Download - image classification taxonomy
![Page 1: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/1.jpg)
Guillermo VILLAIGN Spain
From Classifications, Legends and Nomenclatures to Parametric Object Oriented Databases: a paradigm shift in Geographic
Information
CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
![Page 2: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/2.jpg)
2 2CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Warning: This presentation is intentionallyprovocative and “politically incorrect”
We apologize for the annoyances it may cause
![Page 3: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/3.jpg)
3 3CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
paradigm= unquestioned* ‘true’ in a scientific or technical domain
(*) in a certain historical time
![Page 4: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/4.jpg)
4 4CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Outline
1. Introduction2. Example: Database of people3. Example: Land Cover classifications4. New solution: Parametric Object Oriented
databases. Application to the Land Cover case
5. Classifications, legends and nomenclatures “hidden” in other G.I. themes
6. Conclusions
![Page 5: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/5.jpg)
5 5CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Everybody agrees on the importance ofgeographical information (G.I.) but…
![Page 6: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/6.jpg)
6 6CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Why is geographical information so difficult to achieve correctly ?
![Page 7: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/7.jpg)
7 7CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Our world is infinitely complex, and it is made by multiple interaction of myriads of phenomenon that go from de atomic scale to geographical scale
It is constantly changing (plant fenology, agricultural cycles, climatology, human action…)
G.I. Information uses concepts and terms:With “fuzzy” definitions and significancesFrom science and technique as well as everyday useChanging from one place to another of the world (same term for different thing and different terms for same thing)
Because …
![Page 8: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/8.jpg)
8 8CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
G.I. needs to “mix” concepts and terms from many different thematic fields:
Geology, Geomorphology, EdaphologyHidrography, HydrologyBotanics, AgricultureForestry, Ecology, ChemistryUrban PlanningEconomic ActivityInfrastructuresArchitectureClimatologyEtc….
Because …
![Page 9: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/9.jpg)
9 9CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• Historically, the information has been stored in paper maps
• So it was useless to retrieve information impossible to be stored in a paper map
Historical evolution of geographicinformation storing technologies
PaperMaps
PaperMaps GIS
Databases
GIS Databases
Digital Maps
(CAD)
Digital Maps
(CAD)
TIME LINE
![Page 10: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/10.jpg)
10 10CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• The problem is that information you can store in a paper map is a very small part of information you could retrieve or need about reality
• Paper maps are a good way to show information, but a very bad way to store it
Amount of information in a paper map
NeededInformation
NeededInformation
Paper MapInfo.
Paper MapInfo.
Reality
![Page 11: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/11.jpg)
11 11CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• The first information that was introduced in digital databases was the info already present in paper maps
Databases designed to contain onlyinformation already present in paper maps
Paper MapInfo.
Paper MapInfo.
The first digital databases were designed to store only the information that was already in the maps
NeededInformation
NeededInformation
Reality
old G.I.databases
Map-centric data model
![Page 12: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/12.jpg)
12 12CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• Databases that would be able to store all the information needed are completely different (and more complex) compared with old G.I. databases
Databases able to store all theinformation needed about reality
Paper MapInfo.
Paper MapInfo.
Neededdatabase
old G.I.database
NeededInformation
NeededInformation
![Page 13: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/13.jpg)
13 13CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
That most of present GI databases are obsolete, because they were designed only to store information in paper maps
That many of new data models being built today are also obsolete, because they are “contaminated” (“poluted”) by “map centric” “out of date” way of thinking. (E.g: the use of classifications, legendsor nomenclatures)
That it is imperative to change all this data models and databases if we want GI to achieve the goals that we expect in the 21st century
In this presentation we will try to show:
![Page 14: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/14.jpg)
14 14CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Outline
1. Introduction2. Example: Database of people3. Example: Land Cover classifications4. New solution: Parametric Object Oriented
databases. Application to the Land Cover case
5. Classifications, legends and nomenclatures “hidden” in other G.I. themes
6. Conclusions
![Page 15: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/15.jpg)
15 15CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Suppose we need to build a database of “people”
![Page 16: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/16.jpg)
16 16CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Characteristics considered
- fat- medium weigh- thin
- tall- medium height- small
- man- woman
Possible values
3weight
3height
2gender
Number of values
Characteristic
![Page 17: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/17.jpg)
17 17CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Characteristics considered
- fat- medium weigh- thin
- tall- medium height- small
- man- woman
Possible values
3weight
3height
2gender
Number of values
Characteristic
Classification Number of classes = 2 x 3 x 3 = 18
![Page 18: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/18.jpg)
18 18CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
“Hierarchical classification” of people
2 x 3 x 3 = 18 classes
1. Men1.1. Tall men
1.1.1. Tall and fat men1.1.2. Tall and medium-weight men1.1.3. Tall and thin men
1.2. Medium height men1.2.1. Medium height and fat men1.2.2. Medium height and medium-weight men1.2.3. Medium height and thin men
1.3. Small men1.3.1. Small and fat men1.3.2. Small and medium-weight men1.3.3. Small and thin men
2. Women2.1.Tall women
2.1.1. Tall and fat women2.1.2. Tall and medium-weight women2.1.3. Tall and thin women
2.2. Medium height women2.2.1. Medium height and fat women2.2.2. Medium height and medium-weight women2.2.3. Medium height and thin women
2.3. Small women2.3.1. Small and fat women2.3.2. Small and medium-weight women2.3.3. Small and thin women
![Page 19: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/19.jpg)
19 19CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
But there are many other possible characteristics to be considered:
- nationality- age- study level- work- residence- eyes color- hair color- diseases- marital status- number of sons- hobbies- religion- etc, etc, etc…..
![Page 20: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/20.jpg)
20 20CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
What would be the number of classes needed to store all this information ?
250 * 100 * 4 * 100 * 250 * 5 * 4 * 20 * 4 * 10 * 20 * 10 =
![Page 21: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/21.jpg)
21 21CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
250 * 100 * 4 * 100 * 250 * 5 * 4 * 20 * 4 * 10 * 20 * 10 =
= 8,000,000,000,000,000
= 8 * 1015 classes !!
