Data collection plan

the data basis for this study is TanDEM-X co-registered single-look slantrange complex (CoSSC) data, which are acquired in X-band with a signal wavelength, ë, of 3.1 cm.Five test areas have been selected to compare the four proposed approaches WaMaPro, RaMaFlood, TFS, and TDX WAM based on subsets of StripMap data of the TanDEM-X mission. The test areas are distributed over three continents and are located in Vietnam, the Netherlands, Germany, Mali, and China (Figure 1), covering areas of 538.4, 324.4, 452.9, 284.2, and 120.1 km2, respectively

In order to verify the validity of the model and algorithm, this paper established the simulation network as shown in figure 3, the network node number is 28, road number is 45, we specifie node 1 as a starting point and node 28 as the end.

he OzCT geotagger has been recently deployed in a

Sündmus, kus süstemaatiliselt hinnatakse olemasolevate ja tekkivate vajaduste lahendusi nii, et praktikud saaksid seda rakendada järgides praktilist ja süstemaatilist lähenemisviisi.

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system of the OzCrisisTracker application.The best method to evaluate the effectiveness of automated or semi-automated geotagging processes—such as identifying geo/non-geo references, specifying the geographic focus in the content and disambiguation of the results of the geo references—is to compare the results with manual human geotagging.

A decision problem framework and a case study have been used as theoretical framework and an application test, respectively, to evaluate the EDER knowledge architecture and models.Together, they provide an evaluation of the knowledge architecture and corresponding models, including whether and how the architecture and models can be used for the EDER representation.Our case study example ‘Estimation of earthquake influence area and possible heavy disaster area’ focuses on a complex decision problem as mentioned in Table 1.

In progress

Use of case study Technologies for the future of hotels (THOFU) [80], EscapeSim has been employed to model a hotel environment and to assess emergency evacuations in this space

The input for our building detection procedure is an elevation map, or, if geo-referenced, a Digital Surface Model (DSM) that either results from an Airborne Laser Scan (ALS) or is sampled from a dense point cloud resulting from one or several depth maps.The first data set we present stems from the village Bonnland, a widely used urban training facility in Southern Germany.The input data is a DSM computed from an ALS point cloud of as well as a corresponding digital orthophoto. The 3D building polygons were generated by methods of Gross et al. (2005).The second data set (Vaihingen area 3) is an ISPRS WG III/4 benchmark due to Rottensteiner et al. (2012) for urban terrain reconstruction. It represents a purely residential area in the city of Vaihingen (Germany) and contains 56 small, detached houses and many trees.the last data set is the area 4 of the Toronto data set which is also an ISPRS benchmark.

Participants were instructed that an avian influenza outbreak had occurred in the Pacific Northwest and that their task was to develop hypotheses for the source of the outbreak using the artifacts and tools they had been provided with. During the experiment, participants were asked to provide a talk-aloud (Ericsson and Simon 1993) verbal protocol to state what they were doing. Averbal protocol provides context for participant actions during the experiment to aid post-experiment coding analysis.

Semantic representation of building safety features, visibility graph  (for the specific technical characteristics and the architecture of the model please see the article)Defined rules: Rule #1 An agent can detect the navigational points that are within the line of sight at each simulation step. Rule #2 An agent chooses intermediate navigation points based on its navigation destinations and its knowledge of the building.Rule #3 An agent ‘‘memorizes’’ the traveled space to avoid backtracking.At the individual level, an agent has a physical profile, a level of familiarity with the building, and prior known exits of at least one that the agent enters. The physical profile includes attributes such as age, gender, body size, travel speed, and personal space.• At the group level, the attributes defined for social groups include a group leader (if any), the group intimacy level (e.g., high intimacy for a family group), the group-seeking property (describing agents’ willingness to search for missing members), and the group influence (describing the influence of a member to the others in the same group). The agents belonging to the same group share the same group attributes.• At the crowd level, an agent’s social position is defined by the social order that reflects the likelihood of the agent to exhibit deference behavior. The lower the social order, the higher the chance for theagent to defer decision to other agents when negotiating the next move. A special agent, such as authority figures and a safety personnel, may have assigned roles and is responsible for executing actions, such as sharing information and giving instructions.

They collected data through interviews and official reports.