OpenForest: a data catalog for machine learning in forest monitoring
Forests play a crucial role in the Earth’s system processes and provide a suite of social and economic ecosystem services, but are significantly impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advanta...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460224000530/type/journal_article |
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| author | Arthur Ouaknine Teja Kattenborn Etienne Laliberté David Rolnick |
| author_facet | Arthur Ouaknine Teja Kattenborn Etienne Laliberté David Rolnick |
| author_sort | Arthur Ouaknine |
| collection | DOAJ |
| description | Forests play a crucial role in the Earth’s system processes and provide a suite of social and economic ecosystem services, but are significantly impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advantages in mitigating human impacts and enhancing our comprehension of forest composition, alongside the effects of climate change. While statistical modeling has traditionally found applications in forest biology, recent strides in machine learning and computer vision have reached important milestones using remote sensing data, such as tree species identification, tree crown segmentation, and forest biomass assessments. For this, the significance of open-access data remains essential in enhancing such data-driven algorithms and methodologies. Here, we provide a comprehensive and extensive overview of 86 open-access forest datasets across spatial scales, encompassing inventories, ground-based, aerial-based, satellite-based recordings, and country or world maps. These datasets are grouped in OpenForest, a dynamic catalog open to contributions that strives to reference all available open-access forest datasets. Moreover, in the context of these datasets, we aim to inspire research in machine learning applied to forest biology by establishing connections between contemporary topics, perspectives, and challenges inherent in both domains. We hope to encourage collaborations among scientists, fostering the sharing and exploration of diverse datasets through the application of machine learning methods for large-scale forest monitoring. OpenForest is available at the following url: https://github.com/RolnickLab/OpenForest. |
| format | Article |
| id | doaj-art-e697a4ece6ad405ab70abb07f9470f1e |
| institution | DOAJ |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-e697a4ece6ad405ab70abb07f9470f1e2025-08-20T03:12:47ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2024.53OpenForest: a data catalog for machine learning in forest monitoringArthur Ouaknine0https://orcid.org/0000-0003-1090-6204Teja Kattenborn1Etienne Laliberté2David Rolnick3School of Computer Science, McGill University, Montréal, QC, Canada Mila, Quebec AI Institute, Montréal, QC, CanadaRemote Sensing Centre for Earth System Research, Leipzig University, Leipzig, Germany German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, GermanyInstitut de recherche en biologie végétale, Département de sciences biologiques, Université de Montréal, Montréal, QC, CanadaSchool of Computer Science, McGill University, Montréal, QC, Canada Mila, Quebec AI Institute, Montréal, QC, CanadaForests play a crucial role in the Earth’s system processes and provide a suite of social and economic ecosystem services, but are significantly impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advantages in mitigating human impacts and enhancing our comprehension of forest composition, alongside the effects of climate change. While statistical modeling has traditionally found applications in forest biology, recent strides in machine learning and computer vision have reached important milestones using remote sensing data, such as tree species identification, tree crown segmentation, and forest biomass assessments. For this, the significance of open-access data remains essential in enhancing such data-driven algorithms and methodologies. Here, we provide a comprehensive and extensive overview of 86 open-access forest datasets across spatial scales, encompassing inventories, ground-based, aerial-based, satellite-based recordings, and country or world maps. These datasets are grouped in OpenForest, a dynamic catalog open to contributions that strives to reference all available open-access forest datasets. Moreover, in the context of these datasets, we aim to inspire research in machine learning applied to forest biology by establishing connections between contemporary topics, perspectives, and challenges inherent in both domains. We hope to encourage collaborations among scientists, fostering the sharing and exploration of diverse datasets through the application of machine learning methods for large-scale forest monitoring. OpenForest is available at the following url: https://github.com/RolnickLab/OpenForest.https://www.cambridge.org/core/product/identifier/S2634460224000530/type/journal_articledatasetsforest monitoringmachine learningremote sensing |
| spellingShingle | Arthur Ouaknine Teja Kattenborn Etienne Laliberté David Rolnick OpenForest: a data catalog for machine learning in forest monitoring Environmental Data Science datasets forest monitoring machine learning remote sensing |
| title | OpenForest: a data catalog for machine learning in forest monitoring |
| title_full | OpenForest: a data catalog for machine learning in forest monitoring |
| title_fullStr | OpenForest: a data catalog for machine learning in forest monitoring |
| title_full_unstemmed | OpenForest: a data catalog for machine learning in forest monitoring |
| title_short | OpenForest: a data catalog for machine learning in forest monitoring |
| title_sort | openforest a data catalog for machine learning in forest monitoring |
| topic | datasets forest monitoring machine learning remote sensing |
| url | https://www.cambridge.org/core/product/identifier/S2634460224000530/type/journal_article |
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