![Page 22: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/22.jpg)
22 22CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• Would these classes be useful in practice?• Would it be possible to implement them in an
information system?
⇒ not at all !!
![Page 23: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/23.jpg)
23 23CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
This is called by computer engineersthe ‘class explosion’ problem
![Page 24: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/24.jpg)
24 24CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
So… this is clearly not the way to go…
![Page 25: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/25.jpg)
25 25CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Outline
1. Introduction2. Example: Database of people3. Example: Land Cover classifications4. New solution: Parametric Object Oriented
databases. Application to the Land Cover case
5. Classifications, legends and nomenclatures “hidden” in other GI themes
6. Conclusions
![Page 26: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/26.jpg)
26 26December 2007 INSPIRE D2.6 workshop Ispra
3.1. The actual ‘paradigm’*:Land Cover Classifications
(*) paradigm= unquestioned ‘true’ in a certain technical domain
![Page 27: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/27.jpg)
27 27CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
‘Hierarchical classification’ of people
2 x 3 x 3 = 18 classes
1. Men1.1. Tall men
1.1.1. Tall and fat men1.1.2. Tall and medium-weight men1.1.3. Tall and thin men
1.2. Medium height men1.2.1. Medium height and fat men1.2.2. Medium height and medium-weight men1.2.3. Medium height and thin men
1.3. Small men1.3.1. Small and fat men1.3.2. Small and medium-weight men1.3.3. Small and thin men
2. Women2.1.Tall women
2.1.1. Tall and fat women2.1.2. Tall and medium-weight women2.1.3. Tall and thin women
2.2. Medium height women2.2.1. Medium height and fat women2.2.2. Medium height and medium-weight women2.2.3. Medium height and thin women
2.3. Small women2.3.1. Small and fat women2.3.2. Small and medium-weight women2.3.3. Small and thin women
![Page 28: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/28.jpg)
28 28CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 41.1.1.1 Residential continuous dense urban fabric.Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories.1.1.1.2 Residential continuous medium dense urban fabric.Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories.1.1.1.3 Informal settlements1.1.2.1 Residential discontinuous dense urban fabric.Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit.1.1.2.2 Residential discontinuous sparse urban fabric.Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture.
1.1.2.3 Residential urban blocks1.1.2.4 Informal discontinuous residential structures
1.1. Urban fabric: 1. ARTIFICIAL AREAS
1.1.1. Continuous urban fabric:Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.2 Discontinuous urban fabricMost of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
Corine - Moland Classifications
This is the same methodology used in Land Cover Classification databases. E.g: Corine LC, Moland, Anderson,…
![Page 29: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/29.jpg)
29 29CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
3.2. Problems experienced during the design, production and useof Land Cover classifications (CORINE, Moland, etc…)
![Page 30: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/30.jpg)
30 30CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Proliferation of “difficult to use” classes, due to the multiple “crossings” of several classification criteria
1) Class “explosion” (in detailed nomenclatures)
This has been a great problem in the effort to develop and use of Spanish 5 level 84 classes developed for CLC2000
![Page 31: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/31.jpg)
31 31CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Complex definitions, in which several “classification rules” apply simultaneously, cause incoherencies:- “overlapping definitions” (some polygons can be assigned to more than one class)- “un-definitions” (some polygons cannot be assigned to any of the classes)
- E.g.: Moland 1.1.1.1. Residential continuous dense urban fabric:- Most of the land is covered by structures and transport network.- Buildings, roads and artificially surface areas cover more than 80% of the total surface- Non-linear areas of vegetation and bare soil are exceptional- Residential structures cover more than 80% of the total surface- More than 50% of the buildings have three or more stories.”
2) Class definition incoherencies
![Page 32: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/32.jpg)
32 32CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Information stored in the database is much less than information acquired by the photointerpreter:
E.g.: The photointerpreter evaluates a certain polygon’s trees percentageas 75 %, and in consequence he labels it as Corine 3.1.1. “Broad-leaved forest”.
… But the user only receives the informationthat trees are “more than 30 %”
3) Important information lost
![Page 33: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/33.jpg)
33 33CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
4) Mixed classes problems
Need to create “mixed classes”, due to the obligation to assign a single class to mixed polygonsE.g: “Mixed forest”, “Complex cultivation patterns”,…
- Mixed classes provide little information to user:E.g.: “Complex cultivation patterns”,…???
- Mixed classes lead to erroneous conclusions in the use of the database:E.g.: If one wants to know how many vineyards there are in a certain region, he will search for the class 2.2.1: “Vineyards”. But there can also be plenty of vineyards “hidden” in other classes as: 2.2.3: “Olive groves”(which includes the association of olives trees and vines); 2.4.2: “Complex cultivation patterns”, etc…
![Page 34: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/34.jpg)
34 34CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Important spatial variations in certain parameters values do notappear in the database if this variations do not “cross” the “threshold line”
5) Important spatial variations not registered
- E.g.: Urban areas with very different levels of building densities (as 10 % and 50 %) have to be assigned to the sameMoland class (1.1.2.2. Residential discontinuous sparse urban fabric.)
![Page 35: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/35.jpg)
35 35CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Important temporal changes do not appear in the changes database, because:
- These changes do not “cross” the “definition rule” threshold.E.g.: If the building density of a polygon has increased from 11% to 79 %in the time between 2 revisions of the database, this polygon is labeled as Corine´s 1.1.2. “Discontinuous urban fabric”, in both databases, and so no change is registered.
and/or:- These changes are “hidden” inpolygons assigned to dominantclasses or to mixed classes.
6) Important temporal changes not detected
![Page 36: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/36.jpg)
36 36CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Many parameters and “Indicators” could be calculated from the values of the parameters appearing in class definitions (sometimes “crossing” them with exogenous information such as population, etc…).
