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. https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1717 2026-04-17T20:27:59Z 2026-04-17T20:27:59Z Dataset: Image-based water stage measurement with deep learning Eltner, Anette https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1727 2020-04-06T22:36:35Z 2020-01-01T00:00:00Z Dataset: Image-based water stage measurement with deep learning Eltner, Anette 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. 2020-01-01T00:00:00Z