In addition to the Czech Republic, the team was strengthened by two other partners from Finland (Lahti University of Applied Sciences) and Italy (University of Foggia – a partner of the SKIN project (www.shortfoodchain.eu), who added test farm data to the shared table:

https://docs.google.com/spreadsheets/d/14wNsA-NWfZydRj5ulFtfrbXPmlXvQDczXzl8p6vjeSY/edit#gid=1514958348

Using principle of the SPOI (http://sdi4apps.eu/spoi), the farm map was updated:

http://gis.zcu.cz/spoi/viz/farmy/index.html

This application will be introduced as a pilot version during the INSPIRE Hack 2017 (http://inspire.ec.europa.eu/conference2017/kehl).

We are planning to carry on our activities, so we would like to invite other partners to join our team and put your farm data.

Another upcoming Hackathon will held in Pilsen, in the Czech Republic on October 2-3, 2017, where we are going to introduce an enhanced version of this app with additional features.

Pleas find more about the following event: https://kgm.zcu.cz/geomatics-in-projects-2017/hackathon/

Do you want to join this team? Please contact a team leader horakova@wirelessinfo.cz

A database was created in the form of an excel spreadsheet where you can add data – farm name, coordinates, description, contacts, products and other activities
The table for filling in the data is at the following address:

https://docs.google.com/spreadsheets/d/14wNsA-NWfZydRj5ulFtfrbXPmlXvQDczXzl8p6vjeSY/edit?usp=sharing

WirelessInfo, as one of the partners of the SKIN project (www.shortfoodchain.eu), has completed testing data from the Czech Republic – 18 farms that sell products from the farm yard or have some added value.
Using principle of the SPOI (http://sdi4apps.eu/spoi) a sample application was created by West Bohemian University in the form of a map where the data inserted into a table is visualized on the map.
Link to test map here:

http://gis.zcu.cz/spoi/viz/farmy/index.html

Next goals:

Find other partners and get more farm data

The application will be accompanied by a search window

We are looking for partners!!!
Do you want to join this team?

Do you have farms with interesting products in your country or area?
It’s simple to join us, just add farm data to a spreadsheet or contact the Team leader horakova@wirelessinfo.com for more information
The application will be introduced at INSPIRE Hack 2017

More about INSPIRE Hack: https://www.plan4all.eu/updates/inspire-hack-2017-progress-stories/

DataBio developers support the INSPIRE conference through organizing the Open Data, VGI and Citizen Observatories INSPIRE Hack. One of the latest achievements is a connection of three worlds through metadata: INSPIRE world, world of semantics and citizen world.

Lesprojekt, Spacebell and Sintef prepared a demo that shows the following story. Earth Observation data stored at Spacebell applications are described by INSPIRE metadata as requested by legislation. Such metadata are also online converted into the GeoDCAT format in order to support tasks related to semantisation. Moreover, Lesprojekt developed a convertor allowing to transform on-line GeoDCAT into KML and/or GeoJSON. Metadata may then be visualized through Google Earth and/or any GIS platform respecting KML/GeoJSON.

We would like to present through this demo the ideas of opening INSPIRE to IT as well as “wipe off” the border between metadata and data.

This is an update of the linked data publication (OLU, OTM, SPOI and others) team – Dimitri (CZ), Raitis (LV), Sam (PL) and Raul (PL).

The first task for this group was to convert to RDF form OLU and OTM datasets from a PostgreSQL relational database. Because of OLU dataset depends on objects from other datasets (Corine, Urban Atlas, Cadastral Parcels) it also practically meant to convert all those objects to RDF form as well.

For the transformation, first ontologies for each of these datasets were generated. The ontologies generated are (available from https://github.com/FOODIE-cloud/ontology ):

• ttl (namespace: http://w3id.org/foodie/corine )
• urban-atlas.ttl (namespace: http://w3id.org/foodie/atlas )
• ttl (namespace: http://w3id.org/foodie/hilucs )
• ttl (namespace: http://w3id.org/foodie/olu )
• ttl (namespace: http://w3id.org/foodie/otm )

The next step was to carry out the transformation itself. For this process, we used the D2RQ Platform (http://d2rq.org/), which uses mapping files (in RDF) to execute the transformation.