Eg:• building density (m3/m2)in an area• m2 of building per person in an area• average height of buildings in a town• % of impervious surface in an area• % of trees in a forest• m2 of green areas per person in an area• land take by transport infrastructures in a city• etc…
7) Parameters and Indicators calculations not possible
![Page 37: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/37.jpg)
37 37CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Need for calculation of parameters and Indicators
WorldWorld
Database1
Database1
Datamodel
1
Datamodel
1 DatasetA
DatasetA
Environ-mental
Processesmodels
Environ-mental
Processesmodels
Predictionsof Future
Predictionsof Future- Laws
- Policies- Laws- Policies
Database2
Database2
Datamodel
2
Datamodel
2Dataset
B
DatasetB
Databasen
Databasen
DataModel
n
DataModel
n
DatasetN
DatasetN
MultilayerGeoprocessingMultilayer
Geoprocessing
MultilayerGeoprocessing
New data sets:
- Parameters
- Agri-environmental Indicators
Interpretation
Actions
Political decisions
DatabasesData specifications
e.g: Land Cover e.g: Urban sprawl
e.g: Climatic
change
e.g: Future climatee.g: Kioto Protocol
![Page 38: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/38.jpg)
38 38CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Land Cover Classifications do not allow calculating these indicators, because de real values of the different parameters are not storedin the database (only “intervals”):
E.g: You can not divide “Artificially surfaced areas are more than 80 %” by a surface
Calculations not possible …
![Page 39: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/39.jpg)
39 39CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
It is impossible to compare, “translate” or mosaic two databases built with different Nomenclatures:
E.g.: When we have a 3.1.1. “Broad leaved forest” polygon in Corine (defined as having more than 30 % of canopy closure)
→ there is no way to know if it should be labeled as “Forest” in a database with a different Nomenclature where “forests” are defined as “more than 50 % of canopy closure”.
8) Mosaicking different databases not possible
![Page 40: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/40.jpg)
40 40CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
If one wants to derive a smaller scale database (through a “generalization” process), one must aggregate polygons into bigger ones.But there is no way to automatically derive the class of the resultant polygon:
E.g: A polygon classified as Corine´s 1.1.1 (“Continuous urban fabric”) aggregated with a polygon classified as 1.1.2 (“Discontinuous urban fabric”) could result in an aggregated polygon that should be classified as 1.1.1 or 1.1.2.
There is no way to know, without repeating the interpretation !
9) Automatic generalizations not possible
![Page 41: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/41.jpg)
41 41CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
3.3. UN FAO´s LCCS (Land Cover Classification System) approach
![Page 42: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/42.jpg)
42 42CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Land Cover Classification System (LLCS) has been developed by UN-FAO in an effort to solve the problems associated with existing Classification databases.
LCCS establishes a standardized comprehensive system that allows one to build “a priori” classification “nomenclatures”and “legends” in a more rigorous and coherent way
LCCS approach
![Page 43: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/43.jpg)
43 43CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LCCS: our opinion
LCCS correctly exposes some of the problems of Land Cover classifications mentioned before
In particular, it addresses and solves quite wellProblem Number 2 (Class definitions incoherencies)
![Page 44: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/44.jpg)
44 44CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
.. But there are still many remaining problems with LCCS approach…
LCCS: our opinion (2)
![Page 45: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/45.jpg)
45 45CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• Plenty of information lost• Mixed classes problems• Spatial variations not registered• Temporal variations not detected• Parameters and Indicators calculations not possible• Database mosaicking not possible• Automatic generalization not possible
Problems remaining
![Page 46: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/46.jpg)
46 46CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LCCS makes the same mistake than traditional HC LU/LC nomenclatures:
It tries to classify each polygon in one and only one class, using the values of the “classifiers” to put sequentially the polygon to one side or the other of the “classification rules”
(It is important to note that the values that this classifiers haIt is important to note that the values that this classifiers have in a ve in a particular polygon are not stored in the database. Only the particular polygon are not stored in the database. Only the polygonpolygon’’s resultant class label is stored in the databases resultant class label is stored in the database)
LCCS: our opinion (3)
![Page 47: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/47.jpg)
47 47CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• “Special”, non-standard “language” and software
Other problems of LCCS (1)
![Page 48: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/48.jpg)
48 48CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• Doesn’t fulfill ISO 19100 standards
• Not object oriented
• Not UML
• Attributes values not stored in the database: it is still only a Classification Method !
Other problems of LCCS (2)
![Page 49: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/49.jpg)
49 49CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Outline
1. Introduction2. Example: Database of people3. Example: Land Cover classifications4. New solution:
Parametric Object Oriented databases.Application to the Land Cover case
5. Classifications, legends and nomenclatures “hidden” in other GI themes
6. Conclusions
![Page 50: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/50.jpg)
50 50CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Characteristics to be considered:- nationality- age- study level- work- residence- eyes color- hair color- diseases- married ?- number of sons- hobbies- religion…….…..
Back to the example: “People”
![Page 51: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/51.jpg)
51 51CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Parametric database of “people”
- Gender: controlled list (man, women)- Height (m): real- Weigh (Kg): real- Nationality: controlled list (country table)- Age (years): integer- Study level: controlled list- Work: controlled list- Residence: text- Eyes color: controlled list- Hair color: controlled list- Diseases: controlled list- Married: boolean- Number of sons: integer- Hobbies: controlled list- Religion: controlled list
People
![Page 52: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/52.jpg)
52 52CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
One instance of “people”
- Gender: man- Height (m): 1.77- Weigh (Kg): 82.6- Nationality: USA- Age (years): 52- Study level: University- Work: Engineer- Residence: San Diego, CA- Eyes color: brown- Hair color: blond- Diseases: none- Married: yes- Number of sons: 2- Hobbies: golf, sailing- Religion: protestant
John Smith
![Page 53: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/53.jpg)
53 53CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• A data model is the description of what we are storing in a database and how.
• It is the “link” between reality and the Database
What is a Data Model ?
WorldWorld DatabaseDatabaseData
Model
DataModel
![Page 54: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/54.jpg)
54 54CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• “Object Orientation” is the standard in Computer Science today
• Parametric Object Oriented Data Models (POODM) are used extensively in every type of databases and Information Systems (airports, hospitals, production facilities,..)….
….including “some” GIS databases
What is a Object Orientation ?
