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

Summary

• Introduction

• OpenStreetMap Data

• Urban Atlas/Corine Land Cover Nomenclature

• Methodology

• Case Studies

• Conclusions and future work

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

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)

• 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)

• 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) invector format

• Minimum mapping units:

• 0,25km2 for area features

• 100m for linear features

• The positional accuracy is 100m and the overall thematic accuracy isgreater than 85%.

• The classification of CLC is made considering a nomenclatureseparated into levels (there are 44 land cover classes in the mostdetailed level).

Corine Land Cover (CLC)

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 coverclasses in CLC are more detailed than in the UA, which reflectsdifferences 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 fabric1.1.2 Discontinuous urban fabric1.1.3 Isolated Structures

1.Artificial Surfaces 1.1 Urban Fabric 1.1.1 Continuous urban fabric1.1.2 Discontinuous urban fabric

1.2 Industrial, commercial, public, military, private and transport units

1.2.1 Industrial, commercial, public, military and private units1.2.2 Road and rail network and associated land1.2.3 Port areas1.2.4 Airports

1.2 Industrial, commercial, public, military, private and transport units

1.2.1 Industrial or commercial units1.2.2 Road and rail network and associated land1.2.3 Port areas1.2.4 Airports

1.3 Mine, dump and construction sites

1.3.1 Mineral extraction and dump sites1.3.3 Construction sites1.3.4 Land without current use

1.3 Mine, dump and construction sites

1.3.1 Mineral extraction1.3.2 Dump sites1.3.3 Construction sites

1.4 Artificial non-agricultural vegetated areas

1.4.1 Green urban areas1.4.2 Sports and leisure facilities

1.4 Artificial non-agricultural vegetated areas

1.4.1 Green urban areas1.4.2 Sports and leisure facilities

2. Agricultural, semi-natural areas, wetlands 2.Agricultural areas 2.1 Arable land 2.1.1 Non-irrigated arable land2.1.2 Permanently irrigated land2.1.3 Rice fields

2.2 Permanent crops 2.2.1 Vineyards2.2.2 Fruit trees and berry plantations2.2.3 Olive groves

2.3 Pastures 2.3.1 Pastures2.4 Heterogeneous agricultural areas

2.4.1 Annual crops associated with permanent crops2.4.2 Complex cultivation patterns2.4.3 Land principally occupied by agriculture, withsignificant areas of natural vegetation2.4.4 Agro-forestry areas

3. Forests 3. Forest and semi natural areas 3.1 Forests 3.1.1 Broad-leaved forest3.1.2 Coniferous forest3.1.3 Mixed forest

3.2 Scrub and/or herbaceous vegetation associations

3.2.1 Natural grasslands3.2.2 Moors and heathland3.2.3 Sclerophyllous vegetation3.2.4 Transitional woodland-shrub

3.3 Open spaces with little or no vegetation

3.3.1 Beaches, dunes, sands3.3.2 Bare rocks3.3.3 Sparsely vegetated areas3.3.4 Burnt areas3.3.5 Glaciers and perpetual snow

------- 4. Wetlands 4.1 Inland wetlands 4.1.1 Inland marshes4.1.2 Peat bogs

4.2 Maritime wetlands 4.2.1 Salt marshes4.2.2 Salines4.2.3 Intertidal flats

5. Water 5. Water 5.1 Inland waters 5.1.1 Water courses5.1.2 Water bodies

5.2 Marine waters 5.2.1 Coastal lagoons5.2.2 Estuaries5.2.3 Sea and ocean

Urban Altas nomenclature CORINE Land Cover nomenclature

Level 1 Level 2 Level 3 Level 1 Level 2 Level 3

Methodology to convert OSM data into UA/CLC nomenclatureThe main steps of the methodology are:

• Step 1: Associate the key/value combinations available in OSM withthe LULC classes in the LULC product of interest, in this case the UA orCLC

• Step 2: Choose any user defined values that are necessary for theprocessing

• Step 3: Run the conversion process

• Step 4: Eliminate inconsistencies such as overlapping regions assignedto different classes

• Step 5: If a MMU is to be considered, then generalize the map so thatall regions with smaller areas are merged with neighboring features

Methodology (workflow – example: class 1.2)

MethodologySolving 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

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

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 water3 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 wetlands6 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 Fabric9 --- --- 2.2 Permanent crops10 --- --- 2.1 Arable land11 --- --- 2.4 Heterogeneous agricultural areas

12 --- --- 2.3 Pastures13 --- --- 3.2 Scrub and/or herbaceous vegetation

associations14 --- --- 3.3 Open spaces with little or no vegetation

15 --- --- 3.1 Forests

Methodology (application workflow)

Case studiesParis area

OpenStreetMap

Case studiesParis area

OpenStreetMap

OSM extracted data Corine Land Cover

Case studiesLondon area

OpenStreetMap

Case studiesLondon area

OpenStreetMap

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

Thank You!