The OPARA service was recently upgraded to a new technical platform. You are visiting the outdated OPARA website. Please use https://opara.zih.tu-dresden.de/ for new data submissions. Previously stored data will be migrated in near future and then the old version of OPARA will finally be shut down. Existing DOIs for data publications remain valid.
Dataset: Image-based water stage measurement with deep learning
Metadata
| Other contributing persons, institutions or organisations | eu - Funder | |
| Person(s) who is (are) responsible for the content of the research data | Eltner, Anette | |
| Abstract | 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. | |
| Year or period of data production | 2020 | |
| Publication year | 2020 | |
| Publisher | Technische Universität Dresden | |
| Content of the research data | 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 | |
| Holder of usage rights | Technische Universität Dresden | |
| Usage rights of the data | CC-BY-4.0 | |
| Discipline(s) | Geography | de |
| Title of the dataset | Dataset: Image-based water stage measurement with deep learning |
Files in this item
This item appears in the following Collection(s)
-
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.