![Page 55: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/55.jpg)
55 55CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
UML (Unified Modeling Language)
The standard for Object Oriented ModelsUsed by ISO in its standards
![Page 56: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/56.jpg)
56 56CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Principal relationships between classes:
Inheritance (Generalization / Specialization): A class inherits (or specializes) the state and behavior of another class
Aggregation: allows to capture the structural relationshipsamong entities in the real world (part-of)
Association: allows to capture other kinds any of relationships among entities in the real world
![Page 57: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/57.jpg)
57 57CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Standards – ISO 19109
Example: Three features (Property Parcel, Building and Loan)
![Page 58: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/58.jpg)
58 58CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• POODM (Parametric Object Oriented Data Models) allow an unprecedented flexibility and capability in de design and use of very complex Information Systems
POODM
![Page 59: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/59.jpg)
59 59CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• POODM is the best technology to address the great complexity of Geographic information:
Represent the “systemic” nature of the world
Define “objects” of different scales (Covers, Elements,…) and the relations between them
Assign attribute values to each one of these “objects” in a structured and organized way
![Page 60: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/60.jpg)
60 60CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
UML (Universal Modeling Language) lets us express, store, modify, extend,… this structure easily and make it understandable by anybody
![Page 61: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/61.jpg)
61 61CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• Land Cover has been traditionally modeled (Corine, MolandAndersons,…) using classifications, legends, nomenclatures…. … All of them obsolete techniques
• Up to now (to our knowledge) POODM have not been used for Land Cover Information
POODM for Land Cover Information
![Page 62: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/62.jpg)
62 62CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 41.1.1.1 Residential continuous dense urban fabric.Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories.1.1.1.2 Residential continuous medium dense urban fabric.Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories.1.1.1.3 Informal settlements1.1.2.1 Residential discontinuous dense urban fabric.Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit.1.1.2.2 Residential discontinuous sparse urban fabric.Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture.
1.1.2.3 Residential urban blocks1.1.2.4 Informal discontinuous residential structures
1.1. Urban fabric: 1. ARTIFICIAL AREAS
1.1.1. Continuous urban fabric:Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.2 Discontinuous urban fabricMost of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
Density thresholds
Land Cover Elements
Percentage of polygon occupation
Attributes
Land Cover Classifications (Moland legend)
![Page 63: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/63.jpg)
63 63CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 41.1.1.1 Residential continuous dense urban fabric.Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories.1.1.1.2 Residential continuous medium dense urban fabric.Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories.1.1.1.3 Informal settlements1.1.2.1 Residential discontinuous dense urban fabric.Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit.1.1.2.2 Residential discontinuous sparse urban fabric.Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture.
1.1.2.3 Residential urban blocks1.1.2.4 Informal discontinuous residential structures
1.1. Urban fabric: 1. ARTIFICIAL AREAS
1.1.1. Continuous urban fabric:Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.2 Discontinuous urban fabricMost of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
Density thresholds:
- somewhat “arbitrary”
- induce class proliferation
![Page 64: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/64.jpg)
64 64CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 41.1.1.1 Residential continuous dense urban fabric.Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories.1.1.1.2 Residential continuous medium dense urban fabric.Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories.1.1.1.3 Informal settlements1.1.2.1 Residential discontinuous dense urban fabric.Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit.1.1.2.2 Residential discontinuous sparse urban fabric.Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture.
1.1.2.3 Residential urban blocks1.1.2.4 Informal discontinuous residential structures
1.1. Urban fabric: 1. ARTIFICIAL AREAS
1.1.1. Continuous urban fabric:Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.2 Discontinuous urban fabricMost of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
Simple components:
- not structured
- not explicit (“hidden” in de definitions of the classes)
- incomplete
- not “extensible” (one can not subdivide a component in different types)
![Page 65: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/65.jpg)
65 65CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 41.1.1.1 Residential continuous dense urban fabric.Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories.1.1.1.2 Residential continuous medium dense urban fabric.Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories.1.1.1.3 Informal settlements1.1.2.1 Residential discontinuous dense urban fabric.Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit.1.1.2.2 Residential discontinuous sparse urban fabric.Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture.
1.1.2.3 Residential urban blocks1.1.2.4 Informal discontinuous residential structures
1.1. Urban fabric: 1. ARTIFICIAL AREAS
1.1.1. Continuous urban fabric:Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.2 Discontinuous urban fabricMost of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
Attributes:
- not structured
- not explicit in the database (“hidden” in de definitions of the classes)
- incomplete
- actual values not stored (only “ranges”)
- not “extensible” (one can not add new attributes)
![Page 66: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/66.jpg)
66 66CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 41.1.1.1 Residential continuous dense urban fabric.Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories.1.1.1.2 Residential continuous medium dense urban fabric.Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories.1.1.1.3 Informal settlements1.1.2.1 Residential discontinuous dense urban fabric.Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit.1.1.2.2 Residential discontinuous sparse urban fabric.Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture.
1.1.2.3 Residential urban blocks1.1.2.4 Informal discontinuous residential structures
1.1. Urban fabric: 1. ARTIFICIAL AREAS
1.1.1. Continuous urban fabric:Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.2 Discontinuous urban fabricMost of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
Percentage of polygon occupation:
- sometimes expressed vaguely (e.g: “predominant”)
- actual values not stored in database
- not explicit (“hidden” in the definition of the classes)
![Page 67: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/67.jpg)
67 67CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Land Cover elements:- complete
- structured
- explicit
- extensible
Attributes:- complete
- structured
- explicit
- exact values measured
and stored in database
- extensible
Percentage of polygon occupation:
- explicit
- expressed rigorously:
(type: real, integer, boolean, list,…)
- exact values measured and
stored in database
Parametric object oriented data model (POODM)
![Page 68: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/68.jpg)
68 68CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
• From the “Conceptual Model” in an UML diagram an expert in Relational Databases can easily derive a “Physical Implementation” in any standard RDBMS
• This RDB stores all the objects and attributes in a structured and robust way, and allows to interact with all these data through:• SQL queries• Different databases “crossing”• Interaction with an specific “application”
![Page 69: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/69.jpg)
69 69CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
The use of POODM for Land Cover Information has been developed, tested and is working in the Spanish SIOSEProject, which is in advanced production phase(finishing by end of 2008)
![Page 70: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/70.jpg)
70 70CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
A physical implementation (Relational Database and an Application to fill it) according with data model specifications have been developed and are in use in SIOSE production
![Page 71: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/71.jpg)
71 71CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
We are presenting here an “evolution” of SIOSE Data Model, designed to allow for:
Different scalesDifferent geographical areasDifferent needsDifferent production methodology
![Page 72: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/72.jpg)
72 72CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
![Page 73: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/73.jpg)
73 73CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Land Cover
Elements
Land Cover
Classes
Core Clases
![Page 74: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/74.jpg)
74 74CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Basic principles of POODM (1)
We do not “classify” polygons. We describe polygons
Each polygon has one or more “Land covers” (LC)
We do not use “mixed classes”: we store in the database the % of the polygon occupied by each “Land cover”
![Page 75: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/75.jpg)
75 75CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Basic principles of POODM (2)
Land Covers are made up of“Land Cover Elements” (LCE)
We store in the database the % of each land cover occupied by each element.
