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From OpenStreetMap data to Land
Use/Land Cover maps: experiments
and first results
Cidália Fonte(1,2), Marco Minghini(3), ...
Summary
• Introduction
• OpenStreetMap Data
• Urban Atlas/Corine Land Cover Nomenclature
• Methodology
• Case Studies
• Co...
Introduction
• Land Use/Cover Maps (LUCM)
• Fundamental for many areas
• Produced through the classification of satellite ...
OpenStreetMap (OSM) data
• OSM is the richest, most diverse, most complete and
often most up to date geospatial database o...
• Project of the Global Monitoring for Environment and Security (GMES) -
European Environment Agency (EEA)
• Aims to provi...
• Project of the Copernicus Land Monitoring Service
• Provides a consistent, comparable, pan-European land cover product
(...
Nomenclatures
• The nomenclatures of the UA and CLC are clearly compatible. The main
difference is that more detailed urba...
2. Agricultural, semi-natural areas, wetlands 2.Agricultural areas 2.1 Arable land 2.1.1 Non-irrigated arable land
2.1.2 P...
Methodology to convert OSM data into
UA/CLC nomenclature
The main steps of the methodology are:
• Step 1: Associate the ke...
Methodology (workflow – example: class 1.2)
Methodology
Solving inconsistencies
• Aspects considered to create a hierarchical approach
• Elements of the landscape tha...
Methodology (solving inconsistencies:
priority level 1)
Level of
priority
UA
Class
UA class name CLC class CLC class
name
...
Methodology (priority level 2)
Level of
priority
UA Class UA class name CLC
class
CLC class name
1 1.2 Industrial, commerc...
Methodology (application workflow)
Case studies
Paris area
OpenStreetMap
Case studies
Paris area
OpenStreetMap
OSM extracted data Corine Land Cover
Case studies
London area
OpenStreetMap
Case studies
London area
OpenStreetMap
Conclusions and future work
• Capabilities
• LULC maps with a level of detail comparable to UA and CLC can be obtained
• A...
Thank You!
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From OpenStreetMap data to Land Use/Land Cover maps: experiments and first results

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Presentation at EO OpenScience 2016

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From OpenStreetMap data to Land Use/Land Cover maps: experiments and first results

  1. 1. From OpenStreetMap data to Land Use/Land Cover maps: experiments and first results Cidália Fonte(1,2), Marco Minghini(3), Vyron Antoniou(4), Linda See(5), Joaquim Patriarca(2), Maria Antonia Brovelli(3), Grega Milcinski(6) (1) Dep. of Mathematics, University of Coimbra, Coimbra, Portugal (2) INESC Coimbra, Coimbra, Portugal (3) Politecnico di Milano, Department of Civil and Environment Engineering, Como, Italy (4) Hellenic Army Academy, Greece (5) Ecosystems Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria (6) Sinergise, Ljubljana, Slovenia COST actions TD1202 – Mapping and the Citizen Sensor and IC1203 – ENERGIC
  2. 2. Summary • Introduction • OpenStreetMap Data • Urban Atlas/Corine Land Cover Nomenclature • Methodology • Case Studies • Conclusions and future work
  3. 3. Introduction • Land Use/Cover Maps (LUCM) • Fundamental for many areas • Produced through the classification of satellite imagery • This however has limitations • Volunteered Geographic Information (VGI) • OpenStreetMap (OSM) • OSM dataset has LUC data • It has been shown that it is possible to create a LUCM from OSM data (Jokar Arsanjani et al., 2013) • This process has difficulties and limitations • Aim • Propose an automated methodology to convert OSM data into a LUCM using Urban Atlas (UA)/Corine Land Cover (CLC) nomenclatures
  4. 4. OpenStreetMap (OSM) data • OSM is the richest, most diverse, most complete and often most up to date geospatial database of the world • OSM database • Collection of vector data objects (points, lines, polygons) • Each object must have at least one attribute • OSM attributes are known as “tags” • A tag is the combination of a “key” and a “value” • Key: highway; value: motorway • Additional tags can further characterize the feature • OpenStreetMap data can be downloaded in several ways: • using tools available in some GIS software • from the OSM web page indicating a Bounding Box • using Geofabrik (http://download.geofabrik.de/) • Others (OSM Planet file, Overpass API and OSM extracts)
