## 5 Use Case and Experiments  
The presented approaches have been implemented and experimented using the actual case of the Wenchuan earthquake. The Wenchuan earthquake was a deadly earthquake that measured at 8.0 Ms which occurred on May 12, 2008 in Sichuan province of China, killing an estimated 68,000 people. To study the damages of the earthquake, there are a large number of space-borne, air-borne, and terrestrial data collected during or after the earthquake. For example, the CBERS-02/02B satellite gathered 186 scenes of images from May 13 to June 7, and 504 scenes of images before the earthquake. The Beijing-1 satellite collected multispectral images at a resolution of 32 m covering all the disaster areas (1.39 million km\({}^{2}\)) before the earthquake, and covering the 3.49 million km\({}^{2}\) areas from May 13 to June 4. Different government departments involved in disaster mitigation used various geospatial information techniques to provide decision making support [9]. Using the approaches developed in this paper to find out the damage information in the earthquake region, six steps were implemented based on the PGIS architecture as illustrated in Figure 10.  
Step 1: User submits task information. In the ideal situation, the submitted information should be simple and natural. For instance, the user task in this case might be described as \"information about disaster damage in Wenchuan region\". Step 2: Task analyses. By using task templates and ontology technology, the user's tasks are mapped to a five dimension attribute vector related to the attributes of the geospatial data including area, temporal, spatial resolution, spectrum, and product level, as shown in Figure 11. This step corresponds to the task analysis step in the service engine layer mentioned in Section 4.3. Figure 9: Near real-time traffic map in New York City on April 17, 2011 based on the Google Map. Step 3: Task plan. According to the area, temporal, spatial resolution, spectrum information of the task, the task plan module queries the sensor registration information using the SensorML, and retrieves the sensors satisfying the demands. Then, the data acquiring demands are sent to the sensors. An example of the SensorML code is shown in Figure 12. This step can be considered as the sensor plan step in the service engine layer in Section 4.3. Figure 11: Task analyses and task template. Figure 10: Flowchart of PGIS on finding damage information in the Wenchuan earthquake. Step 4: Data acquisition by sensors. When the sensors receive the tasks, they will observe the area following the pre-defined attributes (see in Figure 13). The acquired data will be sent to the ground and stored in the database with standard descriptor language, for example OWL. After all these processes are finished, notifications will be sent to the service engine again for data processing. Step 5: Service composition. When the service engine receives the demands, it then begins to compose services for the services chain. In this case, the change detention method by comparing the remote sensing images before and after the earthquake is an effective appro  
Figure 12: Part of the SensorML code regarding the sensor description. Figure 13: Sensor plan and data acquiring based on the pre-defined attributes. damage information. Therefore, a change detection service chain is implemented as shown in Figure 14. In the composition process, the service engine performs querying, matching, and selecting services (chain) in the services (chain) database. The results of the service chain after the service composition are shown in Figure 15. The steps of services discovery and services composition in service engine layer in Section 4.3 are included in this step. _Step 6_: Service chain execution. Once the GISC has been generated, the last task is to bind every service logically to actual services distributed in the internet. The active BPEL engine is used for GISC execution. After the services chain has been executed, the results will be sent to the users.