1 Challenge the future INSPIRE coverages Modelling Land Use coverages for INSPIRE.

Slides:



Advertisements
Similar presentations
Data Models There are 3 parts to a GIS: GUI Tools
Advertisements

Geographical Information Systems and Science Longley P A, Goodchild M F, Maguire D J, Rhind D W (2001) John Wiley and Sons Ltd 9. Geographic Data Modeling.
WFM 6202: Remote Sensing and GIS in Water Management © Dr. Akm Saiful IslamDr. Akm Saiful Islam WFM 6202: Remote Sensing and GIS in Water Management Akm.
AN ORGANISATION FOR A NATIONAL EARTH SCIENCE INFRASTRUCTURE PROGRAM Information modelling – tools Simon Cox.
Kick-off meeting Tuesday, June 02, 2015 Anders Östman Imad Abugessaisa.
Geographic Information Systems
©2005 Austin Troy. All rights reserved Lecture 3: Introduction to GIS Part 1. Understanding Spatial Data Structures by Austin Troy, University of Vermont.
Lecture 4. Interpolating environmental datasets
New ways to geo-reference and classify spatial data in Annex II & III data specifications Clemens Portele interactive instruments GmbH Drafting Team „Data.
Prepared by Abzamiyeva Laura Candidate of the department of KKGU named after Al-Farabi Kizilorda, Kazakstan 2012.
Spatial Data Model: Basic Data Types 2 basic spatial data models exist vector: based on geometry of points lines Polygons raster: based on geometry of.
Modelling INSPIRE based data specifications for NATURE-SDIplus Co-funded by the Community Programme eContentplus ECP-2007-GEO
EuroGeographics Workshop Network Service, Paris, /06 Download services Olaf Østensen Network Services Drafting Team.
ALKIS-ATKIS modelling using ISO standards Workshop “Standards in action” – Lisbon – Clemens Portele interactive instruments GmbH Trierer.
Grid-based Analysis in GIS
GIS 1110 Designing Geodatabases. Representation Q. How will we model our real world data? A. Typically: Features Continuous Surfaces and Imagery Map Graphics.
Chapter 3 Sections 3.5 – 3.7. Vector Data Representation object-based “discrete objects”
Applied Cartography and Introduction to GIS GEOG 2017 EL Lecture-2 Chapters 3 and 4.
Coverages and the DAP2 Data Model James Gallagher.
1 The NERC DataGrid DataGrid The NERC DataGrid DataGrid AHM 2003 – 2 Sept, 2003 e-Science Centre Metadata of the NERC DataGrid Kevin O’Neill CCLRC e-Science.
Mapping between SOS standard specifications and INSPIRE legislation. Relationship between SOS and D2.9 Matthes Rieke, Dr. Albert Remke (m.rieke,
The OpenGIS Consortium Geog 516 Presentation #2 Rueben Schulz March 2004.
Chapter 3 Digital Representation of Geographic Data.
8. Geographic Data Modeling. Outline Definitions Data models / modeling GIS data models – Topology.
How do we represent the world in a GIS database?
Raster Data Model.
EuroRoadS for JRC Workshop Lars Wikström, Triona Editor of EuroRoadS deliverables D6.3, D6.6, D6.7.
What is Information Modelling (and why do we need it in NEII…)? Dominic Lowe, Bureau of Meteorology, 29 October 2013.
Lecture2: Database Environment Prepared by L. Nouf Almujally & Aisha AlArfaj 1 Ref. Chapter2 College of Computer and Information Sciences - Information.
® GRDC Hydrologic Metadata - core concepts - 5 th, WMO/OGC Hydrology DWG New York, CCNY, August 11 – 15, 2014 Irina Dornblut, GRDC of WMO at BfG Copyright.
GUS: 0265 Applications in GIS Lecture Presentation 1: Vector Data Model and Operations Jeremy Mennis Department of Geography and Urban Studies Temple University.
GIS Data Structures How do we represent the world in a GIS database?
Copyright © 2009, Open Geospatial Consortium, Inc. Modelling Meteorological Observations and Forecast Data as Discrete Coverages for exchange using WFS.
The Geographic Information System of the European Commission (GISCO) By Albrecht Wirthmann, GISCO, Eurostat ESPON.
OGC ® ® Suite of Water Information Standards HIC-11 Tutorial: Standardization of Water Data Exchange WMO/OGC Hydrology Domain Working Group Irina Dornblut,
Geography Markup Language (GML). GML What is GML? – Scope  The Geography Markup Language is  a modeling language for geographic information  an encoding.
Towards Unifying Vector and Raster Data Models for Hybrid Spatial Regions Philip Dougherty.
UNCEEA NYC June 2009 Land Cover and Land Use Classifications in the SEEA Revision Xiaoning Gong (FAO) & Jean-Louis Weber (EEA) Fourth Meeting of.
LUCAS 2006 J. Gallego, MARS AGRI4CAST. Sampling scheme Adaptation of the Italian AGRIT First phase: Systematic sampling of unclustered points (single.
What is GIS? “A powerful set of tools for collecting, storing, retrieving, transforming and displaying spatial data”
Spatial Data Models Geography is concerned with many aspects of our environment. From a GIS perspective, we can identify two aspects which are of particular.
ESA UNCLASSIFIED – For Official Use INSPIRE Orthoimagery TWG Status Report Antonio Romeo ESRIN 15/02/2012.
Serving society Stimulating innovation Supporting legislation INSPIRE Thematic Cluster on Land Cover and Land Use - State of Play.
Serving society Stimulating innovation Supporting legislation Web Coverage Services (WCS) Thematic Cluster #3 Jordi Escriu Facilitator.
Bavarian Agency for Surveying and Geoinformation AAA - The contribution of the AdV in an increasing European Spatial Data Infrastructure - the German Way.
Introduction to Geodatabases
Experience Transforming Coverage-data Jordi Escriu.
U.S. Department of the Interior U.S. Geological Survey WaterML Presentation to FGDC SWG Nate Booth January 30, 2013.
Michael Lutz INSPIRE MIG-T meeting #38 Ghent March 2017
Facilitator Thematic Cluster #3
Physical Structure of GDB
Geographic Information Systems
Encoding National OI datasets into INSPIRE specifications
Alternative encodings
Spatial Data Models Raster uses individual cells in a matrix, or grid, format to represent real world entities Vector uses coordinates to store the shape.
Pushing implementation of European coverage data and services forward
Point of the agenda LUCAS 2008/2009 : the Core Survey
Statistical surfaces: DEM’s
Data Queries Raster & Vector Data Models
Item 5.1 of the agenda Preliminary results of LUCAS 2009 Part II
Eurostat E-1 - Structural Statistics, Agriculture
Schema translation and data quality Sven Schade
Session 3: Information Modelling and Information Communities
Point 3.3 of the agenda Future actions and strategy
INSPIRE Directive & LUCAS: coordination of activities
Proposal of a Geographic Metadata Profile for WISE
Land Cover and Land Use Statistics
Finnish experiences in deriving CORINE land cover information
Land Use/Cover Area Frame Statistical Survey
Geographic Information Systems
Presentation transcript:

