Geospatial Meaning

We have entried in the last year of CINeSPACE project.

Mans Shapshak has written an excellent article in Directions Magazine about the project entitled Combining GIS and Semantic Technology to Create a Cultural Visualizer, which has been indexed by the ACM TechNews. Our work has also been reviewed by Roland Piquepaille in his Emerging Tech blog, with the article Discovering Venice with a CINeSPACE device.

Who doesn’t want to experience a CINeSPACE device???

The early attempts to store geographic data in standard relational tables failed. This issue gave rise to spatial databases, which extent the relational model providing specific representations for geospatial data.

As the number of geographic features in a database grew, it became evident that it was imposible to store all the relationships between geographic entities in a large GIS. So distances, intersections, centroids, etc. are easyly computed with built-in functions. Questions like the depicted in the image could be seen as real-time spatial inferencing.

Is featureA within a 5km distance from the border of any water body feature?

When developing spatial reasoning support for Semantic Web we find the same problems. Description logic standards as OWL don’t provide specific spatial built-ins to enable real-time spatial inferencing or querying.

So we need the preprocessing of all spatial-related statements using spatial specific algorithms (or GIS) before adding them to the semantic KB or we need the adding of some kind of spatial layer that combines spatial queries with semantic queries to extent DL expressivity.

We use both approaches in our spatial model for CINeSPACE project:

• A preprocessing of fuzzy spatial relationships such as mereology or neighbourhood, that are added to the KB. This is an feasible approach while the number of spatial elements remains low (hundreds of elements for each city) and the spatial extent of polygons remains constant (non-monotonicity will be reviewed in a future post).
• A spatial layer used for dynamic queries, such as “Retrieve me Geoconcepts I am looking at”, where geoconcept is used to name any entity with an inherently or indirectly associated spatial dimension. SPARQL queries are used over the spatial results to refine the answer.

The need of extra spatial computation limits the success of Geospatial Semantic Web in terms of GIS professionals, who see Semantic Web as a complex world with not great benefits for dealing with space. A step forward spatial integration with DL logics. We are looking for researchers and people interested in this integration. Feel free to contact!!!

We have developed a new Geospatial ontology within the CINeSPACE project. The main idea behind this ontology is a more ample model of the spatial dimension. Some concepts, such as country, mountain or building are inherently spatial. But there exists an important number of concepts that are not physically geographic, although they are commonly related to certain areas. Some examples could be human activities as going shopping. Also events and parties or famous personalities are often related to a spatial extent. We have introduced the term “geoconcept” to name any entity with an inherently or indirectly associated spatial dimension. Some characteristics of the ontology are:

• It reuses existing standards, such as GeoOWL and Geonames feature type hierarchy.
• Its expressivity is OWL Lite. Fuzzy logic extensions were planned for spatial relationships, so simplicity is important to guarantee the decidability.
• A rough set of spatial relationships is defined. As the spatial extent of abstract concepts is vague or uncertain, the RCC8 model can’t help with this modeling. Mereologic and neighbourhood properties are defined.
• Spatial common reasoning is strongly related to sight sense. Many times we reference our position with what we see, although we don’t be exactly in that place. This effect is clearly seen in Geotagging processes, when sometimes the position of the photo’s target is provided, but other times the point where the photo was taken is recorded. We call this the source-target problem and we have added a new spatial relationship, hasNiceViewsOf, to model this problem.
• Polygons centroid is added to the geometry part.
• While complete fuzzy reasoners are developed, a simple model is provided to deal with different degrees of uncertainty, adding several fuzzy subproperties to spatial relationships. A set of SWRL rules is optionally added to manage fuzzy transitivity.

An skeleton of the ontology is depicted in the following figure:

Two versions can be used: Geoconcepts Ontology v1.2, and v1.2 SWRL enhanced model.

Finally we have a multimedia presentation of FP6 CINeSPACE project, Experiencing Urban Film and Cultural Heritage while on the move. How to deal with spatial dimension in a dinamic application is a new challenge for all of us Geospatial researchers. In a few years, nobody will visit Venice, San Sebastian, Glasgow or Pamplona without a CINeSPACE device!!!

CINeSPACE, experiencing urban film and cultural heritage while on the move, is a 6th Framework Programme european project in its second year. One of its main objetives is providing a rich media delivery platform for distributing specific, location-based urban, cultural and film information. The access to the content is semantically enhanced, so information can be filtered according to the context or user profiles.

As part of this project we are studing how humans interact with our physical context. The scenario changes when humans interact with the context, and with the multimedia available in that context. To model his scenario we should consider not only where we are but also where we are looking at.

We have analyzed geotagging process in social information spaces and found that there exists an ambivalent meaning for geographic points in such process. The geographic lat/long usually refers to the target of the video or photo, but sometimes the place where the photo was taken from is recorded. We call this issue the source-target problem in geotagging processes. Again, location and sight concepts are merged.

We have devised Geoconcepts ontology, which attempts at exploding the source-target problem. The results of this research will be presented at ICGIS 2008 conference.

Several new proposals are focusing on the “vision aware” context, and thanks to image recognition, providing related information. Some days ago Mans Shapshak introduced us Photosynth, a virtual 3D photo browser. It’s a pretty cool tool, which allows walking or flying through a virtual scene to see photos from any angle. But one thing is getting this working in a PC, and a different one to incorporate it to a mobile device.

Eyephone, in the other side, makes a realistic proposal for mobile device leaving all processing costs to server side. A demo is showed below. Using advanced object recognition from SuperWise Technologies they take as an input the GPS position, a photograph taken by the user, and the relative angle of vision to provide information about the location the user is looking at. You will think, It is perfect! Well, it would, if they had a prototype in the market…

We have made a small step in order to integrate vision and location to geospatial semantics, but more research is needed. Both meanings should be taken into account in systems which provide multimedia content while on the move, as in CINeSPACE.