Each LC and LCE can have “attributes”
The actual values of all attributes for each LC and LCE are storedin the database
![Page 76: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/76.jpg)
76 76CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Land cover 1: 100% of poligon’s surfaceArtificial area. Built-up area. ResidentialLand Cover Elements in it:
• 50% Artificial element. Structure. Building+individual house
• 15% Artificial element. Artificial surface. Road• 15% Vegetation. Trees• 15% Vegetation. Herbaceous. • 5% Artificial liquid
Examples
![Page 77: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/77.jpg)
77 77CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Land cover 1: 2% of polygon´s surfaceAgricultural area. Herbaceous crops+Greenhouses: yesLand Cover Elements in it:
• Vegetation. Herbaceous.
Land cover 2: 70% of polygon’s surfaceAgricultural area. Herbaceous cropsLand Cover Elements in it:
• Vegetation. Herbaceous. Wheat
Land cover 3: 20% of polygon’s surfaceAgricultural area. Permanent crop. Fruit trees plantationLand Cover Elements in it:
• 60% Vegetation. Woody. Tree. Orange trees• 40% Vegetation. Woody. Tree. Lemon trees
Land cover 4: 8%of polygon’s surfaceArtificial area. Infrastructure. ReservoirsLand Cover Elements in it:
• Natural terrain. Water
Examples
![Page 78: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/78.jpg)
78 78CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Land cover 1: 100% of polygon’s surfaceNatural area. Terrestrial. Natural area with woodland crops
Land Cover Elements in it:• 45% Vegetation. Woody. Tree.
+ Pinus pinaster• 55% Vegetation. Woody. Tree.
+ Quercus ilex
Examples
![Page 79: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/79.jpg)
79 79CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
“Backward” compatibility
From a robust and well designed Parametric Object Oriented Land Cover Database, land cover classifications and nomenclaturescan be derived by making appropriate SQL queries to the database
![Page 80: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/80.jpg)
80 80CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
“Forward” compatibility
The information in existing Land Cover classifications datasets can be entered in an adequately designed POO database:
Class definitions attributes intervals
+ attribute_max+ attribut_min
![Page 81: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/81.jpg)
81 81CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Adequacy to INSPIRE data flows ?
![Page 82: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/82.jpg)
82 82CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
YESYes, but not applicable because of point 2.
3. Semantic generalization
2. Extensibility
1. Spatial generalization
Need
YESNO- Proliferation of unusable classes, due to the multiple “crossings” of several classification criteria- It is impossible to add external information from a specialized field to a HC database
YESNOWhen we aggregate polygons into bigger ones, there is no way to automatically derive the class of the resultant polygon
Object OrientedData Models
Land Cover classifications
Adequacy to INSPIRE data flows
![Page 83: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/83.jpg)
83 83CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
YESOne can update certain “covers”, elements or attributes of an Object Oriented database more frequently. E.g:- urban fabric: 1 year- forest trees: 5 years
NO
Each polygon has a single attribute: the class label. Is has to be updated at once
4. Different update periods
YESThe mean of continuous value variables for each polygon´s area can then be input and stored in the OODM database as a parameter that qualifies each polygon
NOEach polygon has a single attribute: the
class label
6. Integration with remote sensing automatically derived parameters
5. Allow formulti- layer geoprocessing
Need
YESNOIt is not possible to calculate derived Parameters (and agri-environmental indicators) based on database's parameter values, because these values are not stored in the database
Object OrientedData Models
Land Cover classifications
Adequacy to Inspire data flows
![Page 84: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/84.jpg)
84 84CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
YES
One can translate existing classifications to a common POODB
NO
There is no way to know de label of a polygon in a DB with a different Nomenclature
7. Mosaicking of existing Land Cover databases
Need Object OrientedData Models
Land Cover classifications
Adequacy to Inspire data flows
![Page 85: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/85.jpg)
85 85CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Outline
1. Introduction2. Example: Database of people3. Example: Land Cover classifications4. New solution: Parametric Object Oriented
databases. Application to the Land Cover case
5. Classifications, legends and nomenclatures “hidden” in other GI themes
6. Conclusions
![Page 86: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/86.jpg)
86 86CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Classifications “hidden” in other GI themes
Road networks:Main roadsSecondary roadsLocal roads…
Hidrology:First order riversSecond order rivers………
![Page 87: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/87.jpg)
87 87CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Outline
1. Introduction2. Example: Database of people3. Example: Land Cover classifications4. New solution: Parametric Object Oriented
databases. Application to the Land Cover case
5. Classifications, legends and nomenclatures “hidden” in other GI themes
6. Conclusions
![Page 88: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/88.jpg)
88 88CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Conclusions (1)
Geographic Information should not be modeled by classifications / legends / nomenclatures, as they imply a great decrease in the quantity and usefulness of information stored
FAO’s LCCS (Land Cover Classification System) is notan acceptable solution either
Land Cover, as any other G.I. theme can and shouldbe modeled in UML using Parametric Object Orientedtechnology
![Page 89: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/89.jpg)
89 89CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Conclusions (2)
The ideas and problems presented here are applicableto the development of many other Inspire Themes Data Models
Most of present GI databases are obsolete, because they were designed only to store information in paper maps
Many of new data models being built today are also obsolete, because they are “contaminated” (“poluted”) by “map centric” “out of date” way of thinking. (E.g: the use of classifications, legends or nomenclatures)¡
![Page 90: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/90.jpg)
90 90CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Many GMES data models currently in use suffer from this same problems
Conclusions (3)
![Page 91: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/91.jpg)
91 91CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Some of this obsolete “map centric” data models are even being proposed as international standards (ISO, INSPIRE,…)
CEN and INSPIRE should be careful not to adopt obsolete data models as standards
Conclusions (4)
![Page 92: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/92.jpg)
92 92CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
It is imperative to change all this data models and databases if we want G.I. to achieve the goals that we expect of it in the 21st century
INSPIRE is designed mainly to permit the interaction between existing databases…
As most existing G.I. databases are “map based”….
