Networks such as transportation, water, and power are critical lifelines for society. Managers plan and execute interventions to guarantee the operational state of their networks under various circumstances, including after the occurrence of (natural) hazard events. Creating an intervention program demands knowing the probable direct and indirect consequences (i.e., risk) of the various hazard events that could occur in order to be able to mitigate their effects. This paper introduces a methodology to support network managers in the quantification of the risk related to their networks. The methodology is centered on the integration of the spatial and temporal attributes of the events that need to be modeled to estimate the risk. Furthermore, the methodology supports the inclusion of the uncertainty of these events and the propagation of these uncertainties throughout the risk modeling. The methodology is implemented through a modular simulation engine that supports the updating and swapping of models according to the needs of network managers. This work demonstrates the usefulness of the methodology and simulation engine through an application to estimate the potential impact of floods and mudflows on a road network located in Switzerland. The application includes the modeling of (i) multiple time-varying hazard events; (ii) their physical and functional effects on network objects (i.e., bridges and road sections); (iii) the functional interrelationships of the affected objects; (iv) the resulting probable consequences in terms of expected costs of restoration, cost of traffic changes, and duration of network disruption; and (v) the restoration of the network.

The objective of interactive geographic maps is to provide geographic information to a large audience in a captivating and intuitive way. Storytelling helps to create exciting experiences and to explain complex or otherwise hidden relationships of geospatial data. Furthermore, interactive 3D applications offer a wide range of attractive elements for advanced visual story creation and offer the possibility to convey the same story in many different ways. In this paper, we discuss and analyze storytelling techniques in 3D geographic visualizations so that authors and developers working with geospatial data can use these techniques to conceptualize their visualization and interaction design. Finally, we outline two examples which apply the given concepts.

Historical maps are almost the exclusive source to trace back the characteristics of earth before modern earth observation techniques came into being. Processing historical maps is challenging due to the factors such as diverse designs and scales, or inherent noise from painting, aging, and scanning. Our paper is the first to leverage uncertainty estimation under the framework of Bayesian deep learning to model noise inherent in maps for semantic segmentation of hydrological features from scanned topographic historical maps. To distinguish different features with similar symbolization, we integrate atrous spatial pyramid pooling (ASPP) to incorporate multi-scale contextual information. In total, our algorithm yields predictions with an average dice coefficient of 0.827, improving the performance of a simple U-Net by 26%. Our algorithm outputs intuitively interpretable pixel-wise uncertainty maps that capture uncertainty in object boundaries, noise from drawing, aging, and scanning, as well as out-of-distribution designs. We can use the predicted uncertainty potentially to refine segmentation results, locate rare designs, and select reliable features for future GIS analyses.