  5. 5. • Project of the Global Monitoring for Environment and Security (GMES) - European Environment Agency (EEA) • Aims to provide high resolution Land Use Land Cover maps for Pan- European regions • with more than 100 000 habitants • Freely available through the European Environmental Agency website: • (http://www.eea.europa.eu/data-and-maps/data/urban-atlas) in vector format • Minimum mapping units: • 0,25ha (0.0025 km2) for area features (urban), 1 ha (rural) • 100m for linear features • Positional accuracy is ±5m • The classification of UA is made considering a nomenclature separated into levels Urban Atlas (UA)
  6. 6. • Project of the Copernicus Land Monitoring Service • Provides a consistent, comparable, pan-European land cover product (http://land.copernicus.eu/pan-european/corine-land-cover). • Freely available through the European Environmental Agency website (http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012) in vector format • Minimum mapping units: • 0,25km2 for area features • 100m for linear features • The positional accuracy is 100m and the overall thematic accuracy is greater than 85%. • The classification of CLC is made considering a nomenclature separated into levels (there are 44 land cover classes in the most detailed level). Corine Land Cover (CLC)
  7. 7. Nomenclatures • The nomenclatures of the UA and CLC are clearly compatible. The main difference is that more detailed urban classes can be found in the UA (even without considering the fourth level) and other land cover classes in CLC are more detailed than in the UA, which reflects differences in their overall purpose. Urban Altas nomenclature CORINE Land Cover nomenclature Level 1 Level 2 Level 3 Level 1 Level 2 Level 3 1.Artificial Surfaces 1.1 Urban Fabric 1.1.1 Continuous urban fabric 1.1.2 Discontinuous urban fabric 1.1.3 Isolated Structures 1.Artificial Surfaces 1.1 Urban Fabric 1.1.1 Continuous urban fabric 1.1.2 Discontinuous urban fabric 1.2 Industrial, commercial, public, military, private and transport units 1.2.1 Industrial, commercial, public, military and private units 1.2.2 Road and rail network and associated land 1.2.3 Port areas 1.2.4 Airports 1.2 Industrial, commercial, public, military, private and transport units 1.2.1 Industrial or commercial units 1.2.2 Road and rail network and associated land 1.2.3 Port areas 1.2.4 Airports 1.3 Mine, dump and construction sites 1.3.1 Mineral extraction and dump sites 1.3.3 Construction sites 1.3.4 Land without current use 1.3 Mine, dump and construction sites 1.3.1 Mineral extraction 1.3.2 Dump sites 1.3.3 Construction sites 1.4 Artificial non-agricultural vegetated areas 1.4.1 Green urban areas 1.4.2 Sports and leisure facilities 1.4 Artificial non-agricultural vegetated areas 1.4.1 Green urban areas 1.4.2 Sports and leisure facilities
  8. 8. 2. Agricultural, semi-natural areas, wetlands 2.Agricultural areas 2.1 Arable land 2.1.1 Non-irrigated arable land 2.1.2 Permanently irrigated land 2.1.3 Rice fields 2.2 Permanent crops 2.2.1 Vineyards 2.2.2 Fruit trees and berry plantations 2.2.3 Olive groves 2.3 Pastures 2.3.1 Pastures 2.4 Heterogeneous agricultural areas 2.4.1 Annual crops associated with permanent crops 2.4.2 Complex cultivation patterns 2.4.3 Land principally occupied by agriculture, with significant areas of natural vegetation 2.4.4 Agro-forestry areas 3. Forests 3. Forest and semi natural areas 3.1 Forests 3.1.1 Broad-leaved forest 3.1.2 Coniferous forest 3.1.3 Mixed forest 3.2 Scrub and/or herbaceous vegetation associations 3.2.1 Natural grasslands 3.2.2 Moors and heathland 3.2.3 Sclerophyllous vegetation 3.2.4 Transitional woodland-shrub 3.3 Open spaces with little or no vegetation 3.3.1 Beaches, dunes, sands 3.3.2 Bare rocks 3.3.3 Sparsely vegetated areas 3.3.4 Burnt areas 3.3.5 Glaciers and perpetual snow ------- 4. Wetlands 4.1 Inland wetlands 4.1.1 Inland marshes 4.1.2 Peat bogs 4.2 Maritime wetlands 4.2.1 Salt marshes 4.2.2 Salines 4.2.3 Intertidal flats 5. Water 5. Water 5.1 Inland waters 5.1.1 Water courses 5.1.2 Water bodies 5.2 Marine waters 5.2.1 Coastal lagoons 5.2.2 Estuaries 5.2.3 Sea and ocean Urban Altas nomenclature CORINE Land Cover nomenclature Level 1 Level 2 Level 3 Level 1 Level 2 Level 3
