Flooding is one of the most occurring natural hazards every year risking the lives and properties of the affected communities, especially in Philippine context. To visualize the extent and mitigate the impacts of flood hazard in Malingon River in Valencia City, Bukidnon, this paper presents the combination of Geographic Information System, high-resolution Digital Elevation Model, land cover, so...

In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as forests and agricultural fields. The test area is located at River Saale, Saxony-Anhalt, Germany, which is c...

Paper describes the state-of-the-art of flood management both in the Czech Republic and in the broader framework of EU. Special focus is given to geoinformation and cartographic issues within consecutive parts of emergency management cycle used in European Union countries (prevention, preparation, response, and recovery phases). For each phase an adaptive mapping solution is outlined. The princ...

The Lower Delta Islands of the Paraná River in Argentina constitute a major coastal tidal freshwater wetland located at the De La Plata River estuary, very close to Buenos Aires City. It is a complex region where the landscape pattern is characterized by mosaics of natural and man made ecosystems. Since the last few years an increased number of SAR images have been available for monitoring coas...

Abstract. Real-time probabilistic flood inundation mapping is crucial for risk warning and decision-making during the emergency period before an upcoming event. Considering high uncertainties involved in modeling of a nonlinear complex event, providing deterministic map can be erroneous misleading reliable timely decision-making. The conventional hazard maps provided different return periods ca...

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially convolutional neural networks, have proved be effective, they require intensive manual labeling of flooded pixels train a multi-layer network that learns abstract semantic features the input ...

Many researchers seek to take advantage of the recently available and virtually uninterrupted supply of satellite-based rainfall information as an alternative and supplement to the ground-based observations in order to implement a cost-effective flood prediction in many under-gauged regions around the world. Recently, NASA Applied Science Program has partnered with USAID and African-RCMRD to im...