Conclusions (5)
![Page 93: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/93.jpg)
93 93CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
…. INSPIRE is in great danger of implementingobsolete specifications (Corine Land Cover, LCCS,…)
Conclusions (6)
![Page 94: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/94.jpg)
94 94CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
We need a new ‘paradigm’ in 21st century geographic information !
Conclusions (7)
![Page 96: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/96.jpg)
96 96CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Extra slides…..
![Page 97: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/97.jpg)
97 97CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Principles
1) The working area must be divided in a set of closed polygons, each one containing a surface that is as homogeneous as possible.
2) Our aim is not to classify each polygon but to “describe” each one as well as possible. These descriptions are made associating “Land Covers”, “Land Cover Elements” and “attributes” for them to each polygon.
3) “Land Covers” are thematic categories. They are defined with conceptual definitions of biophysical or socio-economic criteria (morphology, structure, relation with other land cover entities, etc….)
![Page 98: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/98.jpg)
98 98CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Principles
4) “Land Cover Elements” are the objects found in the terrain that make up “Land Covers”. Land cover elements (e.g.: buildings, trees, rock, sand, etc…) are the basic components of land cover.
5) Each “Land Cover” (LC) and “Land Cover Element” (LCE) has its own attributes. Attributes are observable characteristics (biophysical or socio-economic) that describe LC or LCE in more detail. These attributes take different values in each “instance” (appearance of the LC or LCE).
![Page 99: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/99.jpg)
99 99CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Principles
6) Some attributes are simple variables of the adequate type (e.g.: number of floors: integer). Other attributes are “Controlled lists” (e.g.: “vegetation distribution geometry”). Controlled lists are defined as “enumerations” in UML. Other attributes are more complex (e.g.: “vegetation state”) and are represented as “UML classes” (white rectangles in the UML diagram).
![Page 100: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/100.jpg)
100100CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Principles
7) Homogeneous polygons (at the scale of the database) have one Land Cover.
8) When homogeneous surfaces have an area smaller than the Minimum Mapping Unit, the photointerpreter must draw a non-homogeneous polygon that encloses areas with different characteristics. In this case, thephotointerpreter must measure (or estimate), and store in the database, the percentage of surface in which each “Land Cover” is present in the polygon. The sum of all percentages of each polygon must be 100 %.
9) For each “Land Cover” found in a polygon, the photointerpreter must study its “inner composition”, and measure and store in the database:
- The exact values for each of the attributes in this “Land Cover”- The “Land Cover Elements” present in this “Land Cover”, and the percentage of
the surface that each occupies.- The exact values for each of the attributes in each “Land Cover Element”. (e.g.:
trunk diameter=0.40m; Number of floors = 4)
![Page 101: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/101.jpg)
101101CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Principles
10) All the information of each polygon (percentage of surface of each LC present, average values of each parameter affecting each LC or LCE,…) is stored in an alphanumerical relational database (RDB). This RDB has been designed with two objectives in mind:
- Materialize as exactly as possible the Parametric Object Oriented Data Model represented in the UML diagram.
- Allow for unlimited future extensions of the model, making it as easy as possible to add more classes, parameters, conditions, etc…
![Page 102: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/102.jpg)
102102CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 41.1.1.1 Residential continuous dense urban fabric.Residential structures cover more than 80% of the total surface. More than 50% of the buildings have three or more stories.1.1.1.2 Residential continuous medium dense urban fabric.Residential structures cover more than 80% of the total surface. Less than 50% of the buildings have three or more stories.1.1.1.3 Informal settlements1.1.2.1 Residential discontinuous dense urban fabric.Buildings, roads and artificially surface areas cover between 50% and 80% of the total surface area of the unit.1.1.2.2 Residential discontinuous sparse urban fabric.Buildings, roads and artificially surface areas cover between 10% and 50% of the total surface area of the unit. The vegetated areas are predominant by but is not land dedicated to forestry or agriculture.
1.1.2.3 Residential urban blocks1.1.2.4 Informal discontinuous residential structures
1.1. Urban fabric: 1. ARTIFICIAL AREAS
1.1.1. Continuous urban fabric:Most of the land is covered by structures and transport network. Buildings, roads and artificially surface areas cover more than 80% of the total surface. Non-linear areas of vegetation and bare soil are exceptional
1.1.2 Discontinuous urban fabricMost of the land is covered by structures. Buildings, roads and artificially surface areas are associated with vegetated areas and bare soil, which occupy discontinuous but significant surfaces. Between 10% and 80% of the land is covered by residential structures.
Murbandy/Moland legend (1/9)
![Page 103: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/103.jpg)
103103CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
1.2.1.1. Industrial areasSurfaces occupied by industrial activities, including their related areas.1.2.1.2. Commercial areasSurfaces basically occupied by commercial activities, including their related areas.
