DEEP LEARNING APPLIED TO WATER SEGMENTATION

We present cost-effective photogrammetric systems for high-resolution rockfall monitoring. The paper outlines the components, assembly, and programming codes required. The systems utilize prime cameras to generate 3D models and offer comparable performance to lidar for change detection monitoring. Real-world applications highlight their potential in geohazard monitoring which enables accurate detection of pre-failure deformation and rockfalls with a high temporal resolution.

The rise of new technologies such as drones (unmanned aerial systems – UASs) has allowed widespread use of image velocimetry techniques in place of more traditional, usually slower, methods during hydrometric campaigns. In order to minimize the velocity estimation errors, one must stabilise the acquired videos. In this research, we compare the performance of different UAS video stabilisation tools and provide guidelines for their use in videos with different flight and ground conditions.

Intensified extreme weather events due to climate change can result in changes of soil erosion. These unclear developments make an improvement of soil erosion modelling all the more important. Assuming that soil erosion models cannot keep up with the current data, this work gives an overview of 44 models, their strengths and weaknesses and discusses their potential for further development with respect to new and improved soil and soil erosion assessment techniques.

An automatic workflow is introduced to measure surface flow velocities in rivers. The provided tool enables the measurement of spatially distributed surface flow velocities independently of the image acquisition perspective. Furthermore, the study illustrates how river discharge in previously ungauged and unmeasured regions can be retrieved, considering the image-based flow velocities and digital elevation models of the studied river reach reconstructed with UAV photogrammetry.

Three-dimensional reconstruction of earth surfaces from overlapping images is a promising tool for geoscientists. The method is very flexible, cost-efficient and easy to use, leading to a high variability in applications at different scales. Performance evaluation reveals that good accuracies are achievable but depend on the requirements of the individual case study. Future applications and developments (i.e. big data) will consolidate this essential tool for digital surface mapping.