Experiment planning and deviations

Comparison of:œ Water Mask Processor (WaMaPro); œ Rapid Mapping of Flooding (RaMaFlood); œ TSX Flood Service (TFS); œ TanDEM-X Water Indication Mask processor (TDX WAM).

Algorithm development and simulation

Furthermore, with a fictive nuclear accident scenario, the relevant CDIRS spaces and ordering rules are constructed, and then the appropriate cross-domain integrated response solution is obtained and verified effectively by reasoning and simulating in the CDIRS modelA fictitious example (the scenario ‘‘A” shown in Fig. 1) is used here to illustrate the principle and the implementation process of CDIRS: In this scenario, a disaster chain is caused by a nuclear accident ‘‘cooling system failing”; and it has rapidly evolved from ‘‘core melting” to ‘‘containment broken”, and then from ‘‘containment broken” to ‘‘radiation leaking” event which may cause a large number of casualties.

Software developmentFirst, we evaluated the system by comparing its output against human judgement as ground truth for a selection of 500 tweetsSecondly, we tested our geotagger performance with the testing dataset against other geocoding platforms including: The Alchemy API NER and the Yahoo! GeoPlanet service.The IBM research (Australia) plans the OzCrsisTracker 2.0 deployment in the Australian Bushfire and Flood season 2014.

In this paper, we suggest a solution to this problem using earthquake disaster emergency response (EDER) knowledge.We present a computer-based modeling and representation method for EDER knowledge, which is a foundation for computer-aided knowledge use.

Tool development, development of Test Scenarios and Test Data Collection in progress

Holistic framework descriptionImplementationtesting

Presentation of a robust, modular algorithm for context-based urban terrain modeling from sensor datawe proposed and described an automatic and a semi-automatic method for pose estimationAlgortihm for: geometric reconstruction, roof analysis and visibility analysis and texturing processes, to synthesise data

Eighteen participants were recruited for this study, including eight disease surveillance and biological/chemical threat analysts from PNNL, five GIScience experts from the Penn State GeoVISTA Center, and five infectious disease experts from the Penn State Center for Infectious Disease Dynamics (CIDD).Participants are asked to devise hypotheses from the collection of artifacts, and to arrange and modify artifacts and the workspace using standard office tools such as pens, post-it notes, and markersThis article reports synthesis experiment results from sessions with analysts at Pacific Northwest National Laboratory (PNNL) and experts at The Pennsylvania State University (PSU)Our experiment featured an hour-long synthesis activity in which participants work in isolation to organize and annotate a set of physical artifactsParticipants were provided markers, pens, adhesive tags, and post-it notes of multiple sizes and colors to modify the workspace and artifacts as desired.

Planning of the simulation:During the simulation, the dynamic attribute values are updated at each process stage as described belowThe Perception Module updates four attributes such as:• Emergency cues, such as smoke and alarm, that are visible or audible to the agent.• Visible floor objects, such as doors and signs, that are visible to the agent.• Visible group members that are visible to the agent.• Neighboring agents that are visible to and are located within a certain radius from the agentThe Interpretation Module maps the current knowledge of the agent into a set of internal thresholds that describe the urge and well-being of the agent.The decision-making module invokes the decision tree modeling the behavior assigned to the agent. Given the agent’s characteristics and the invoked decision tree, it looks up the agent’s behavior and determines the long- term navigation goal, such as the familiar exit of the agent or the location of the group leader, and the intermediate navigation point given the agent’s knowledge and location.The Locomotion Module calculates the agent’s movement toward the navigation target and returns the updated spatial position of the agents, which areCartesian coordinates (x, y, and z) in the continuous space.The Memory Module registers the decision made during the simulation cycle and updates the spatial knowledge. The spatial knowledge is an array storing the navigation points that the agents have visited. The agents remembered the traveled navigation points and can later refer to the spatial knowledge to avoid backtracking.