Application of Topology to Complex Object Identification. Eliseo CLEMENTINI University of L Aquila

Transcription

1 Application of Topology to Complex Object Identification Eliseo CLEMENTINI University of L Aquila

2 Agenda Recognition of complex objects in ortophotos Some use cases Complex objects definition An ontology of spatial relations and operators Evaluation of results Research issues and conclusions

3 Question: what do you see in this image?

4 Question: what do you see in this image (the blue part)?

5 Question: what do you see in this image?

6 Answers Airports Rivers Destroyed buildings You discovered complex objects in an image My claim is: You evaluated spatial properties of simple objects by visual inspection You used your personal semantic knowledge base No much additional information

7 Observation: There must be geometric properties that we (humans) recognize (without metrics). GOAL: Automatic recognition of complex objects in ortophotos by the means of spatial and semantic information

8 Semantic knowledge Hierarchy of subparts airport runway control tower hangar terminal building Hierarchy of subclasses Security check Luggage claim Gates airport big airport small airport single runway shorter than 1,000 m dirt, grass, or gravel runways

9 Semantic knowledge: official sources Corine Land Cover, Urban Atlas LISA (Land Information System of Austria) LCML (Land Cover Meta Language)

10 Observation Official definitions say nothing about the spatial properties and relations among objects This is a gap that should be filled by research

11 Spatial knowledge Based on image resolution, we can identify simple objects that can be part of complex objects Size and shape of runway Size and shape of control tower Spatial relations between runway and terminal building

12 Spatial knowledge If resolution is high we might have better indications!

13 Recognition of complex objects in ortophotos We need to define a methodology to: find the intrinsic definition of a complex object (semantic and spatial) ontological develop an algorithmic strategy to assess the structural properties of a complex object procedural build an appropriate representation for the complex object (visual appearence on a target scale) visual

14 Recognition of complex objects in ortophotos Some use cases Complex objects definition An ontology of spatial relations and operators Evaluation of results Research issues and conclusions

15 LISA (Land Information System of Austria) The LISA is a model of the Austrian environment, representing Land Cover and Land Use objects. The LISA data model 14 classes of Land Cover categories (e.g., buildings, built-up areas, rocks,...); Land Cover is directly derived from EO data; 25 classes of Land Use categories (e.g., settlement, traffic, agriculture, forestry,...); Land Use is derived using additional spatial data, such as: Spatial planning (land use zoning plans) Street maps Agricultural information system Water information system

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17 LISA use cases Urban settlement (residential)

18 LISA use cases Urban settlement (industrial or commercial)

19 LISA use cases Road

20 LISA use cases Parking

21 LISA use cases Airport

22 LISA use cases Agricultural farmland

23 LISA use cases River basin

24 Recognition of complex objects in ortophotos Some use cases Complex objects definition An ontology of spatial relations and operators Evaluation of results Research issues and conclusions

25 Complex objects definition How an urban settlement is defined? From user requirements (cartography experts) we can define a set of rules that identify the object

26 Definition of urban settlement An urban settlement (residential) is defined as: A group of buildings Each building should be smaller than a certain size (otherwise the use of the building would be most likely nonresidential: commercial or industrial use) Small parts of different land cover (high and low vegetation, water, bare soil) connected to the buildings should be part of an urban settlement

27 Definition of urban settlement An urban settlement (residential) is defined as: Narrow segments of roads passing through the group of houses should be aggregated to the complex object, and parkings and culde-sac as well. Main roads should separate the urban settlements Other larger areas (woods, bare soil, and so on) should delimit the complex object as well

28 Complex objects definition Combination of constraints: Thematic constraints Geometric constraints Rules to identify the simple objects that are part of the complex object: e.g., boolean operator to check whether two objects are touching each other Geometric operations to build a representation of the complex object: e.g., a merge operator to combine two simple objects and a split operator to take a piece of a larger object

29 Procedure to build urban settlements 1. Start from a seed object (a given building) 2. Finding the neighboring objects (of given land cover classes) 3. Repeat the previous step with other neighboring objects 4. Stop when the aggregate is entirely surrounded by other constructed areas (roads, parkings, )

30 Procedure to build urban settlements 5. Repeat previous steps with other buildings not previously considered 6. Group the objects found till now in such a way there exist pairs of neighboring objects that are at a distance less than a given threshold (this means that they are separated by a secondary road) 7. Connect the groups of objects previously identified by some corridors

31 Procedure to build urban settlements 8. Filter the result to remove small holes and concavities (internal roads and parkings) 9. From the set of results, eliminate objects that have a size below a given threshold.

32 Test about Urban Settlements (residential areas)

33 Test about Urban Settlements (residential areas) aggregate

34 Test about Urban Settlements (residential areas) group

35 Test about Urban Settlements (residential areas) refine

36 Test about Urban Settlements (residential areas) validate

37 Recognition of complex objects in ortophotos Some use cases Complex objects definition An ontology of spatial relations and operators Evaluation of results Research issues and conclusions

38 Classification of spatial relations Cardinality Granularity Properties of geometric space Object type and dimension Representation level Spatial relations Embedding space dimension

39 Geometric space vs Cardinality of relations Geometric properties of spatial objects (unary relations) Topologic Two disconnected components A hole Projective concave four points of order 0 Metric Four right angles

40 Geometric space vs Cardinality of relations Binary relations Topologic A B A touch B Projective B A A inside concavity of B Metric A bigger than B

41 Geometric space vs Cardinality of relations Ternary relations Topologic B C A overlap Projective C A B A between B and C Metric equidistant

