Annual Earth-observation embeddings encode wildfire disturbance and support simplified burned area mapping

📅 2026-09-22
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🤖 AI Summary
该研究使用Tessera和AlphaEarth年度地球观测嵌入来简化中分辨率火烧区域的映射,无需特定火灾图像或密集时间序列分析,有效提高了火烧区域监测的准确性和效率。
📝 Abstract
Medium-resolution (10-30 m) burned area mapping is vital for monitoring wildfires and their impacts, but remains difficult to scale. Existing methods require either curated fire-specific imagery or dense time-series analysis. Here, we tested whether annual Earth-observation embeddings retain wildfire disturbance signals sufficiently to map burned areas without either requirement. Using Tessera and AlphaEarth embeddings, we tested individual burn-scar delineation, mapping of all same-year fires within an area, regional wall-to-wall mapping, cross-continental transfer, and intra-annual fire timing. Tessera strongly encoded wildfire disturbance, allowing even linear models to separate burned from unburned pixels; the signal was weaker in AlphaEarth. Models trained on a single Tessera embedding matched or exceeded equivalent models using paired pre- and post-fire HLS imagery, and outperformed post-fire imagery alone. The same approach mapped all same-year fires within benchmark scenes (F1 = 0.90). Applied across California, with no California fire data used for downstream training, it recovered 97% of reference burned area and detected substantially more small and medium-sized fires than GABAM or MCD64A1. Separately, a model trained on 2018-2021 US fires transferred without retraining to 88 European fires from 2024-2025 (F1 = 0.88). For well-detected fires, ignition timing was recovered with a mean absolute error of 13 days. Performance declined for fires ignited near the end of the calendar year, and wall-to-wall deployment produced systematic false positives in some unseen landscapes. Annual embeddings nevertheless achieve high segmentation accuracy while moving the burden of dense time series processing upstream, providing a promising path towards simpler regional burned area mapping.
Problem

Research questions and friction points this paper is trying to address.

wildfire disturbance
burned area mapping
Earth-observation embeddings
Innovation

Methods, ideas, or system contributions that make the work stand out.

Annual Earth-observation embeddings
wildfire disturbance
burned area mapping
Tessera
cross-continental transfer
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