As we are dealing with huge pan-European datasets, not all the data was converted. We have decided to convert the following data:

• Full Hilucs classification
• Corine and Urban Atlas data for EU agriculture related lands (hilucs_code<200) & for main cities in Czech Republic (centers of NUTS3 regions), Poland (agglomeration areas from Urban Atlas) and Spain (agglomeration areas from Urban Atlas)
• OLU data for agriculture related lands (hilucs_code<200) in CZ, PL, ES & for main cities in Czech Republic (centers of NUTS3 regions), Poland (agglomeration areas from Urban Atlas) and Spain (agglomeration areas from Urban Atlas)
• OTM data for CZ, ES, PL; but RoadLinks only for FunctionalRoadClassValue of type: (‘mainRoad’,’firstClass’,’secondClass’,’thirdClass’,’fourthClass’) (see http://opentransportmap.info/OSMtoOTM.html );

After the data was transformed, we loaded it to our triplestore in Virtuoso:

• SPARQL endpoint: https://www.foodie-cloud.org/sparql
• Faceted search browser endpoint: http://www.foodie-cloud.org/fct/

Additionally, we loaded the following datasets:

• SPOI (provided by WRLS)
• NUTS (open dataset)
• Eurovoc (open dataset)
• Emergel (provided by CTIC)

The current statistics (total # of triples in FOODIE triplestore:  701,286,392) are:

Regarding the links, the datasets were generated already with some links. For instance, OLU has links to Urban, Corine and Czech cadaster. SPOI has links to different datasets including dbpedia and others

Hence the next step was to think about possible queries that could show the RDF data and their connections.

Regarding the links, the datasets were generated already with some links. For instance, OLU has links to Urban, Corine and Czech cadaster. SPOI has links to different datasets including dbpedia and others

Hence the next step was to think about possible queries that could show the RDF data and their connections.

PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX virtrdf:    <http://www.openlinksw.com/schemas/virtrdf#>
PREFIX poi: <http://www.openvoc.eu/poi#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT *
FROM <http://www.sdi4apps.eu/poi.rdf>
WHERE {
  ?Resource rdfs:label ?Label .
  ?Resource poi:class ?POI_Class .
  ?Resource geo:asWKT ?Coordinates .
  FILTER(bif:st_intersects (?Coordinates, bif:st_geomFromText("POLYGON
  ((6.11553983198 54.438016608357, 6.95050076948 47.230985358357, 13.36651639448
   47.626493170857, 14.99249295698 54.701688483357, 6.11553983198 54.438016608357)
   )"))) .
}

PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX virtrdf:    <http://www.openlinksw.com/schemas/virtrdf#>
PREFIX poi: <http://www.openvoc.eu/poi#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX olu-instance: <http://w3id.org/foodie/olu/LandUse/22165431>

SELECT *
FROM <http://www.sdi4apps.eu/poi.rdf>
WHERE {
  ?Resource rdfs:label ?Label .
  ?Resource poi:class ?POI_Class .
  ?Resource geo:asWKT ?Coordinates .
  FILTER(bif:st_intersects (?Coordinates, bif:st_geomFromText(?coordinates))) .
  {
      SELECT bif:st_astext(?x) as ?coordinates
      FROM <http://w3id.org/foodie/olu#>
      WHERE {
             olu-instance: geo:hasGeometry ?geometry.
             ?geometry geo:asWKT ?x
      }
  }
}

PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX virtrdf:    <http://www.openlinksw.com/schemas/virtrdf#>
PREFIX poi: <http://www.openvoc.eu/poi#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX olu-instance: <http://w3id.org/foodie/olu/LandUse/22165431>
SELECT count (*)
FROM <http://www.sdi4apps.eu/poi.rdf>
WHERE {
  ?Resource rdfs:label ?Label .
  ?Resource geo:asWKT ?Coordinates .
  FILTER(bif:st_intersects (?Coordinates, bif:st_geomFromText(?coordinates))) .
  {
      SELECT bif:st_astext(?x) as ?coordinates
      FROM <http://w3id.org/foodie/olu#>
      WHERE {
          olu-instance: geo:hasGeometry ?geometry.
          ?geometry geo:asWKT ?x
      }
  }
}

PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX virtrdf:    <http://www.openlinksw.com/schemas/virtrdf#>
PREFIX poi: <http://www.openvoc.eu/poi#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT ?olu
FROM <http://w3id.org/foodie/olu#>
WHERE {
  ?olu a <http://w3id.org/foodie/olu#LandUse> .
  ?olu geo:hasGeometry ?geometry .
  ?geometry geo:asWKT ?x .
  FILTER(bif:st_intersects (?x, bif:st_geomFromText("POLYGON((15.88623046875
  50.66817794949724,17.05078125 50.66817794949724,17.05078125
  49.74711237803693,15.88623046875 49.74711237803693,15.88623046875
  50.66817794949724))"))) .
}

PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX virtrdf:    <http://www.openlinksw.com/schemas/virtrdf#>
PREFIX poi: <http://www.openvoc.eu/poi#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX otm: <http://w3id.org/foodie/otm#>

