## 1 Introduction  
Nowadays, the trend of geospatial information (GI) technology is toward an open (share and interoperation), web-based and distributed structure [1, 2]. Three typical mechanisms have been discussed in the past decade to help in implementing this task: sensor web [3]; linked data [4]; and service-oriented computing [5].  
Sensor Web Enablement (SWE) is a framework published by the Open Geospatial Consortium (OGC), which specifies the interoperability interfaces, protocols, and metadata encodings enabling near real-time integration of heterogeneous sensor webs into the geospatial information infrastructure [3]. SWE helps navigate through the increasing amounts of data acquired from a wide variety of _in-situ_ and dynamic sensors.  
Linked data enables links to be set between items in different data sources and therefore connected for discovery, accessing, integration, and use of data [4]. It connects data into a globe data space.  
Service-oriented computing is a promising approach to change the existing geospatial information processing methods into geospatial information services [5]. It allows manual data processing tasks to be automated through geospatial information service chains (GISC) [6].  
From data to service is the common goal of the above mentioned techniques. The term \"service\" has also been extended to human or citizen recently. Human/citizen sensor network is a new paradigm to strengthen data collection by Internet or Web-enabled mobile sensors and powerful computing ability. Humans as citizens on the ubiquitous Web, acting as sensors and sharing their observations and views using mobile devices and Web 2.0 services [7], _i.e.,_ \"all for one and one for all\" [8].  
However, most of the existing approaches are either simple data-centered or sensor-centered, which lead to unsatisfactory data sharing and interoperation. The raw data collected from the sensors cannot be processed in depth enough to satisfy complex and flexible user demands in term of knowledge discovery. They cannot be shared and fused to achieve more integrated goals in term of scalability either. The contrast between the increasing ability to acquire data and insufficient abilities to process data becomes more serious. The problem \"data-rich but analysis-poor\" [8] is worsening. The fundamental reason behind this problem is not due to the lack of processing approaches to deal with those tremendous datasets, but the shortage of effective sharing, aggregation, and cooperation service mechanisms to handle the sensors, data, and processes focusing on user's complex tasks in near real-time [9].  
With regard to the problem mentioned above, this paper presents a service-oriented architecture for Proactive Geospatial Information Service (PGIS) based on our previous works [9, 10, 11, 12]. The PGIS is an aggregation and cooperation mechanism to integrate all the sensor, data, processing, and social resources to satisfy complicated user requirements. The rest of this paper is organized as follows. After giving a literature review on the related works in Section 2, the fundamentals of the proactive geospatial information service are discussed in Section 3. Section 4 discusses the details of the service-orientated architecture for PGIS. Section 5 presents an actual geospatial information service application with regard to the Wenchuan earthquake in China and the preliminary experimental results. Finally, the concluding remarks are presented and future work discussed in Section 6.