Deep learning techniques through semantic segmentation networks have been widely used
for natural disaster analysis and response. The underlying base of these implementations …

Although reflectometry is not the primary application of Global Positioning System (GPS) and
similar Global Navigation Satellite Systems (GNSS), fast‐growing GNSS tracking networks …

Hydro-numerical models are increasingly important to determine the adequacy and evaluate
the effectiveness of potential flood protection measures. However, a significant obstacle in …

Various tasks encountered in real-world surveillance can be addressed by determining
posteriors (eg by Bayesian inference or machine learning), based on which critical decisions …

Waterbody extraction from satellite imagery plays a crucial role in various environmental
monitoring and management applications. Accurate identification and delineation of water …

Flooding in urban areas is counted as a significant disaster that must be correctly mitigated
due to the huge amount of affected people, material losses, hampered economic activity …

The water level provides critically important information for disaster mitigation and water
resource management. Image-based recognition and processing systems employed to …

Accurate segmentation of river water in close-range Remote Sensing (RS) images is vital for
efficient environmental monitoring and management. However, this task poses significant …

Riverbed materials serve multiple environmental functions as a habitat for aquatic
invertebrates and fish. At the same time, the particle size of the bed material reflects the …

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic
segmentation of flood images. Imagery datasets of urban flooding were used to train two …