Geospatial Semantic Web Bookmarks
I’ve created a page with geospatial semantic web links. I hope readers of this blog will find these bookmarks to be useful.
http://www.geospatialsemanticweb.com/bookmarks/
If you have other links to suggest, please let me know — email me or add comments to the page.
Folksonomy in Geospatial Applications
I came across an article that talks about the use of folksonomy in geospatial applications. It reminded me of a post that I have written. Today folksonomy is in the roots of many interesting web applications, e.g., flickr and technorati. I agree with the authors on that future geospatial applications could explore similar ideas. For example,
- Geospatial folksonomies perhaps one day could help the standardization of FGDC (Federal Geographical Data Committe) metadata, which is aimed to improve the organization, search and sharing of geospatial information
- Folksonomies may be used by geo-workers to annotate images and layers, so that information can be ranked and sorted.
- RSS feeds could also exploit geospatial folksonomies. News items and blogs that are tagged with geospatial information can be used to develop location-based search engines and directory services.
Few other thoughts that come to my mind.
- How to develop shared ontologies is a big problem in buliding geospatial semantic web applications. Different agencies use different vocabularies. It’s often difficult for them to agree on a shared ontology in the begining. Maybe folksonomy can help to solve this problem — build systems that can accomandate the evoloution of ontologies.
- While folksonomy can help us to build better geospatial applications, geospatial technology can also help to improve the use of folksonomy. For example, geospatial reasoning can improve the quality of search results. Knowing the zip code 90210 is located in Los Angeles, CA., when a user searches for blogs in LA, blogs that are tagged with "90210" will also be returned.
Goolge Moon
Since Google has conquered the mapping of the Earth, it’s working on the mapping of Lunar surface. Google Moon is a project that maps the landing sites of our first travel to the moon on July 20, 1969.
What’s more? Google’s Copernicus Center is hiring.
Geospatial Technology for the Everyday People
During this holiday season, while people are busy with holiday shopping and travels, companies and government agencies are busy with new geospatial applications for the everyday people. For example, both Google Earth and NORAD provide interactive map services that track the journey of Santa Claus.
This is a healthy sign that shows geospatial technology is not only valuable to the secretive government agencies but also to the everyday people.
Quick links:
- Google Earth Santa KML file.
- NORAD tracks Santa (since 1955)
Governments Tremble at Google Earth
People love Google Earth, but their governments may not. New York Times reports that the growing popularity of Goolge Earth has many governments worried. For example,
India, whose laws sharply restrict satellite and aerial photography, has been particularly outspoken. "It could severely compromise a country’s security," V. S. Ramamurthy, secretary in India’s federal Department of Science and Technology, said of Google Earth. And India’s surveyor general, Maj. Gen. M. Gopal Rao, said, "They ought to have asked us."
I believe in the free use of information, including geospatial data. Should new technology enables everyday people to become GIS specialists, that would be great. If geospatial technology can solve many of our everyday problems, there is no reason to keep them behind the closed doors.
Should new technology threaten national security, we will develop new solutions to overcome this problem. That’s how we as a society has advanced in the past, and I believe that’s how we will continue to do so in the future.
A New GPS Device that Does Caching
Mobile computing is a big market for GPS navigation. According to this IHT article, as the price of powerful mobile devices descreses, the demand for GPS-enabled mobile devices will increase.
Signal loss is a major problem for the existing GPS devices. In cities, tall buildings sometimes can break the links between the mobile devices and the satellies.
TeleNav is working on a new technology to solve this problem.
Hassan Wahla, senior director of business development at TeleNav, said the system calculates where a user is and then - based on speed, as determined by an internal accelerometer - indicates where the user is likely to be whenever satellite signals are interrupted.
"If you lose signal while traveling under a bridge or because of a tall building, you keep navigating," Wahla said. "The entire trip is downloaded in the first minute of a trip and is stored on your phone or BlackBerry as you’re driving. If the GPS goes off line, you will continue to be given guidance. It knows your last known location and speed."
Sometimes the easiest way to solve a network connection problem is by caching.
Geospatial Semantic Web Challenges
Prof. Max Egenhofer has written a short paper, "Toward the Semantic Geospatial Web", that discusses some key issues in building a new Web that can exploit geospatial semantics. He believes that in order for Semantic Geospatial Web (or geospatial semantic web as I call it) to take off, it will require the development of standard geospatial ontologies for representing data and standard query languages for accessing data.
I believe standard ontologies and query languages only solve part of the problem. In real world geospatial applications, building standard vocabularies and queries langugaes are the easy part of the tasks. The hard part of the problem is how to integrate mass amount of geospatial data that already exists.
