Dataset: Image-based water stage measurement with deep learning
datacite.FundingReference.funderName | Europäische Union | |
Contributing person | Schütze, Niels (orcid: https://orcid.org/0000-0002-2376-528X) | |
Documentation of the data | 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 | |
Description of the data | Image-based water stage measurement using deep learning 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. | |
Type of the data | Image | |
Type of the data | Dataset | |
Total size of the dataset | 3932202634 | |
Author | Eltner, Anette | |
Upload date | 2020-04-06T21:51:37Z | |
Publication date | 2020-04-06T21:51:37Z | |
Publication date | 2026-05-20T14:54:07Z | |
Data of data creation | 2020 | |
Publication date | 2020-04-06 | |
Abstract of the dataset | 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. | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/2394 | |
Public reference to this page | https://doi.org/10.25532/OPARA-72 | |
Publisher | Technische Universität Dresden | |
Licence | Attribution 4.0 International | |
URI of the licence text | http://creativecommons.org/licenses/by/4.0/ | |
Specification of the discipline(s) | 3::34::317 | |
Title of the dataset | Dataset: Image-based water stage measurement with deep learning | |
Project abstract | The project goal is the development of innovative techniques for spatio-temporal high resolution monitoring and small-scale simulation of extreme events. In a cooperation between the chairs of Hydrology, Meteorology, Geoinformatics and Photogrammetry, of the TU Dresden, new types of operational monitoring systems will be developed. Existing monitoring networks will be densified using modern low-cost sensors, specific remote sensing data and geographical information systems. Additionally, historical analyses and predictive modelling of small-scale extreme events with different climate scenarios will support to predict the expected effects of climate change. The developed information will serve as a base for upcoming early warning systems and future adjustment strategies. | |
Project title | EXTRUSO - Extreme Events in Small and Medium Scale Catchments (EXTRUSO) |
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