  9. 9. Methodology to convert OSM data into UA/CLC nomenclature The main steps of the methodology are: • Step 1: Associate the key/value combinations available in OSM with the LULC classes in the LULC product of interest, in this case the UA or CLC • Step 2: Choose any user defined values that are necessary for the processing • Step 3: Run the conversion process • Step 4: Eliminate inconsistencies such as overlapping regions assigned to different classes • Step 5: If a MMU is to be considered, then generalize the map so that all regions with smaller areas are merged with neighboring features
  10. 10. Methodology (workflow – example: class 1.2)
  11. 11. Methodology Solving inconsistencies • Aspects considered to create a hierarchical approach • Elements of the landscape that are more important in the organization of space • For example roads and water lines • Most frequent ordering of the overlapping elements in reality • For example, roads are usually found over water and not the inverse • Typical relative size of objects in the regions under analysis • Size of parks and urban green areas relative to agricultural regions • The importance of the features • Industrial areas versus residential • The most common topological relations • For example, an agricultural region may contain buildings but buildings do not contain agricultural regions
  12. 12. Methodology (solving inconsistencies: priority level 1) Level of priority UA Class UA class name CLC class CLC class name 1 1 Artificial surfaces 1 Artificial surfaces 2 5 Water 5 Water 3 2 Agricultural, semi-natural areas, wetlands 4 Wetlands 4 3 Forests 2 Agricultural areas 5 --- --- 3 Forests
  13. 13. Methodology (priority level 2) Level of priority UA Class UA class name CLC class CLC class name 1 1.2 Industrial, commercial, public, military, private and transport units 1.2 Industrial, commercial, public, military, private and transport units 2 5.0 Water 5.1 Inland water 3 1.4 Artificial non-agricultural vegetated areas 5.2 Marine water 4 1.3 Mine, dump and construction sites 4.1 Inland wetlands 5 1.1 Urban Fabric 4.2 Maritime wetlands 6 2.0 Agricultural, semi-natural areas, wetlands 1.4 Artificial non-agricultural vegetated areas 7 3.0 Forests 1.3 Mine, dump and construction sites 8 --- --- 1.1 Urban Fabric 9 --- --- 2.2 Permanent crops 10 --- --- 2.1 Arable land 11 --- --- 2.4 Heterogeneous agricultural areas 12 --- --- 2.3 Pastures 13 --- --- 3.2 Scrub and/or herbaceous vegetation associations 14 --- --- 3.3 Open spaces with little or no vegetation 15 --- --- 3.1 Forests
  14. 14. Methodology (application workflow)
  15. 15. Case studies Paris area OpenStreetMap
  16. 16. Case studies Paris area OpenStreetMap OSM extracted data Corine Land Cover
  17. 17. Case studies London area OpenStreetMap
  18. 18. Case studies London area OpenStreetMap
  19. 19. Conclusions and future work • Capabilities • LULC maps with a level of detail comparable to UA and CLC can be obtained • As more and more features are added to OSM on a daily basis, the richness of the obtained LUCM obtained from OSM will increase • It is possible to create LUCM using the dynamic and continuously updated information available in OSM • It is also possible to create LUCM for different time periods using the historical data available in OSM • Challenges • The number of tag values available in OSM and how they change with location • Conversion of linear features to polygons • Quality limitations due to the nature of OSM data • Future work • Increase the number of tags and tag values used • Derive additional rules to convert the linear features to area features • Improve the automated approach to solve some types of inconsistencies
  20. 20. Thank You!

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