丹麦树木物种制图：比较光谱时间特征与地理空间基础模型嵌入

Computer Science > Computer Vision and Pattern Recognition arXiv:2609.03480 (cs) [Submitted on 3 Sep 2026 (v1), last revised 4 Sep 2026 (this version, v2)] Title:Tree species mapping in Denmark: A comparison of spectral-temporal features with geospatial foundation model embeddings Authors:Alkiviadis Koukos, Spyros Kondylatos, Thomas Nord-Larsen, Lotte Nyborg, Christian Tøttrup, Kenneth Grogan View a PDF of the paper titled Tree species mapping in Denmark: A comparison of spectral-temporal features with geospatial foundation model embeddings, by Alkiviadis Koukos and 5 other authors View PDF HTML (experimental) Abstract:We map tree species across Denmark using National Forest Inventory plots and EO data, while evaluating the potential of foundation models for large-scale forest characterization. We compare two alternative input representations for tree species classification: (i) manually engineered spectral-temporal features (STF) derived from multi-temporal Sentinel-1 and Sentinel-2 observations, and (ii) embeddings generated by the EO FMs TESSERA and AlphaEarth. Both representations are complemented with canopy height information. Random forest, XGBoost, and Multi-Layer Perceptron (MLP) classifiers are evaluated for all input representations, with separate assessments for pure and mixed forest stands. The STF-based MLP achieves the highest classification performance, yielding macro F1 scores of 0.843 and 0.653 for pure and mixed stands, respectively. The MLP trained on TESSERA embeddings delivers competitive performance for pure stands, achieving results within 1.1 percentage points of the best-performing model. TESSERA consistently outperforms STF-based models when fewer than approximately 25% of training plots are available, demonstrating a substantial advantage under limited training data. Multi-year observations systematically improve classification accuracy relative to single-year inputs, while ablation experiments reveal the complementary contributions of Sentinel-1 backscatter, spectral indices, and canopy height data. The best-performing model is subsequently applied at the national scale to generate a 10 m tree species map of Denmark. Area-adjusted validation indicates an overall map accuracy of 79.9%. The resulting map, released as an open-access product, is the first high-resolution national tree species map of Denmark and provides a valuable resource for forest monitoring, ecological research, and land management applications. Comments: Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2609.03480 [cs.CV] (or arXiv:2609.03480v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.03480 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alkiviadis Koukos [view email] [v1] Thu, 3 Sep 2026 07:37:29 UTC (21,701 KB) [v2] Fri, 4 Sep 2026 11:48:56 UTC (21,701 KB) Full-text links: Access Paper: View a PDF of the paper titled Tree species mapping in Denmark: A comparison of spectral-temporal features with geospatial foundation model embeddings, by Alkiviadis Koukos and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-09 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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