SELECT DISTINCT ?Resource, ?Label, bif:st_astext(?coordinatesPOIa) as ?coordinatesPOI
FROM <http://www.sdi4apps.eu/poi.rdf>
WHERE {
  ?Resource rdfs:label ?Label .
  ?Resource poi:class <http://gis.zcu.cz/SPOI/Ontology#lodging> .
  ?Resource geo:asWKT ?coordinatesPOIa .
  FILTER(bif:st_within(?coordinatesPOIa,bif:st_geomFromText(?coordinatesOTM),0.00045)) .
  {
    SELECT bif:st_astext(?x) as ?coordinatesOTM
    FROM <http://w3id.org/foodie/otm#>
    WHERE {
      ?roadlink a otm:RoadLink .
      ?roadlink otm:roadName ?name.
      ?roadlink otm:functionalRoadClass ?class.
      ?roadlink otm:centerLineGeometry ?geometry .
      ?geometry geo:asWKT ?x .
      FILTER(bif:st_intersects (?x, bif:st_geomFromText("POLYGON((14.426647
      50.0751251,14.426647 50.07685089,14.43054696 50.07685089,14.43054696
      50.0751251,14.426647 50.0751251))"))) .
      FILTER(STRSTARTS(STR(?class),"firstClass") ) .
    }
  }
}

PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX virtrdf:    <http://www.openlinksw.com/schemas/virtrdf#>
PREFIX poi: <http://www.openvoc.eu/poi#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX otm: <http://w3id.org/foodie/otm#>
PREFIX olu: <http://w3id.org/foodie/olu#>

SELECT DISTINCT ?olu ?hilucs ?source ?municode ?specificLandUse
FROM <http://w3id.org/foodie/olu#>

WHERE {
  ?olu a olu:LandUse .
  ?olu geo:hasGeometry ?geometry .
  ?olu olu:hilucsLandUse ?hilucs .
  ?olu olu:geometrySource    ?source .
          OPTIONAL {?olu olu:municipalCode      ?municode} .
          OPTIONAL {?olu olu:specificLandUse    ?specificLandUse} . 
  ?geometry geo:asWKT ?coordinatesOLU .
  FILTER(bif:st_within(bif:st_geomFromText(?coordinatesPOI),?coordinatesOLU)).
  {
    SELECT DISTINCT ?Resource, ?Label, bif:st_astext(?coordinatesPOIa) as ?coordinatesPOI
    FROM <http://www.sdi4apps.eu/poi.rdf>
    WHERE {
       ?Resource rdfs:label ?Label .
       ?Resource poi:class <http://gis.zcu.cz/SPOI/Ontology#lodging> .
       ?Resource geo:asWKT ?coordinatesPOIa .
       FILTER(bif:st_within(?coordinatesPOIa,bif:st_geomFromText(?coordinatesOTM),0.00045)) .
       {
          SELECT bif:st_astext(?x) as ?coordinatesOTM
          FROM <http://w3id.org/foodie/otm#>
          WHERE {
             ?roadlink a otm:RoadLink .
             ?roadlink otm:roadName ?name.
             ?roadlink otm:functionalRoadClass ?class.
             ?roadlink otm:centerLineGeometry ?geometry .
             ?geometry geo:asWKT ?x .
             FILTER(bif:st_intersects (?x, bif:st_geomFromText("POLYGON((14.426647
             50.0751251,14.426647 50.07685089,14.43054696 50.07685089,14.43054696
             50.0751251,14.426647 50.0751251))"))) .
             FILTER(STRSTARTS(STR(?class),"firstClass") ) .
         }
       }
    }
  }
}

Finally, the final task is to visualize the interlinked data on the map. For instance, here is a map, that shows the OLU polygons colored by the number of SPOI that lie inside them:
http://ng.hslayers.org/examples/olu_spoi/?hs_panel=info&hs_x=1607799.902082933&hs_y=6462976.717926565&hs_z=16&visible_layers=Base%20layer;Land%20use%20parcels

When any OLU polygon is clicked – the information about the polygon and all the SPOI points that lie inside is displayed in a pop-up window.  The pop-up window is browsable i.e. when some object in it is clicked it expands and one can see information about that clicked object.

Two 3D application prototypes created

(24th of August)

The 3D-OLU team, consisting of DataBio project partners, has developed two application prototypes which are going to be presented at the INSPIRE HACK 2017. The first, let’s say a global app (3D-OLU), shows a visualization of Open Land Use in 3D, which can be combined with any web map service of user choice. The second app (Rostěnice Farm) presents the potential of 3D data visualization in a local area of interest.

3D-OLU

This application visualises the Open Land Use dataset on top of the EU-DEM terrain model in a perspective view. Displaying the data in 3D environment helps the user to explore an area of interest in more natural way than a traditional map. Moreover, the user can add a web map service of his choice (as long as the WMS is in WGS84 coordinate system) to make custom 3D mash-ups. The technical details can be found here.