1.2.1.3 Public and private services not related to the transport systemSurfaces occupied by general government, semi-public or private administrations, including their related areas.1.2.1.4 Technological infrastructures for public services1.2.1.5 Archaeological sites1.2.1.6 Places of worship1.2.1.7 Non-vegetated cemeteries1.2.1.8 Hospitals1.2.1.9 Restricted access services1.2.1.10 Agro-industrial complexes1.2.1.11 Surface pipelines1.2.2.1 Fast transit roads and associated landMotorways, by-pass roads, toll-ways, etc1.2.2.2 Other roads and associated land1.2.2.3 Railways and associated land1.2.2.4 Other railways1.2.2.5 Additional transport structures1.2.2.6 Parking sites for private vehicles1.2.2.7 Parking sites for public vehicles
1.2.3 Port areasInfrastructure of port areas, including quays, dockyards and marinas1.2.4 AirportsAirport installations: runways, buildings and associated land
1.2. Industrial, commercial and transport units
1.2.1 Industrial, commercial, public and private units:Artificially surface areas devoid of vegetation, occupy more then 50% of the area in question, which also contains buildings and /or vegetated areas.
1.2.2 Road and rail networks and associated landMotorways, railways, including associated installations . Minimum width to include: 25 m
Murbandy/Moland legend (2/9)
![Page 104: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/104.jpg)
104104CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
1.3.1 Mineral extraction sitesAreas with open-pit extraction of industrial minerals or other minerals (opencast mines). Included flooded gravel pits, except for river-bed extraction.1.3.2 Dump sitesLandfill or mine dump sites, industrial or public.1.3.3 Construction sitesSpaces under construction development, soil or bedrock excavations, earthworks.1.3.4 Abandoned land 1.3.4.1 Bombed areas1.4.1 Green urban areasAreas with vegetation within urban fabric. Includes parks (and cemeteries with vegetation)
1.4.1.1. Vegetated cemeteries
1.4.2 Sport and leisure facilitiesCamping grounds, sport grounds, leisure parks, golf courses, racecourses, etc. Includes formal parks not surrounded by urban zones.
1.3.Mine, dump and construction sites
1.4.Artificial non-agricultural vegetated areas
Murbandy/Moland legend (3/9)
![Page 105: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/105.jpg)
105105CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
2.1.1.1 Arable land without dispersed vegetation
2.1.1.2 Arable land with scattered vegetation
2.1.1.3 Greenhouses
2.1.1.4 Drained arable land
2.1.2 Permanently irrigated landCrops irrigated permanently or periodically, using a permanently infrastructure. Most of these crops cannot be cultivated without an artificial water supply.2.1.3 Rice fieldsLand prepared for rice cultivation. Flat surfaces with irrigated channels. Surfaces periodically flooded.
2.2.1 VineyardsAreas planted with vines, when the vineyards parcels exceed the 50% of the area and/or they determine the land use of the area.
2.2.2 Fruit trees and berry plantationsParcels planted with fruit trees or shrubs: single or mixed fruit species, fruit trees mixed with permanent grass surfaces.
2.2.3 Olive grovesAreas planted with olive tress, including mixed occurrence of olive trees and vines on the same parcel.
2. AGRICULTURAL AREAS
2.1.1 Non-irrigated arable landCereals, legumes, fodder crops, root crops and fallow land, flowers, vegetables, nurseries of fruit trees, whether open field, under plastic or glass. Includes other annually harvested plants with more than 75% of the area under a rotation system.
2.1 Arable land
2.2 Permanent crops
Murbandy/Moland legend (4/9)
![Page 106: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/106.jpg)
106106CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
2.3.1.1 Pastures without trees and shrubs
2.3.1.2 Pastures with trees and shrubs
2.4.1 Annual crops associated with permanent cropsNon permanent crops (arable land or pastures) associated with permanent crops on the same parcel
2.4.2.1 Complex cultivation patterns without settlement
2.4.2.2 Complex cultivation patterns with scattered settlement
2.4.3.1 Prevalence of arable land and significant areas of natural vegetation
2.4.3.2 Prevalence of pastures and significant areas of natural vegetation
2.4.4 Agro-forestry areasAnnual crops or grazing land, covering less than 50% of the surface, under the wooded cover of forestry species.
2.3.1 PasturesDense grass cover, of floral composition, dominated by graminaceas, not under a rotation system. Mainly for grazing , but the fodder can be harvested mechanically.
2.4.3 Land principally occupied by agriculture, with significant areas of natural vegetationAreas principally occupied by agriculture, interspersed with significant natural areas, including wetlands or water bodies, out crops
2.4.2 Complex cultivation patternsJuxtaposition of small parcels of diverse annual crops, pastures and/or permanent crops.
2.4 Heterogeneous agricultural areas
2.3 Pastures
Murbandy/Moland legend (5/9)
![Page 107: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/107.jpg)
107107CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
3.1.1.1 Deciduous forest with continuous canopy
3.1.1.2 Deciduous forest with discontinuous canopy
3.1.1.3 Evergreen forest with continuous canopy
3.1.1.4 Evergreen forest with discontinuous canopy
3.1.2.1 Coniferous forest with continuous canopy
3.1.2.2 Coniferous forest with discontinuous canopy
3.1.3.1 Forest mixed by alternancy of single trees with continuous canopy3.1.3.2 Forest mixed by alternancy of single trees with discontinuous canopy3.1.3.3 Forest mixed by alternancy of stand of trees with continuous canopy3.1.3.4 Forest mixed by alternancy of stand of trees with discontinuous canopy3.2.1.1 Coarse permanent grassland / Tall herbs without trees and shrubs
3.2.1.2 Coarse permanent grassland / Tall herbs with trees and shrubs
3.2.1.3 Coastal and floodplain meadows
3.2.2.1 Heath land
3.2.2.2 Dwarf pine
3.2.3 Sclerophyllous vegetationBushy sclerophyllous vegetation, includes maquis and garriga. In case of shrub vegetation areas composed of sclerophyllous species and heathland species with no visible dominance (each species occupy about 50% of the area), priority will be given to sclerophyllous vegetation and the whole class will be assigned class 323.
3.2.4.1 Artificial young stands3.2.4.2 Natural young deciduous stands3.2.4.3 Natural young coniferous stands3.2.4.4 Wooded fens, bogs and wooded transitional bog
3. FOREST AND SEMI-NATURAL AREAS
3.1.2 Coniferous forestVegetated formation composed principally of trees, including shrub and bush understoreys, where coniferous species predominate (more than 75% of the formation)
3.1.3 Mixed forestVegetated formation composed principally of trees, including shrub and bush understoreys, where neither broad-leaved nor coniferous species predominate.
