GeoFairy: Towards a one-stop and location based Service for Geospatial Information Retrieval
Introduction
Mobile devices play an essential role in our lives today. Since wireless network is turned on by a great amount of devices, mobile platforms have already become the mainstream method for people to search and retrieve information. Location based services are widely deployed as mobile Apps to customize and deliver various kinds of information and capabilities to users.
A number of such Apps are already on the shelves, e.g. Google Maps, Apple Maps, Navigator, The Weather Channel, AccuWeather, Climate FieldView and ESRI ArcGIS mobile. Each App is designed to provide some specific information in a relatively static way, e.g., Google Maps for maps and satellite images, AccuWeather for current weather and weather forecast, Climate FieldView for agricultural field related information and ArcGIS for spatial data viewing and analyzing. The Apps have different transfer channels and separated outlets. Users have to download and install an App to acquire the contained information at one time. Besides, extra operations are often needed such as signing up, subscribing services, learning user guide and formalizing recognizable requests. It is complicated and very inconvenient for most users. A simplified App one-stop serving all kinds of GI has been widely recognized as a public desire, especially in emergent scenarios like response actions to disasters like earthquakes, flooding, wildfires and hurricanes. However, there are very few progresses towards this direction yet. The main challenge comes from the high heterogeneity and poor interoperability of the involved data and service interfaces.
As plenty of EO datasets have been released by agencies such as NASA (National Aeronautics and Space Administration), NOAA (National Oceanic and Atmospheric Administration), USGS (U.S. Geological Survey), ESA (European Space Agency) and EEA (European Environment Agency), the first problem becomes how to collect and decode those products, extract and encode information, and display it on an intuitive interface. To ensure the sources are live updated, the App is required to real-time and on-demand retrieve data from providers via machine-to-machine communication. During this process, interoperability issues are inevitably raised. Protocols and standards are used to facilitate the communication. Standard interfaces can formalize the access, negotiation and delivery among clients and services. There are a number of standards which have been established by several organizations like OGC (Open Geospatial Consortium), ISO (International Organization for Standardization), W3C (World Wide Web Consortium) and OASIS (Organization for the Advancement of Structured Information Standards) and widely applied in industrial businesses. Most of the existing services are standard compliant and demand clients to learn about those standards before using them. Therefore, it is a big challenge to realize the ability automatically interacting with disparate service standards and data formats, and enable intelligent collection and stream from geospatial data providers to endpoint devices.
To address these issues, this paper proposes a LBS framework for one stop retrieval of GI on mobile platforms. The framework integrates a number of state-of-the-art techniques with geospatial resources and let them cooperate together to provide a robust and highly available LBS. A system named GeoFairy is implemented. It supports to gather and deliver twelve kinds of geospatial related information on real time user locations. Two clients are built to adapt to the iOS and Android systems. GeoFairy is preliminarily tested on twenty six phones at various locations. The results show that GeoFairy is capable of one-stop delivering multiple GI of current locations to consumers and reducing cost on information searching and retrieving. This capability is extremely helpful in scenarios like agricultural activity planning, vegetation monitoring, disaster response and daily inquiry. On both practical and theoretical aspects, GeoFairy sets an example to the community in realizing one-stop operational LBS for GI distribution and retrieval.
The remainder of the paper is organized as follows. Section 2 introduces the background of this work. Section 3 investigates the related work. Section 4 describes the proposed framework. Section 5 briefs the implementation of GeoFairy. In Section 6, tests are made on GeoFairy and the results are evaluated. Section 7 discusses the advantages and disadvantages of GeoFairy. The conclusion and future work are given in Section 8.
Section snippets
Background
The state-of-the-art technologies create a suitable environment for the born of GeoFairy. Fig. 1 shows the related architectures and platforms. Wireless network makes mobile devices accessible to the Internet. Mobile software development kit (SDK) is used to write mobile Apps. Geospatial web services provide geospatial data products and processing capabilities on the Web. RESTful services support concise interfaces for clients. LBS customizes the service content according to coordinates. High
Related work
An investigation has been conducted on the recent researches of one-stop geospatial services. Geoportal represents a website as one-stop entry point to GI. Tait reviewed four key distributed GIS portal projects in 2004 and discussed the challenges for wider usage of GIS [39]. Maguire et al. studied the emergence of geoportals and discussed the contribution of SDI (spatial data infrastructure) to simplifying the access to GI [40]. Goodchild et al. reviewed the history of geospatial data products
Framework
This section introduces a client-and-server framework (shown in Fig. 2). The service module is divided into several sub modules: request handler, data collector, service adapter, data decoder, data cache, information encoder and load balancer. The client module includes five sub modules: location manager, information viewer, request builder, response parser and message transceiver. All the modules are explained below.
Implementation
We implemented a system named GeoFairy for validation. The programming languages, libraries and tools used in GeoFairy include Java, C ++, Shell, Javascript, HTML5, CSS3, MySQL, GeoTools, GDAL, Eclipse, Apache Tomcat, Apache HTTP Server, Apache Cordova, iOS SDK, Android SDK and JSON Java processing API. Java is used to develop most components of the server module. GDAL C ++ and GeoTools are used in basic image and feature processing such as getting metadata and getting the value of a specific
Experiments and results
Experiments have been made to evaluate GeoFairy's coverage, responding speed, precision and consistency. We preliminarily installed GeoFairy on twenty six mobile devices, including eighteen iPhones and eight Samsung Android phones, and use them to retrieve GI. The services they accessed include NOAA weather service, GMU vegetation index products, elevation, Google Geocode service, USDA cropland data layer and hardiness zone product, NASA soil moisture and atmosphere products. The test shows the
What is new?
The basic techniques are not newly created. The original contribution is the GeoFairy framework which is able to smoothly plug in the existing techniques and apply them on distributed and heterogeneous geospatial resources. The integration successfully achieves the goal of on-demand gathering from distributed providers and delivering twelve kinds of specialized GI in a genuine one-stop and location based manner. Compared to the existing geoportals which only serves metadata catalogs, the
Conclusion and future work
This paper proposes a framework to realize one-stop and global LBS for GI retrieval on mobile platforms. The framework originally integrates a number of state-of-the-art techniques with geospatial resources and let them cooperate together to provide a robust and highly available LBS. Cloud platform is used to deploy the server module. A location enabled load balancing algorithm is presented to balance the cloud instance VMs on behalf of LBS requirements. A system named GeoFairy is developed to
Acknowledgments
This work is supported partially by grants from NSF EarthCube (Grant # ICER-1440294, PI: Dr. Liping Di), the U.S. Department of Energy (Grant # DE-NA0001123, PI: Dr. Liping Di) and OGC Testbed 12.
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