Estimating Canopy Height at Scale
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917333938733056 |
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| author | Pauls, Jan Zimmer, Max Kelly, Una M. Schwartz, Martin Saatchi, Sassan Ciais, Philippe Pokutta, Sebastian Brandt, Martin Gieseke, Fabian |
| author_facet | Pauls, Jan Zimmer, Max Kelly, Una M. Schwartz, Martin Saatchi, Sassan Ciais, Philippe Pokutta, Sebastian Brandt, Martin Gieseke, Fabian |
| contents | We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE / RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale maps. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01076 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating Canopy Height at Scale Pauls, Jan Zimmer, Max Kelly, Una M. Schwartz, Martin Saatchi, Sassan Ciais, Philippe Pokutta, Sebastian Brandt, Martin Gieseke, Fabian Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss function designed to counter geolocation inaccuracies inherent in the ground-truth height measurements, and employs data from the Shuttle Radar Topography Mission to effectively filter out erroneous labels in mountainous regions, enhancing the reliability of our predictions in those areas. A comparison between predictions and ground-truth labels yields an MAE / RMSE of 2.43 / 4.73 (meters) overall and 4.45 / 6.72 (meters) for trees taller than five meters, which depicts a substantial improvement compared to existing global-scale maps. The resulting height map as well as the underlying framework will facilitate and enhance ecological analyses at a global scale, including, but not limited to, large-scale forest and biomass monitoring. |
| title | Estimating Canopy Height at Scale |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.01076 |