1 Challenge the future INSPIRE coverages Modelling Land Use coverages for INSPIRE

2 Challenge the future Overview Land Use in INSPIRE Introduction to coverages Land use modeling Land use coverages

3 Challenge the future INSPIRE: Land Use My task: Make an information model for Land Use according to INSPIRE rules Definition of Land Use: Territory characterised according to its current and future planned functional dimension or socio–economic purpose (e.g. residential, industrial, commercial, agricultural, forestry, recreational). Ground rule of land use experts: Each piece of land has exactly one land use category! No gaps (category UNUSED) no overlap (category MULTIPLE LAND USE)  Land Use is a Coverage!

4 Challenge the future Coverages feature that acts as a function to return values from its range for any direct position within its spatial, temporal or spatiotemporal domain In modeling a coverage is defined by defining a type for its domain and range

5 Challenge the future Main characteristic of Coverage Continuous (useful interpolation in range values) Discrete (interpolation on range is useless)

6 Challenge the future Coverage implementation Different implementations Raster/Grid Topological Structure TIN Collection: Points Lines + interpolation method Polygons Choice depends on: The way the data is acquired The way the data is used Storage considerations

7 Challenge the future Raster Coverage Continuous Discrete Efficient encoding in files: -Implicit domain geometry -Range values can be compressed

8 Challenge the future Topology Coverage

9 Challenge the future TIN Coverage

10 Challenge the future Point coverage implementation ContinuousDiscrete

11 Challenge the future Line coverage implementation ContinuousDiscrete

12 Challenge the future Polygon coverage implementation ContinuousDiscrete

13 Challenge the future Categorizing land use Coverages Coverages are always discrete (no interpolation) Land use is a discrete coverage in all member states implemented as: Raster coverage Polygon coverage Point coverage (LUCAS)

copyright Eurostat 2011 LUCAS: Land cover dataset for Europe points LAND COVER classes 1 ARABLE LAND 2 PERMANENT CROPS 3 GRASSLAND 4 WOODED AREAS AND SHRUBLAND 5 BARE LAND, RARE VEGET. 6 ARTIFICIAL LAND 7 WATER First phase sample for stratification: orthophoto interpretation 2km squared grid Ground survey Parameters Land cover Land use pictures etc. Sample of around 260,000 pts Second phase sample: in-situ data collection

15 Challenge the future LUCAS (Land use/cover area frame survey

16 Challenge the future Land Use Range The classification value is a complex object  Land Use is always a discrete coverage

17 Challenge the future What does INSPIRE offer In the Proposed Changes to the Generic Conceptual Model and Encoding Guidelines one chapter about coverages Based on ISO Coverages and ISO Two coverage hierarchies: 1.Coverage-As-Polygon-Value-Pair 2.Coverage-By-Domain-And-Range

18 Challenge the future Discrete Coverage Geometry Value Pairs Model Discrete point coverage Discrete polygon coverage

19 Challenge the future Coverage by Domain and Range model Discrete coverage here

20 Challenge the future Coverage by Domain and Range Discrete point coverage Discrete surface coverage Discrete grid coverage This shared superclass I chose as base class for Land Use

21 Challenge the future Land Use Coverage as a DiscreteCoverage

22 Challenge the future Now the encoding part. The UML diagram model is automatically translated to a GML application schema.

23 Challenge the future Implementation of Coverage by Domain and Range Four options for Range: ValueArray homogeneous arrays of primitive and aggregate values. AbstractScalarValueList List of scalar values DataBlock Tuples of values are stored as a long list of Comma Separate Values. File Actual data is stored in a file outside GML Not what I want What I want but not the way I want it. Might be right for gridded data. So I went for a Coverage by Geometry Value Pairs

24 Challenge the future Implementation of Coverage by Geometry-Value-Pairs It works but…. Hi Wilko, I had a quick look and there are quite a number of issues in the XML so I would suggest not to present it as a sample template. I am also convinced that the coverage representation is not the right way, but that is a separate issue.

25 Challenge the future Conclusions / Limitations INSPIRE coverage model is unclear: Why do we have two types of coverages in the top of the hierarchy? INSPIRE coverage implementation wrong: Why do I have to provode a domain and range if I want to encode a Coverage- by-geometry-value-pair? Now I have GML but how to serve it Efficiently (WCS???) Time for a major revision of coverages in: INSPIRE, OGC and ISO/TC 211