https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1307 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. 2026-03-17T11:48:29Z https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1727 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 https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1405 Dataset (video sequences and orientation information) to measure river surface flow velocities Eltner, Anette 2019-01-01T00:00:00Z https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1353 Flow velocity tracking in thermal imagery Lin, Dong Image data set to the submitted WRR publication ”Evaluating Image Tracking Approaches for Surface Velocimetry with Thermal Tracers“. Lab_Experiments_Data file folder includes eight image sequences acquired in lab experiments. Note that these data are processed after radiometric calibration, which means that the vignetting effect existing in original data has already been removed and the image contrast has also been improved. These processed data are the inputs for PIVlab, PTVlab and LK tracking algorithms. Furthermore, inside of Lab_Experiments_Data file folder, the file folders of 1_8bit_ContrastImprovement, 2_8bit_ContrastImprovement and 3_8bit_ContrastImprovement correspond to the data of laboratory experiments 1-3. Similarly, the file folders of 4_8bit_ContrastImprovement, 5_8bit_ContrastImprovement and 6_8bit_ContrastImprovement correspond to the data of the laboratory experiments 4-6. the file folders of 7_8bit_ContrastImprovement, 8_8bit_ContrastImprovement and 9_8bit_ContrastImprovement correspond to the data of the laboratory experiments 7-9. While Field_Experiments_Data file folder includes two image sequences acquired in field experiments. Note that these data are processed after radiometric calibration, which means that the vignetting effect existing in original data has already been removed and the image contrast has also been improved. These processed data are the inputs for PIVlab, PTVlab and LK tracking algorithms. Furthermore, inside of Field_Experiments_Data file folder, the file folders of 1_8bit_ContrastImprovement and 2_8bit_ContrastImprovement correspond to the data of field experiments 1, 2 respectively. In addition, note that the frame rate of all of the image sequences is 30 HZ. 2018-01-01T00:00:00Z https://opara.zih.tu-dresden.de/xmlui/handle/123456789/1333 StageDetect - An image-based tool for automatic water stage detection - image dataset Eltner, Anette The dataset includes image sets and data to reference the image measurements. The image sets and datasets are used for figure 3 in the in the WRR manuscript 'Automatic image-based water stage measurement for long-term observations in ungauged catchments'. The image sets comprises the master image of the image sequences captured in frequent interval with Raspberry Pi cameras at the river Wesenitz and Wernersbach in Saxony, Germany. If entire image sequences (instead of single masters) are requested (they are not provided here due to large data size), please contact Anette.Eltner@tu-dresden.de 2018-01-01T00:00:00Z