analiza aiqbench
This study evaluates whether geospatial foundation model embeddings offer a distinct advantage over traditional Sentinel satellite features for mapping old-growth forests. The analysis reveals that while these models provide significant gains in standard validation, their performance edge diminishes substantially when rigorous spatial buffering is applied to prevent data leakage. Ultimately, the research highlights that spectral data from established sources remains highly robust for this specific ecological task, challenging the assumption that newer foundation models are strictly necessary for high-accuracy forest classification.
Geospatial embeddings detect old-growth forests but buffered spatial validation narrows their advantage over Sentinel features
arXiv:2609.28194v1 Announce Type: new Abstract: Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, protecting them requires mapping that is accurate for individual forest parcels yet deployable continent-wide. Geospatial foundation model (GFM) embeddings enable label-scarce land classification, but their value for old-growth detection remains unknown. Here, we map old-growth forests across 211,893 ha of Romania's Southern Carpathians, a beech-spruce landscape typical of the Alpine Biogeographic Region. We construct high-confidence, expert-informed reference labels for old-growth and non-old-growth parcels. We add AlphaEarth, TESSERA v2 and Sentinel-1/2 features to a common baseline of topographic and human-access predictors, then compare them under spatially blocked validation with and without 10 km train-test buffers to limit residual autocorrelation. With buffering, GFM and Sentinel-1/2 predictors increase precision-recall AUC by 0.21-0.25 [95% CIs: 0.15-0.34] relative to baseline, indicating spectral data contain a spatially robust old-growth signal. With a PR-AUC of 0.84 [0.79-0.88], TESSERA outperforms Sentinel-1/2 (+0.08 [+0.05 to +0.11]) and AlphaEarth (+0.08 [+0.04 to +0.12]) under unbuffered spatial validation. At a 10 km buffer, however, this advantage narrows to +0.04 [-0.01 to +0.11] and +0.03 [-0.04 to +0.10], intervals consistent with no difference. At 10 m resolution, convolutional neural networks add no benefit over pixel-based XGBoost. Comparisons with four national- and continental-scale products show the importance of non-old-growth labels, and reveal 81% agreement between our predictions and a field-calibrated map. We conclude that buffered spatial validation is vital when transferring old-growth detection models to unseen landscapes, and provide our labels and predictions for future work.
Authors:Thomas Ratsakatika (1), Mihai Zotta (2), Srinivasan Keshav (3), Emily R. Lines (1) ((1) Department of Geography, University of Cambridge, Cambridge, UK, (2) Fundatia Conservation Carpathia, Brasov, Romania, (3) Department of Computer Science and Technology, University of Cambridge, Cambridge, UK)
Abstract:Old-growth forests develop over centuries under minimal anthropogenic disturbance, producing structurally complex and biodiverse stands. In Europe, protecting them requires mapping that is accurate for individual forest parcels yet deployable continent-wide. Geospatial foundation model (GFM) embeddings enable label-scarce land classification, but their value for old-growth detection remains unknown. Here, we map old-growth forests across 211,893 ha of Romania's Southern Carpathians, a beech-spruce landscape typical of the Alpine Biogeographic Region. We construct high-confidence, expert-informed reference labels for old-growth and non-old-growth parcels. We add AlphaEarth, TESSERA v2 and Sentinel-1/2 features to a common baseline of topographic and human-access predictors, then compare them under spatially blocked validation with and without 10 km train-test buffers to limit residual autocorrelation. With buffering, GFM and Sentinel-1/2 predictors increase precision-recall AUC by 0.21-0.25 [95% CIs: 0.15-0.34] relative to baseline, indicating spectral data contain a spatially robust old-growth signal. With a PR-AUC of 0.84 [0.79-0.88], TESSERA outperforms Sentinel-1/2 (+0.08 [+0.05 to +0.11]) and AlphaEarth (+0.08 [+0.04 to +0.12]) under unbuffered spatial validation. At a 10 km buffer, however, this advantage narrows to +0.04 [-0.01 to +0.11] and +0.03 [-0.04 to +0.10], intervals consistent with no difference. At 10 m resolution, convolutional neural networks add no benefit over pixel-based XGBoost. Comparisons with four national- and continental-scale products show the importance of non-old-growth labels, and reveal 81% agreement between our predictions and a field-calibrated map. We conclude that buffered spatial validation is vital when transferring old-growth detection models to unseen landscapes, and provide our labels and predictions for future work.
34 pages, including supplementary material (19-page main article with 7 figures and 3 tables; 15-page supplement with 9 figures and 21 tables). Submitted for publication. Data: this https URL (embargoed until publication); code: this https URL
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
arXiv:2609.28194 [cs.LG]
(or arXiv:2609.28194v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.28194 arXiv-issued DOI via DataCite (pending registration)
Text integral sub licența deschisă a publicației · 355 cuvinte
Subiecte conexe
News
Quantum Computing Report · 7 iun. 2026
News Archive 2026
Quantum Computing Report · 25 iul. 2026
From Foundation Embeddings to Cropland Maps: Label Efficiency, Temporal Transferability and Independent Human Validation
arXiv · 16 sept. 2026
Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States
arXiv · 31 iul. 2026