42 Geometric space vs Cardinality of relations N-ary relations Topologic network Projective surrounded by Metric grid

43 Granularity of relations Topological Relations Low granularity Fewer topological invariants are considered overlap Higher granularity More topological invariants are considered overlap + number of disconnected components of intersection

44 Taxonomy of spatial operators GO.01. Property Identification GO.PI.01. Size and Shape (of single object) GO.PI.02. Binary spatial relations GO.PI.03. N-ary spatial relations GO.PI.04. Network analysis GO.PI.05. Validate planar subdivisions GO.02. Geometry Transformation GO.GT.01. Skeleton GO.GT.02. Buffer GO.GT.03. Simplification GO.GT.04. Generalization GO.GT.05. Container GO.GT.06. Network transformation GO.GT.07. Planar subdivision enforcement GO.GT.08. Set operations

45 Some implemented operators Operator Is Elongated In Shape boolean iselongatedinshape(geometry inputgeometry, Double tolerance) 1 - Compute the inputgeometry s MBR 2 - Compute the ratio between the perpendicular edges of the MBR (A/B) if ratio >= tolerance return true else return false

46 Some implemented operators Operator Same Shape Orientation boolean sameshapeorientation(geometry firstinputgeometry, Geometry secondinputgeometry, Double elongatedtolerance, Double tolerance) 1. Check whether the firstinputgeometry and the secondinputgeometry are elongated in shape 2. Compute the firstinputgeometry s and secondinputgeometry s MBRs (MBR1 and MBR2) 3. Compute the angles α1 and α2 between the longest edges of MBRs and x-axis 4. If difference(α1, α2) <= π/8 ( + tolerance ) OR 7π/8 (- tolerance) <= difference(α1, α2) <= π return true else return false W NW N NE E SW S SE At most π/8 difference corresponds to same orientation

47 Some implemented operators Operator Fusion Geometry fusion(geometry inputgeometry, Double distance, int fusionoption) 1 option 0: neighboring boundaries fusion 2 option 1: convex hull in pairs fusion 3 option 2: convex hull fusion

48 Recognition of complex objects in ortophotos Some use cases Complex objects definition An ontology of spatial relations and operators Evaluation of results Research issues and conclusions

49 Urban settlements (residential)

50 Examples of different fusion and grouping distance parameters Fusion 0 Distance 11 Distance 13 Distance: 30 Fusion 1 Fusion 2

51 Parking lots

52 Urban settlements (industrial or commercial)

53 Agricultural farmland

54 Destroyed buildings

55 Road Network

56 Peculiarities of road networks

57 Classification Classification Accuracy Assessment Error matrix by previous user data Error of commission by Topology System Reference data Single buildings aggregation not aggregated aggregated total User's accuracy not aggregated ,2 aggregated ,3 total Producer's accuracy 81,8 97,1 Error of omission Urban fabric for UA Overal accuracy 87,0 Kappa index 0,731 Reference data not aggregated aggregated total User's accuracy not aggregated , aggregated ,7 total Producer's accuracy 73,7 97,7 Overal accuracy 84,0 Kappa index 0,686

58 Recognition of complex objects in ortophotos Some use cases Complex objects definition An ontology of spatial relations and operators Evaluation of results Research issues and conclusions

59 Classification techniques Pixel-based classification and Segmentationbased classification Multi-temporal rules: based on land cover evolution over time (e.g., soccer field vs corn field) Multispectral images can make finer distinctions (e.g., shadows vs deep water, roofs vs paved roads) GEOBIA (geospatial object based image analysis): to develop methodology for automated or semiautomated classification of geographical elements or complex physical features of Earth land cover

60 State of the art Agencies (e.g., Environmental Protection Agency of Austria) use a combination of automated and manual approaches, based on expert knowledge, to derive land use information from land cover maps. They use ancillary data as well. These methodologies are expensive, time consuming and subjective. In other projects, semi-automatic procedures are applied: e.g, to produce GMES Urban Atlas maps, image analysis packages such as ecognition are used.

61 Proposed approach Automatic recognition of complex objects by combined spatial rules and thematic information. Advantages: once the ontological part (spatial rules) is defined, the process is automatic; the process can be carried out from land cover data without a costly integration with other data sources; direct use of vector data in standard OGC format, which facilitates the integration with other systems; capability of modeling complex objects with a rich internal structure, made of parts and subparts obeying to spatial constraints; the methodology is independent of scale and graphical representation: for instance, the same complex object can have different graphical representations, depending on context and scale.

62 Research Issues How far can we go by checking geometric properties only?

63 Research Issues Definition of a declarative language to express spatial association rules with support and confidence

64 Research issues How to discover the spatial rules that define complex objects? Translating domain experts knowledge Validation of the rule application vs reference data By spatial data mining techniques Automatic extraction of spatial association rules with support and confidence

65 Research Issues Define a modeling tool that is able to represent the ontological part: complex objects, hierarchical structure, spatial association rules.

66 Research Issues Define a high level language for the procedural part From complex objects semantics, it allows us to describe procedures to obtain visual representations at given scale and context.

67 Research Issues Direct application to orthophotos Is it possible to apply directly the methodology to orthophotos without a pre-classification step? It is highly desirable No loss of information due to the pre-classification process Customization of classification process based on needs Approximate spatial relations can be adopted in spatial association rules E.g., building nearly touches road

68 Acknowledgements granted by European Space Agency (ESA), through the Support To Topology (STO) project ( Topology Software System (TSS), developed by SISTEMA GmbH, Vienna, Austria. Environmental Protection Agency of Austria (UBA), Department for Biodiversity and Nature Conservation, Vienna, for providing LISA images GISAT, Praha, Czech Republic, for providing Urban Atlas images

69 Application of Topology to Complex Object Identification Eliseo CLEMENTINI University of L Aquila