AGBD: A Global-scale Biomass Dataset
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| Název: | AGBD: A Global-scale Biomass Dataset |
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| Autoři: | G. Sialelli, T. Peters, J. D. Wegner, K. Schindler |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol X-G-2025, Pp 829-838 (2025) |
| Publication Status: | Preprint |
| Informace o vydavateli: | Copernicus GmbH, 2025. |
| Rok vydání: | 2025 |
| Témata: | FOS: Computer and information sciences, Technology, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, 15. Life on land, Engineering (General). Civil engineering (General), TA1501-1820, Machine Learning (cs.LG), Remote Sensing, Machine Learning, 13. Climate action, FOS: Electrical engineering, electronic engineering, information engineering, Applied optics. Photonics, TA1-2040, Biomass Estimation, Dataset |
| Popis: | Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanity’s biggest challenges: climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they focus on specific, local regions at high resolution, or they offer global coverage at low resolution. There is a need for a machine learning-ready, globally representative, high-resolution benchmark dataset. Our findings indicate significant variability in biomass estimates across different vegetation types, emphasizing the necessity for a dataset that accurately captures global diversity. To address these gaps, we introduce a comprehensive new dataset that is globally distributed, covers a range of vegetation types, and spans several years. This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery. Additionally, it includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map. We also produce a dense, high-resolution (10 m) map of AGB predictions for the entire area covered by the dataset. Rigorously tested, our dataset is accompanied by several benchmark models and is publicly available. It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation. |
| Druh dokumentu: | Article Conference object Other literature type |
| Popis souboru: | application/pdf |
| Jazyk: | English |
| ISSN: | 2194-9050 |
| DOI: | 10.5194/isprs-annals-x-g-2025-829-2025 |
| DOI: | 10.48550/arxiv.2406.04928 |
| DOI: | 10.3929/ethz-c-000784173 |
| Přístupová URL adresa: | http://arxiv.org/abs/2406.04928 https://isprs-annals.copernicus.org/articles/X-G-2025/829/2025/ https://doaj.org/article/8779add686b140a6b2cfb870d66eb1ac |
| Rights: | CC BY CC BY NC SA |
| Přístupové číslo: | edsair.doi.dedup.....2a207a73619d3347386387cb4ddf4eaa |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanity’s biggest challenges: climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they focus on specific, local regions at high resolution, or they offer global coverage at low resolution. There is a need for a machine learning-ready, globally representative, high-resolution benchmark dataset. Our findings indicate significant variability in biomass estimates across different vegetation types, emphasizing the necessity for a dataset that accurately captures global diversity. To address these gaps, we introduce a comprehensive new dataset that is globally distributed, covers a range of vegetation types, and spans several years. This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery. Additionally, it includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map. We also produce a dense, high-resolution (10 m) map of AGB predictions for the entire area covered by the dataset. Rigorously tested, our dataset is accompanied by several benchmark models and is publicly available. It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation. |
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| ISSN: | 21949050 |
| DOI: | 10.5194/isprs-annals-x-g-2025-829-2025 |