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README.md
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The goal of this dataset is to test deep learning algorithms that predict yearly Above Ground Biomass (AGB) for Finnish forests using satellite imagery.
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Feature data: Satellite imagery from the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions, designed to collect a rich array of Earth observation data
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Label data: Ground-truth AGB measurements collected using LiDAR (Light Detection and Ranging) calibrated with in-situ measurements. LiDAR is able to generate high-quality AGB maps, but is more time consuming and intensive to collect than satellite imagery.
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license: cc-by-4.0
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tags:
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- climate
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size_categories:
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---
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license: cc-by-4.0
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language:
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- en
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tags:
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- climate
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pretty_name: BioMassters
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size_categories:
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- 100K<n<1M
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---
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The goal of this dataset is to test deep learning algorithms that predict yearly Above Ground Biomass (AGB) for Finnish forests using satellite imagery.
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Feature data: Satellite imagery from the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions, designed to collect a rich array of Earth observation data
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+
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Label data: Ground-truth AGB measurements collected using LiDAR (Light Detection and Ranging) calibrated with in-situ measurements. LiDAR is able to generate high-quality AGB maps, but is more time consuming and intensive to collect than satellite imagery.
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