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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.08303 |
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| _version_ | 1866918138661044224 |
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| author | Wafiq, M. Warizmi Cutter, Peter Poortinga, Ate Torre, Daniel Marc G. dela Tenneson, Karis Teck, Vanna Bihari, Enikoe Saisaward, Chanarun Suaruang, Weraphong McMahon, Andrea Muin, Andi Vika Faradiba Batiran, Karno B. A, Chairil Qomar, Nurul Metananda, Arya Arismaya Ganz, David Saah, David |
| author_facet | Wafiq, M. Warizmi Cutter, Peter Poortinga, Ate Torre, Daniel Marc G. dela Tenneson, Karis Teck, Vanna Bihari, Enikoe Saisaward, Chanarun Suaruang, Weraphong McMahon, Andrea Muin, Andi Vika Faradiba Batiran, Karno B. A, Chairil Qomar, Nurul Metananda, Arya Arismaya Ganz, David Saah, David |
| contents | Oil palm cultivation remains one of the leading causes of deforestation in Indonesia. To better track and address this challenge, detailed and reliable mapping is needed to support sustainability efforts and emerging regulatory frameworks. We present an open-access geospatial dataset of oil palm plantations and related land cover types in Indonesia, produced through expert labeling of high-resolution satellite imagery from 2020 to 2024. The dataset provides polygon-based, wall-to-wall annotations across a range of agro-ecological zones and includes a hierarchical typology that distinguishes oil palm planting stages as well as similar perennial crops. Quality was ensured through multi-interpreter consensus and field validation. The dataset was created using wall-to-wall digitization over large grids, making it suitable for training and benchmarking both conventional convolutional neural networks and newer geospatial foundation models. Released under a CC-BY license, it fills a key gap in training data for remote sensing and aims to improve the accuracy of land cover types mapping. By supporting transparent monitoring of oil palm expansion, the resource contributes to global deforestation reduction goals and follows FAIR data principles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08303 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia Wafiq, M. Warizmi Cutter, Peter Poortinga, Ate Torre, Daniel Marc G. dela Tenneson, Karis Teck, Vanna Bihari, Enikoe Saisaward, Chanarun Suaruang, Weraphong McMahon, Andrea Muin, Andi Vika Faradiba Batiran, Karno B. A, Chairil Qomar, Nurul Metananda, Arya Arismaya Ganz, David Saah, David Computer Vision and Pattern Recognition Oil palm cultivation remains one of the leading causes of deforestation in Indonesia. To better track and address this challenge, detailed and reliable mapping is needed to support sustainability efforts and emerging regulatory frameworks. We present an open-access geospatial dataset of oil palm plantations and related land cover types in Indonesia, produced through expert labeling of high-resolution satellite imagery from 2020 to 2024. The dataset provides polygon-based, wall-to-wall annotations across a range of agro-ecological zones and includes a hierarchical typology that distinguishes oil palm planting stages as well as similar perennial crops. Quality was ensured through multi-interpreter consensus and field validation. The dataset was created using wall-to-wall digitization over large grids, making it suitable for training and benchmarking both conventional convolutional neural networks and newer geospatial foundation models. Released under a CC-BY license, it fills a key gap in training data for remote sensing and aims to improve the accuracy of land cover types mapping. By supporting transparent monitoring of oil palm expansion, the resource contributes to global deforestation reduction goals and follows FAIR data principles. |
| title | An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.08303 |