- How can we integrate existing geospatial data without needing to create new databases that basically replicate the existing ones?
- How can we query the semantic knowledge that is fused from heterogenous data sources without needing to know the specific representations of these data sources ?
- How can we track the pedigree and provenence of geospatial data in a Web-based information space in which anyone can say anything about everything?
- How can we faciliate the sharing of different types of geospatial data (images, videos, maps etc) in a Web-based environemnt?
Think Geospatial Semantics Not Maps
In the past, when the term "geospatial" is mentioned, people immediately think digitial maps. Today most people think Google Maps and Google Earth when the same term is mentioned. To me, seeing mapping technology as the sole component of geospatial technology is a nearsighted vision.
Geospatial technology is more than just pretty maps. A recent IDC study shows that the spatial information management industry is undergoing radical technology changes, which is likely to impact many IT ecosystems.
Fundamental shifts in the spatial information management industry include basic changes in the nature of geospatial work, and transitions in the broad IT environment toward easier integration and support for business processes.
The study finds that geospatial data, and not the map, has become the raw resource for creating location-specific information. Therefore, efforts to convert paper maps to digital data have been replaced as geospatial data is used to generate new maps, decisions, and automated processes.
Let me take things one step further. I think a wide adoption of geospatial technology in IT is only the begnning. Some of the most exiciting applications in the future will be the ones that exploit geospatial semantics, not just geospatial data.
The Need for Image Annotation Software
One picture is worth a thousand words. But how do you capture that thousand words in a machine processable format so that the knowledge can be reasoned over, shared, searched, and archived? In the intelligence community, analysts are faced with this problem everyday.
At present, analysts rely on text reports to share intelligence information. Often this makes the sharing of intelligence information very difficult. Let’s take imagery analysis as an example. An analyst typically studies satellie images and analyzes the geographical features that are depicted in these images. Based on his/her knowledge, the analyst attempts to extract useful intelligence information from the analysis. For example, seeing the development of new military arm forces in a previously abandoned nuclear facility in North Korean, the analyst concludes that the country is attempting to reopen the nuclear facility.
Now, let’s assume the analyst writes down his/her conclusion in the report, and passes on this report to some other analysts. Based on the report, why should these analysts believe the author? Why should they believe that there is new military arm forces in the target region? What exactly are those geographical features or changes depicted in the pictures that made the analyst to draw his/her conclusion?
Even if we assume the author did write down a comprehensive description of his analysis, how easy would it be for this report and its content to be searched by different analysts in a later time?
It’s clear that plain text is not the best format for building up machine processable knowledge. To better facilitate this kind of intelligence analysis, there is a need for image annotation tools.
Some interesting image annotation tools and resources:
- PhotoStuff — an application that allows the user to annotate different parts of an image with RDF descriptions.
- iPhotoRDF — an Mac iPhoto plugin that allows the user add RDF annotations
- DOM Image Annotation Guide — a guide that reveals how Flickr builds its photo annotation capability using DHTML and Flash
- ESW Image Annotation Archives — a list of image annotation tools that build on Semantic Web technology
Semantic Representation Matters in GIS
GML is a language that attempts to provide standard vocabularies for sharing and exchanging geospatial information. The definition of the language is very comprehensive. GML can be used to express extremely complex geospatial concepts.
However, GML falls short in being the right language for semantic representation. The root of the problem is that the expressiveness of GML is limited by the expressiveness of the XML language. For example, you can use GML to express a particular time instant. However, it’s no easy to reference this defined time instance from a different context (e.g., in a different document).
On the other hand, if you use RDF/OWL to describe a time instant. It’s very easy to reference the defined instance from a different document. This makes easy for extending the description. For example, in one document you define a time instance (e.g., urn-x:t1), and in the other you describe the same time instant as an instance of some event (e.g. (urn-x:t1, rdf:type, evt:Event)) and specify its a calendar/clock value (e.g., (urn-x:t1, tm:hasCalendarClock, "2005-12-02T12:09:93")).
I believe the ability to represent geospatial semantics is of great importance when building geospatial applications. Not only it will enable applications to share information, but also it will allow applications to better reuse information.
Geospatial Semantic Web
What’s Geospatial Semantic Web? It’s part of the future Semantic Web that helps people and computing machines to discover and share information by exploiting geospatial technology. It’s also a web-centric information space that lowers the barrier for the dissemination of geospatial information by leveraging semantic web technology.
Why should we study Geospatial Semantic Web? Breakthrough technologies often result from the cross-fertilization of technologies that originate from distinctive domains. By studying the Geospatial Semantic Web, we hope to gain new insights on the applications of the Semantic Web technology and how semantics will play a role in future geospatial information systems.
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