on computer vision models trained on post-event remote sensing imagery, but often overlook their applicability to time-constrained emergency management scenarios. This study introduces a supervised semantic segmentation workflow aimed at boosting both the performance and efficiency of BA delineation. It targets SPOT-6/7 imagery due to its very high resolution and on-demand availability. Experiments are evaluated based on Dice score, Intersection over Union, and inference time. The results show that U-Net and SegFormer models perform similarly with limited training data. However, SegFormer requires more resources, challenging its practical use in emergencies. Incorporating land cover data as an auxiliary task enhances model robustness without increasing inference time. Lastly, Test-Time Augmentation improves BA delineation performance but raises inference time, which can be mitigated with optimization methods like Mixed Precision.