The document discusses using smartphone and tablet technologies to integrate INSPIRE geospatial data infrastructure. It describes projects like HABITATS that modeled environmental data themes in compliance with INSPIRE. Different use cases for regional and global data are examined. Basic and advanced techniques for transforming data between regional models and INSPIRE are outlined. The document also introduces the HABITATS Reference Laboratory concept for supporting cross-scenario applications and testing local data sharing. It identifies opportunities to make INSPIRE data more accessible through mobile and social applications.

1. INSPIRE IN POCKET
Is it possible to Integrate Smartphone’s
and Tablets with INSPIRE infrastructure?
Help Service - Remote Sensing
Štěpán Kafka, Karel Charvát

2. From Habitats towards INSPIRE in
Pocket
• The HABITATS project focused on the adoption
of INSPIRE standards through a participatory
process to design and validate environmental
geo-spatial data, metadata, and service
specifications with European citizens and
businesses.

3. Habitats Data and Metadata Modeling
• Define data and metadata models for the
following INSPIRE data themes:
– 16. Sea regions
– 17. Bio-geographical regions
– 18. Habitats and Biotopes
– 19. Species distribution
• The results should be in compliance with
INSPIRE directive and possible INSPIRE data
models.

4. HABITATS benefits from participation
in INSPIRE TWG
 Habitats project conceptual data model was
prepared short period before INSPIRE TWG
was established
 All HABITATS activities related to INSPIRE data
themes after establishment of TWG was
reported in TWG
 Feedback from TWG decisions guaranteed
complete harmonization of HABITATS
conceptual data models with INSPIRE
corresponding Annexes

5. Data usage use cases
• Regional data are used regionally
• Global data are used regionally
• Regional data are used cross-regionally (here works
INSPIRE)
• Regional data are used globally (here works INSPIRE)
• Global data are used globally (here works INSPIRE)

6. Regional data used regionally
• There is not direct requirement for INSPIRE
data models
– Local data models could be wider
– Local data models reflect regional needs and also
regional decision processes
– If data are not shared outside of region (but in
many cases it is necessary), in principle global
standards are not needed
– Standards are needed in case of more data
suppliers, to guarantee data consistence

7. Global data used regionally
Global data are in some content something like de facto standards
In some cases it is necessary to be possible transform data into
such models, which is required by regional decision processes
The global model has to cover regional decision needs (GMES case
for example)
Open problems:
 the transformation happens either on fly or as pretransformed
data snapshot
Language problem in the case 'on fly'

8. Regional data used cross regionally
In the case of cross border regions data harmonization
faces extreme challenges.
In many cases, for example tourism, we need to deal
simultaneously with several INSPIRE related data
themes. This is much more complex task than single
data theme case.
In some applications data model could be broader than
corresponding INSPIRE definition.
Open problem – how to manage multi lingual problems

9. Regional data used globally
• Probably most relevant case for INSPIRE data model
• The idea is to combine several local data sets into
single standardized data set
• The regional data has to be transformed (in many
cases simplified) into global data model
• Relevant cases are tourism, transport, education,
research, environment protection, risk management,
strategic decision
• Language problem

10. Global data used globally
• Global data are either standard or de facto
standard.
• It is expected that in the case of public sector data
INSPIRE compliance will be guaranteed.
• Concrete application areas may require specific
transformation. Transformation could be based on
Feature Encoding or Styled Layer Descriptor (SLD)

11. Basic transformations
Basic script for automated
data merge of data
maintained as multiple files
Basic data merge of data
maintained in multiple file
structure using free and
opensource desktop
application
Two solutions for data merging

12. Basic transformations
Basic SQL script for
geometry extraction from
multigeometry, further can
be used as one setep in
larger data transformation
process
Basic geometry extraction
from multigeometry using
free and opensource
desktop application
Two solutions for data extracting

13. Advanced transformations
Data Specifications 2.0
Habitats and biotopes
Harmonization
FMI Data

14. Advanced transformation – scheme (1)
Open SHP file
and its scheme
Save final
SHP file
Reclassification
FMI → EUNIS
New data
model

15. New data model (1)
Existing FMI data model +
referenceHabitatTypeId: CharacterString
referenceHabitatTypeScheme: ReferenceHabitatTypeSchemeValue
localSchemeURI: URI
localNameValue: CharacterString
geometry: polygon
referenceHabitatTypeId: eunis_value
referenceHabitatTypeScheme: eunis
localSchemeURI: link_to_FMI_classification
localNameValue: FMI_classification_value

16. Data model mapping (1)

17. Taxonomy – reclassification
(FMI → EUNIS) (1)
0 Pine → G3.42,"4","Middle European *Pinus sylvestris+ forests"
1 Oak → G1.87,"4","Medio-European acidophilous [Quercus] forests"
2 Beech-oak → G1.82,"4","Atlantic acidophilous [Fagus] - [Quercus] forests"
3 Oak-beech → G1.82,"4","Atlantic acidophilous [Fagus] - [Quercus] forests"
4 Beech → G1.6,"3","*Fagus+ woodland"
5 Fir-beech → G4.6,"3","Mixed [Abies] - [Picea] - [Fagus] woodland"
6 Spruce-beech → G4.6,"3","Mixed [Abies] - [Picea] - [Fagus] woodland"
7 Beech-spruce → G4.6,"3","Mixed [Abies] - [Picea] - [Fagus] woodland"
8 Spruce → G3.1D,"4","Hercynian subalpine *Picea+ forests"
9 Dwarp pine → F2.45,"4","Hercynian *Pinus mugo+ scrub"

18. Reclassification (1)

19. FMI Data
INSPIRE / Habitats
Data

20. Advanced transformation (2)
CQL
filter

21. Advanced transformation (3)
Ontology
Description
Nomenclatures
Derived
transformation
rules

22. Description in ontology (3)

23. Reference Laboratory
• The HABITATS Reference Laboratory is a central hub
with the support of global data, but also supporting
cross scenarios implementations, and the HABITATS
pilot applications, as implementations of single
HABITATS pilot cases, which will also be used for
testing the sharing of local data and metadata.

24. Reference of RL to Pilots

25. RL Architecture

26. RL advanced principles
• RL include all basic Geoportal Functionality, but
– Support work with Maps not only with services
– Extending of INSPIRE services – usage of KML
– Include already possibilities for Open Linked Data
– Embeded functionality

27. RL approach

28. RL approach

29. RL Approach

30. WordPress GeoBlog

32. IDEAS
• INSPIRE is not commonly known
• Heavy formats (GML)
• For government …
• INSPIRE data may be widely used
• Portal is not everything
• Need to have bridge (apps) making it accessible
• Social apps may contribute INSPIRE !
• Mobile technologies may help to disseminate

33. SOAP, CSW, WFS, GML …
JSON, KML, …
OFF-LINE / ON-LINE use
Special apps rather than „portal“
INSPIRE infrastructure

35. View services
Definujte vlastní zkratky!

36. CADASTER PARCELS
Definujte vlastní zkratky!

37. KML resources

38. FIELD Editing
Batch post
ADAPTOR
WMS / WFS /KML /

39. Thanks for attention
Karel Charvat
charvat@hsrs.cz