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Dataset: Image-based water stage measurement with deep learning
Metadaten
| Weitere mitwirkende Personen, Institutionen oder Unternehmen | eu - Funder | |
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Eltner, Anette | |
| Kurzbeschreibung | This dataset contains the results of the application of deep learning to classify water areas in images captured with a low-cost Raspberry Pi camera. Furthermore, the photogrammetrically measured 3D point cloud of the shore and the extracted water stage information is included as well as the comparison between camera gauge measurement and an independent reference gauge measurement. | |
| Entstehungsjahr oder Entstehungszeitraum | 2020 | |
| Veröffentlichungsjahr | 2020 | |
| Herausgeber | Technische Universität Dresden | |
| Inhalt der Forschungsdaten | Image, Dataset: extracted water contours, corresponding calculated water stages, 3D point cloud of the shore as well as comparison to reference gauge measurements images with classified water area | |
| Inhaber der Nutzungsrechte | Technische Universität Dresden | |
| Nutzungsrechte des Datensatzes | CC-BY-4.0 | |
| Angabe der Fachgebiete | Geography | de |
| Titel des Datensatzes | Dataset: Image-based water stage measurement with deep learning |
Dateien zu dieser Ressource
Die Datenpakete erscheinen in:
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Image-based water stage measurement using deep learning [1]
A workflow is introduced to automatically measure water stages based on image measurements using deep learning. So far, most camera gauges do not provide the needed robustness to achieve accurate water stage measurements because of changing environmental conditions. The novel, suggested approach is based on two CNNs (i.e. FCN and SegNet) to identify water in imagery. The image information is transformed into metric water level values intersecting the extracted water contour with a 3D model. The workflow allows for the densification of river monitoring networks based on low-cost camera gauges in various scenarios.