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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.18554 |
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| _version_ | 1866913406145003520 |
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| author | Pazos-Outón, Luis Miguel Vasconcelos, Cristina Nader Raichuk, Anton Arnab, Anurag Morris, Dan Neumann, Maxim |
| author_facet | Pazos-Outón, Luis Miguel Vasconcelos, Cristina Nader Raichuk, Anton Arnab, Anurag Morris, Dan Neumann, Maxim |
| contents | Protecting and restoring forest ecosystems is critical for biodiversity conservation and carbon sequestration. Forest monitoring on a global scale is essential for prioritizing and assessing conservation efforts. Satellite-based remote sensing is the only viable solution for providing global coverage, but to date, large-scale forest monitoring is limited to single modalities and single time points. In this paper, we present a dataset consisting of data from five public satellites for recognizing forest plantations and planted tree species across the globe. Each satellite modality consists of a multi-year time series. The dataset, named \PlantD, includes over 2M examples of 64 tree label classes (46 genera and 40 species), distributed among 41 countries. This dataset is released to foster research in forest monitoring using multimodal, multi-scale, multi-temporal data sources. Additionally, we present initial baseline results and evaluate modality fusion and data augmentation approaches for this dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Planted: a dataset for planted forest identification from multi-satellite time series Pazos-Outón, Luis Miguel Vasconcelos, Cristina Nader Raichuk, Anton Arnab, Anurag Morris, Dan Neumann, Maxim Computer Vision and Pattern Recognition Machine Learning Protecting and restoring forest ecosystems is critical for biodiversity conservation and carbon sequestration. Forest monitoring on a global scale is essential for prioritizing and assessing conservation efforts. Satellite-based remote sensing is the only viable solution for providing global coverage, but to date, large-scale forest monitoring is limited to single modalities and single time points. In this paper, we present a dataset consisting of data from five public satellites for recognizing forest plantations and planted tree species across the globe. Each satellite modality consists of a multi-year time series. The dataset, named \PlantD, includes over 2M examples of 64 tree label classes (46 genera and 40 species), distributed among 41 countries. This dataset is released to foster research in forest monitoring using multimodal, multi-scale, multi-temporal data sources. Additionally, we present initial baseline results and evaluate modality fusion and data augmentation approaches for this dataset. |
| title | Planted: a dataset for planted forest identification from multi-satellite time series |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.18554 |