Stefan Hinz
Biographic Data
| ID | 6474939 |
|---|---|
| NAME | Stefan Hinz |
| GIVEN NAMES | Stefan |
| FAMILY NAME | Hinz |
| SIGNATURE | HINZ S |
| AFFILIATIONS | Bielefeld University |
| ORCID | 0000-0002-7323-9800 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
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Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for…
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Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters are spatially limited, costly and time-consuming. In this paper, we propose a combination of hyperspectral data and machine learning methods to estimate and therefore to monitor different parameters for…
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Sociology (11 works) · Political science (8 works) · Diverse Legal and Medical Studies (7 works) · Algae (1 works) · Artificial Intelligence (1 works) · Biology (1 works) · Botany (1 works) · Chlorophyll a (1 works) · Colored dissolved organic matter (1 works) · Computer Science (1 works)