Low elevation coastal areas are vulnerable to the effects of sea level rise and to an increase in the frequency and severity of storm surge events due to climate change.Coastal urban areas are at risk because coastal flooding causes extensive damage to energy and transportation infrastructure, disruptions to the delivery of services, devastating tolls on the public's health and,occasionally, significant loss of life. Although scientists widely stress the compelling need to mitigate and adapt to climate change, public awareness lags behind. Because WebGIS maps (web-based geographic information systems) quickly convey strong messages, condense complex information, engage people on issues of environmental change, and motivate personal actions, this paper focuses on searching the ideal flood assessment WebGIS method to encourage people to mitigate and adapt to climate change. Surveys demonstrated that 3D visualisations have an enormous added value because they are more vivid and therefore more understandable and make it easier to imagine the consequences of a flood than2D visualisations. In this research, the WebGIS will be created using Ol3-Cesium and openlayers to visualise a flood event by dynamic layers in a 2D/3D environment.
ABSTRACT:Over the last couple of years, research on indoor environments has gained a fresh impetus; more specifically applications that support navigation and wayfinding have become one of the booming industries. Indoor navigation research currently covers the technological aspect of indoor positioning and the modelling of indoor space. The algorithmic development to support navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. However, alternative algorithms for outdoor navigation have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behaviour (e.g. simplest paths, least risk paths). These algorithms are currently restricted to outdoor applications. The need for indoor cognitive algorithms is highlighted by a more challenged navigation and orientation due to the specific indoor structure (e.g. fragmentation, less visibility, confined areas…). As such, the clarity and easiness of route instructions is of paramount importance when distributing indoor routes. A shortest or fastest path indoors not necessarily aligns with the cognitive mapping of the building. Therefore, the aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically for this paper, we will focus on the application of the least risk path algorithm of Grum (2005) to an indoor space. The algorithm as proposed by Grum (2005) is duplicated and tested in a complex multi-storey building. The results of several least risk path calculations are compared to the shortest paths in indoor environments in terms of total length, improvement in route description complexity and number of turns. Several scenarios are tested in this comparison: paths covering a single floor, paths crossing several building wings and/or floors. Adjustments to the algorithm are proposed to be more aligned to the specific structure of indoor environments (e.g. no turn restrictions, restricted usage of rooms, vertical movement) and common wayfinding strategies indoors. In a later stage, other cognitive algorithms will be implemented and tested in both an indoor and combined indoor-outdoor setting, in an effort to improve the overall user experience during navigation in indoor environments. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.
ABSTRACT:In recent years, the interest of many researchers in various domains is triggered to move beyond the traditional border of twodimensionality and explore the possibilities of the third and even the fourth, temporal, dimension. The emerging research interest concerning 3D and 4D and the handling of these additional dimensions can bring many benefits to archaeology as well. A 4D GIS tailored to archaeological data would facilitate better insights and more complex analyses. Its basis must be a conceptual 4D archaeological data model, which pays attention to existing data models and standards. Although in some cases more complex, archaeological data are closely related to geography and geo-information. Since the temporal dimension is a, and possibly the most, substantial element in archaeological research, this paper focusses mainly on this dimension. In this paper, the applicability of the ISO 19108 geo-information standard on temporal information for archaeological data is investigated. For a set of common temporal categories, e.g. the excavation time, the appropriate description according to this standard is determined. This will indicate in which cases the internationally recognized standard is suitable for use in an archaeological data model. Furthermore, three versions of the West European archaeological time scale as temporal ordinal reference system are constructed. For the first version, the ISO 19108 structure is used, whereas the second and third are based on geological variants. The results of the performed analysis are favourable to the usability of the ISO 19108 standard in archaeology; however, other temporal standards or data models may yield up better results.
ABSTRACT:Over the last couple of years, applications that support navigation and wayfinding in indoor environments have become one of the booming industries. However, the algorithmic support for indoor navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. In outdoor space, several alternative algorithms have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behavior (e.g. simplest paths, least risk paths). The need for indoor cognitive algorithms is highlighted by a more challenged navigation and orientation due to the specific indoor structure (e.g. fragmentation, less visibility, confined areas…). Therefore, the aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically for this paper, we will focus on the application of the least risk path algorithm of Grum (2005) to an indoor space. The algorithm as proposed by Grum (2005) is duplicated and tested in a complex multi-story building. Several analyses compare shortest and least risk paths in indoor and in outdoor space. The results of these analyses indicate that the current outdoor least risk path algorithm does not calculate less risky paths compared to its shortest paths. In some cases, worse routes have been suggested. Adjustments to the original algorithm are proposed to be more aligned to the specific structure of indoor environments. In a later stage, other cognitive algorithms will be implemented and tested in both an indoor and combined indoor-outdoor setting, in an effort to improve the overall user experience during navigation in indoor environments.
Natural hazards do not only affect millions of people, but also cause material damages up to 300 billion USD per year worldwide. The SIDS (Small Island Developing States) are characterized by an extremely high vulnerability to these hazards, due to their low-lying, densely populated cities and their fragile economy. To limit the consequences of these hazards, technocratic interventions do not suffice. Therefore, new approaches that focus on flood risk management are developed. In this context, Annotto Bay, Jamaica, was chosen as a case study area to perform a flood damage assessment. In this study, a flood damage map was created for the 2001 flood caused by Tropical Storm Michelle. This map focuses on three types of damage: building, road and crop damage. The first type was calculated using the exact GPS locations of the buildings, as well as average replacement values for each building type and flood damage functions. The total building damage was then combined per land-use polygon to have an orderly visual view of the damage spread. Furthermore, the road damage was calculated, based on a road network extracted from satellite imagery. In a next step, as buildings are mostly located in proximity of roads, buffers were created around the road network, resulting in a more accurate visual view of the building damage spread. Then the crop damage was calculated based on maximum damage values for banana plantains and other crops, combined with the crop damage functions. The final result is a total damage map, visualizing the location of high risk areas with a high accuracy. Additionally, the total calculated damage was compared to the actual damage caused by the 2001 flood. This comparison shows promising results.
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