Rostěnice Farm

This application visualises geographical data in a 3D environment. This can help a farmer to better understand the farm fields. The farmer can explore the relation of the yield potential to the topography, slope, orientation and topography wetness index in his/her field. The farmer can check the parts of fields with steep slopes and how the machinery deals with them (checking the machinery tracklogs). The technical details can be found here.

The application uses the digital terrain model of fifth generation and the digital surface model of first generation produced by ČÚZK. This application is not yet publicly available, as we are awaiting an authorization from ČÚZK. At least, there is a video presentation.

At the beginning we needed to convert to RDF form OLU and OTM datasets. Because the OLU dataset depends on objects from other datasets (Corine, Urban Atlas, Cadastral Parcels) it also practically meant to convert all those objects to RDF form also. So ontologies for all those objects have been proposed and after, there were written and run conversion scripts. As we are dealing with huge pan-European datasets, not all the data was converted. We have decided to convert just the data from some selected cities in Czech Republic (centers of NUTS3 regions), Poland (agglomeration areas from Urban Atlas) and Spain (agglomeration areas from Urban Atlas). After we had all data in RDF we started to think about possible queries that could show interlink the data. For instance, here is a map, that shows the OLU polygons colored by the number of SPOI that lie inside them:
http://ng.hslayers.org/examples/olu_spoi/?hs_panel=info&hs_x=1607799.902082933&hs_y=6462976.717926565&hs_z=16&visible_layers=Base%20layer;Land%20use%20parcels

When any OLU polygon is clicked – the information about the polygon and all the SPOI points that lie inside is displayed in a pop-up window.  The pop-up window is browsable i.e. when some object in it is clicked it expands and one can see information about that clicked object.

Also we started to work on some queries that would bring together three mentioned datasets: OLU, OTM and SPOI. For instance, can be query such as: ‘Show me all the land parcels that have hotels and that lie not more than 50 meters away from the major highway?’ . This query does this job. It returns Open Land Use objects that satisfy abovementioned criteria in part of Prague city center around IP Pavlova metro station.

Now we are working on making some graphical interface were user could customize certain parameters from such query (i.e. type of SPOI, type of the road, buffer distance etc).

This is an update from the 3D Open Land Use project.

Integration of Cesium plugin to HS Layers NG

The project was divided into three phases. Firstly, an integration of Cesium into HS Layers NG was realized – this can be tracked on Github See folders examples/3d-olu and components/cesium at https://github.com/hslayers/hslayers-ng repository. There was also created a local copy of Cesium server (http://cesium.cenia.cz/) for better performance.

Visualization of Open Land Use on top of EU-DEM

The second phase consisted of visualization of Open Land Use in a perspective view, to allow a 3D exploration of the dataset. When exploring the  default cesium terrain source, the team realized that the 30 meter resolution EU-DEM is already incorporated.Therefore the terrain source issue was off the table, as the team is not aware of any other more detailed but open source terrain source available at the moment. The “only” remaining issue to solve was then to visualize the Open Land Use WMS on top of the EU-DEM terrain. As using just one WMS leads to slow response times, the two following optimizations were made:

• The land use is rendered from CORINE data for less detailed zooms. Talking technically: till WMS tiles of smaller scale than 1:200 000 are requested by Cesium. The detailed Open Land Use Map is displayed just in closer zooms.
• For the closer zooms, land use polygons of just visible LAU2 municipalities are shown. The technical realization is as follows:
  • Bounding boxes of LAU2 municipalities are confronted with an observer’s line of sight. ID’s of positive matches are retrieved.
  • Only Open Land Use polygons of selected LAU2 municipalities are displayed.

The resultant application is depicted on following figure and is accessible at the address http://ng.hslayers.org/examples/3d-olu:

Visualization of yield potential on top of detailed terrain

Third phase of 3D Open Land Use hack activity was focused on visualization in higher detail. Therefore a visualization of yield potential from a particular farm was chosen as a use case. A data about yield potential was calculated for Rostěnice farm (a DataBio project agriculture pilot area). This data was then placed on top of digital terrain and digital surface models produced as services by ČÚZK and a web application showing the data on digital surface model was created. Due to licensing issues there is no public link to the application, but we hope in successful agreement with data provider. Meanwhile, you can see the results at least in following video:

Future development and further ideas

The majority of the initial ideas have been achieved, but there are new ideas coming, such e.g. as:

• Displaying detailed information about the OLU categories on click.
• Exploring information about the correlation of yield potential value, local slope and aspect.
• Displaying machinery paths on field.
• Calculation of (at least rough) yield potential or fruit type detection from satellite data.
• …

Thus, our team is still open to new developers wanting to join.