Urban tree species benchmark dataset for time series classificationEasyData - Data Terra
Classification of urban tree species is essential for understanding their ecological functions, managing urban forests (public and private), and informing nature-based solutions for climate resilience. We present a benchmark dataset for urban tree species classification based on multi-source optical...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925005049 |
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| author | Clément Bressant Romain Wenger David Michéa Anne Puissant |
| author_facet | Clément Bressant Romain Wenger David Michéa Anne Puissant |
| author_sort | Clément Bressant |
| collection | DOAJ |
| description | Classification of urban tree species is essential for understanding their ecological functions, managing urban forests (public and private), and informing nature-based solutions for climate resilience. We present a benchmark dataset for urban tree species classification based on multi-source optical satellite image time series (SITS). The dataset provides, on the city of Strasbourg (France), surface reflectance values extracted from coregistered Sentinel-2 and PlanetScope imagery on public trees. Species labels and geolocations are derived from the city inventory Patrimoine arboré 2022. A total of 45,084 trees representing the 20 most common species are included. The dataset is formatted for time series classification, with surface reflectance values and consistent spatial sampling. It supports direct integration into deep learning frameworks and includes three InceptionTime-based models trained on Sentinel-2, PlanetScope and both sources through a fusion architecture (Dual-InceptionTime). Model outputs—predicted species, confidence scores, and correctness flags—are provided, along with an interactive t-SNE visualization of the latent feature space for interpretability and error analysis. This dataset offers a reproducible framework for evaluating species classification models, fusion strategies, and explainability techniques, and contributes to advancing urban vegetation monitoring using satellite image time series and deep learning. |
| format | Article |
| id | doaj-art-ee89739dc2f64e17a73bd13d6d263093 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-ee89739dc2f64e17a73bd13d6d2630932025-08-20T03:42:53ZengElsevierData in Brief2352-34092025-08-016111177710.1016/j.dib.2025.111777Urban tree species benchmark dataset for time series classificationEasyData - Data TerraClément Bressant0Romain Wenger1David Michéa2Anne Puissant3LIVE UMR 7362 CNRS, University of Strasbourg, 3 rue de l’Argonne, Strasbourg, 67000, FranceLIVE UMR 7362 CNRS, University of Strasbourg, 3 rue de l’Argonne, Strasbourg, 67000, France; Corresponding author.EOST UAR 830 CNRS, University of Strasbourg, 5 Rue René Descartes, Strasbourg, 67000, FranceLIVE UMR 7362 CNRS, University of Strasbourg, 3 rue de l’Argonne, Strasbourg, 67000, France; UAR 2013 CNRS, Data Terra / THEIA, 500 rue Jean François Breton, Montpellier, 34090, FranceClassification of urban tree species is essential for understanding their ecological functions, managing urban forests (public and private), and informing nature-based solutions for climate resilience. We present a benchmark dataset for urban tree species classification based on multi-source optical satellite image time series (SITS). The dataset provides, on the city of Strasbourg (France), surface reflectance values extracted from coregistered Sentinel-2 and PlanetScope imagery on public trees. Species labels and geolocations are derived from the city inventory Patrimoine arboré 2022. A total of 45,084 trees representing the 20 most common species are included. The dataset is formatted for time series classification, with surface reflectance values and consistent spatial sampling. It supports direct integration into deep learning frameworks and includes three InceptionTime-based models trained on Sentinel-2, PlanetScope and both sources through a fusion architecture (Dual-InceptionTime). Model outputs—predicted species, confidence scores, and correctness flags—are provided, along with an interactive t-SNE visualization of the latent feature space for interpretability and error analysis. This dataset offers a reproducible framework for evaluating species classification models, fusion strategies, and explainability techniques, and contributes to advancing urban vegetation monitoring using satellite image time series and deep learning.http://www.sciencedirect.com/science/article/pii/S2352340925005049Urban treesSentinel-2PlanetScopeTime series classificationDeep learning |
| spellingShingle | Clément Bressant Romain Wenger David Michéa Anne Puissant Urban tree species benchmark dataset for time series classificationEasyData - Data Terra Data in Brief Urban trees Sentinel-2 PlanetScope Time series classification Deep learning |
| title | Urban tree species benchmark dataset for time series classificationEasyData - Data Terra |
| title_full | Urban tree species benchmark dataset for time series classificationEasyData - Data Terra |
| title_fullStr | Urban tree species benchmark dataset for time series classificationEasyData - Data Terra |
| title_full_unstemmed | Urban tree species benchmark dataset for time series classificationEasyData - Data Terra |
| title_short | Urban tree species benchmark dataset for time series classificationEasyData - Data Terra |
| title_sort | urban tree species benchmark dataset for time series classificationeasydata data terra |
| topic | Urban trees Sentinel-2 PlanetScope Time series classification Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925005049 |
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