EXTRUSO - Extreme Events in Small and Medium Scale Catchments (EXTRUSO)
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.
Dieses Projekt ist Open Access und öffentlich zugänglich.
Auflistung nach
Sammlungen in diesem Bereich
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Automatic image-based water stage measurement for long-term observations in ungauged catchments [1]
Small scale and headwater catchments are mostly ungauged, even though their observation could help to improve the understanding of hydrological processes. However, it is expensive to build and maintain conventional measurement ... -
Evaluating Image Tracking Approaches for Surface Velocimetry with Thermal Tracers [1]
In this paper an automatic approach is proposed to measure flow velocity with an uncooled thermal camera. Hot water is used as thermal tracer. The introduced tracking algorithm utilizes the pyramidal Lucas-Kanade method ... -
Flow velocity and discharge measurement in rivers using terrestrial and UAV imagery [1]
An automatic workflow is introduced, including an image-based tracking tool, to measure surface flow velocities in rivers. The method is based on PTV and comprises an automatic definition of the search area for particles ... -
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 ... -
Calibrating low-cost rain gauge sensors
This repository contains a dataset from a calibration campaign of 20 low-cost rain gauges, data of 3 reference gauges and a dataset of the labcalibration of 66 low-cost raingauges. Further, source code for Arduino and ...
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Dataset: Image-based water stage measurement with deep learning
(Technische Universität Dresden, 2020)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 ... -
Dataset (video sequences and orientation information) to measure river surface flow velocities
(Technische Universität Dresden, 2019) -
Flow velocity tracking in thermal imagery
(Technische Universität Dresden, 2018)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 ... -
StageDetect - An image-based tool for automatic water stage detection - image dataset
(Technische Universität Dresden, 2018)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 ...
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