3.2.1 Natural grasslandLow productivity grassland (at least 75% of the surface). Often situated in areas of rough, uneven ground. Frequently included rocky areas, briars and heathland.
3.2.2 Moors and heathlandVegetation with low and closed cover, dominated by bushes, shrubs and herbaceous plants (heather, briars, broom, gorse, laburnum, etc).
3.1.1. Broad-leaved forestVegetated formation composed principally of trees, including shrub and bush understoreys, where broad-leaved species predominate (more than 75% of the formation)
3.1 ForestAreas occupied by forest or woodland with a vegetation pattern composed of native or exotic trees and which can be used for the production of timber or other forest products. The forest trees are under normal climatic conditions higher than 5 m with a canopy closure of 30% at least.
3.2 Shrubs and/or herbaceous vegetation associations
3.2.4 Transitional woodland/shrubBushy or herbaceous vegetation with scattered trees. Can represent either woodland degradation or forest regeneration/recolonisation.
Murbandy/Moland legend (6/9)
![Page 108: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/108.jpg)
108108CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
3.3.1.1 Dunes
3.3.1.2 Beaches
3.3.1.3 Inland sand
3.3.2.1 Littoral/sub-littoral rocks
3.3.2.2 Coastal cliffs
3.3.2.3 Inland cliffs/ bare rocks/ volcanic debris
3.3.3.1 Sparse vegetation on sand
3.3.3.2 Sparse vegetation on bare rocks
3.3.4 Burnt and damaged by disaster areasAreas affected by recent fires, still mainly black.
3.3.4.1 Burnt areas
3.3.5 Glaciers and perpetual snowLand cover by glaciers or permanent snowfields (glaciers and perpetual snow more than 50%)
3.3.3 Sparsely vegetated areasIncludes steppes, tundra and badlands. Scattered high-altitude vegetation. Vegetation layer covers between 15% and 50% of the surface.
3.3 Open spaces with little or no vegetation
3.3.1 Beaches, dunes and sand planesBeaches, dunes and expanses of sand or pebbles in coastal or continental locations, including beds of stream channels with torrential regime.3.3.2 Bare rockScree, cliffs, rock outcrops, including active erosion, rocks and reef flats situated above the high-water mark (75% of the land surface is covered by rocks).
Murbandy/Moland legend (7/9)
![Page 109: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/109.jpg)
109109CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
4.1.1.1 Marshes with reeds 4.1.1.2 Marshes without reeds 4.1.1.3 Open fen and transitional bog 4.1.2.1 Exploited peat bog with lawn communities4.1.2.2 Unexploited peat bog with lawn communities4.1.2.3 Peat bog with pool communities4.2.1.1 Salt marshes with reeds
4.2.1.2 Salt marshes without reeds
4.2.2 SalinesSalt-pans, active or in process of abandonment. Sections of saltmarsh exploited for the production of salt by evaporation. They areclearly distinguishable from the rest of the marsh by theirparcellation and embankment systems.
4.2.3 Intertidal flatsGenerally unvegetated expanses of mud, sand or rock lyingbetween high and low water marks. 0 m contour on maps.
4.1.2 Peat bogsPeatland consisting mainly of decomposed moss and vegetable matter. May or may not be exploited.
4.1.1 Inland marshesLow-lying land usually flooded in winter, and more or less saturated by water all year around.
4. WETLANDS
4.2 Coastal wetlands
4.1 Inland wetlands
4.2.1 Salt marshesVegetated low-lying areas, above the high-tide line, susceptible to flooding by seawater. Often in the process of filling in, gradually being colonized by halophilic plants.
Murbandy/Moland legend (8/9)
![Page 110: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/110.jpg)
110110CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
5.1.1.1 Canals
5.1.1.2 Rivers
5.1.2.1 Natural standing water5.1.2.2 Artificial reservoirs
5.2.1 Coastal lagoonsStretches of salt or brackish water in coastal areas which areseparated from the sea by a tongue of land or other similartopography. These water bodies can be connected to the sea atlimited points, either permanently or for parts of the year only.
5.2.2 EstuariesThe mouth of a river within which the tide ebbs and flows.
5.2.3 Sea and oceanZone seaward of the lowest tide limit.
5.2 Marine waters
5. WATER BODIES 5.1.1 Water coursesNatural or artificial water-courses serving as water drainage channels. Include canals. Minimum width for inclusion: 25 m
5.1.2 Water bodiesNatural or artificial stretches of water.
5.1 Inland waters
Murbandy/Moland legend (9/9)
![Page 111: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/111.jpg)
111111CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Open issues
![Page 112: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/112.jpg)
112112CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Open issues (1)
What is the best way to model land use: new dm, complex attributes to land cover classes, methods,…?
More “levels” in the data model (e.g: polygon > land cover > patch/stand > population/set > individual > part > material)
Hierarchical polygons ?
Attributes values by mean + standard deviation of the population/set?
Temporal existence/presence of elements?
“Multiple fuzzy membership”?
![Page 113: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/113.jpg)
113113CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Open issues (2)
Model and measure “mobile” elements presence ?:
Artificial: cars, trains,..
Natural:Animals:
Wild
Cattle
People
![Page 114: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/114.jpg)
114114CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Open issues (3)
Buildings
VegetationGeology
SoilsOther artificial elements
Agriculture Aquaculture
Natural areas:Seas
BiotopesHydrography
TransportIndustrial
EnergyMineralUtilities
![Page 115: image classification taxonomy](https://reader034.vdocuments.net/reader034/viewer/2022051012/54647f0caf795974338b4998/html5/thumbnails/115.jpg)
115115CEN TC 287 Mediterranean Workshop. Athens, 14 March 2008
Open issues (4)
Buildings
VegetationGeology
SoilsOther artificial elements
Agriculture Aquaculture
Natural areas:Seas
BiotopesHydrography
TransportIndustrial
EnergyMineralUtilities
Land Cover =(part of) the Core of theInspire ConsolidatedUML model ?