Monthly Archives: January 2010

RSS GeoTagger Nijmegen

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The municipality of Nijmegen maintains an RSS Feeder for information it publishes. Now it wants to provide a spatial selection service to  local content providers and citizens.  The information behind the RSS Feeder is not yet geocoded, so the service must try to extract the geographic location from the content of the published information.

Nijmegen started by creating a simple list of named places such as neighbourhoods, parks and other well known locations. The service then scans the published text for names from this list and adds a geographic tag for each name found.  This scheme worked quite well, but as the size of the list grew, it became slower while the number of newly found names in documents was increasing only very slowly. This is when Nijmegen decided to ask Geodan to have a look at the problem.

The most obvious way to improve the geotagging process was to increase the list of named locations and create indexes for search. Nijmegen has a complete list of all addresses available in their ‘BAG-database’. They also created a list of well known ‘points of interest’ from other sources. Geodan combined these sources into a hierarchical database where larger geographic entities are ‘parents’ of smaller entities. This way, the province of ‘Gelderland’ becomes the parent of the municipality of ‘Nijmegen’ which itself is parent of the stadsdeel of ‘Dukenburg’ etc. The database contains geographic entities from the country level down to the level of (well known) individual buildings.

Published documents are scanned for names in this hierarchical database and tagged with the corresponding geographic entities. Users can now select these documents using these tags.  When users supply the name of the area of interest, this name is converted to a tag in the same hierarchical database. Now the database can perform an indexed query for documents that have tags equal to or inside the supplied tag.

Since the documents are now geotagged, the RssFeed could in principle be displayed on a map. This is possible by extending the RssFeed to GeoRss. Among others, Google Maps has the possibility of directly displaying a GeoRss Feed on the map. To see how this works, see example “d” below

Some results to be viewed online:

a. A Nijmegen RSS feed for press releases: http://www2.nijmegen.nl/RSS/persberichten

b. The same feed filtered for info about stadsdeel  ’Dukenburg’: http://geotagger.geodan.nl/FilterRssFeed/FilterRssFeed?toponiemen=dukenburg&url=http://www2.nijmegen.nl/RSS/persberichten

c. The same feed filtered for point of interest ‘Het Valkhof’: http://geotagger.geodan.nl/FilterRssFeed/FilterRssFeed?toponiemen=het+valkhof&url=http://www2.nijmegen.nl/RSS/persberichten

d. The result can be displayed on a map as GeoRss.
Visit http://maps.google.nl and in the “Search in Maps” field enter the following URL for Dukenburg articles:

http://geotagger.geodan.nl/FilterRssFeed/FilterRssFeed?toponiemen=Dukenburg&url=http://www2.nijmegen.nl/RSS/persberichten&georss=true

WPVS

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The past three years we’ve been involved in the Tripod project. The goal of Tripod is to create new, easy, ways to find visual media. One of the approaches is to use the geographic metadata stored with pictures nowadays to figure out what is on that picture. Once you know what is on a picture, you could search for instance for ‘Stephanskirche’ and get pictures of that particular church, even without users manually adding tags or captions.

The available metadata of photos is increasing with the number of sensors available in cameras. Already all digital cameras record the focal length, aperture and such. Using information from GPS, compass and accelerometer sensors you can also include the location, direction and roll, pitch, yaw of the photo. With this information you can recreate the photo in a 3D environment. If you have geodata available in that 3D environment, you can return information of the visible features.

Take for instance this picture:

We know the location and the direction of the picture. With a simple GIS operation you can find out that the picture is taken in Bamberg, Germany. However you don’t yet know what’s in the picture. For a start the actual picture is shot at the ‘Obere Seelgasse’, which is two streets away from the church. Also the area is both urban and hilly. This means that you could either see a house in that particular street, or a hill nearby, both blocking the view of the church.

So you need to do a 3D analysis of the image. We have a detailed 3D model of Bamberg and a digital terrain model to be able to calculate which features are in the view. To do this we use the Web Perspective View Service (WPVS) from deegree. This is a proposed OGC standard which allows us to both render a perspective view given location, orientation and field of view and to query geo-databases using locations on the photo.

Using the WPVS we get this image:

WPVS view of the Stephanskirche

For automatic identification, it is easier if each building has one color, this way we can calculate the area occupied by a specific building. So we had to uglyfy the image by removing anti-aliasing and shading. We wrote a service called the Feature Identification Service (FIS) which, given a georeferenced photo, will determine the most prominent features and return a list of visible features. In this case it would tell us that it contains the Stephanskirche in Bamberg.

Stephanskirche