CARTOGRAPHIC GENERALIZATION

Transcription

1 CARTOGRAPHIC GENERALIZATION FOR 2D MAP OF URBAN AREA Presented At India Conference on Geo-spatial Technologies & Applications April 12-13, 2012 GISE Lab,Department of Computer Science and Engineering, IIT Bombay Prsented By Dr. J.L.Raheja DSG Group, CEERI, PILANI e.mail:

2 Introduction Map at different scales 1:10,000 1:25,000 1:50,000

3 Cartographic Generalization Is the science and the art to retain the important aspects (entities) in accordance with the purpose (thematic) and the scale of a particular map and the exclusion of irrelevant details that may overload the map and confuse its user. Org. Map Reduced Generalized

4 Objectives: To develop algorithms for various important 2D generalization processes such as selection, simplification and aggregation. Selection Simplification Aggregation Typification Displacement Amalgamation

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10 Selection

11 Polyline Simplification Simplification Algorithm

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15 WORK DONE Results

16 WORK DONE Simplification

17 SIMPLIFICATION OF BUILDINGS

18 Delhi Saraojini Nagar

19 ORIGINAL GML IMAGE

20 Simplification of Buildings for the Tolerance & Threshold Area

21 Simplification of Buildings for the Tolerance & Threshold Area

22 Simplification of Buildings for the Tolerance & Threshold Area

23 Simplification of Buildings for the Tolerance & Threshold Area

24 ORIGINAL GML IMAGE Loading the file which needs to be simplified

25 Simplification of Buildings for the Tolerance & Threshold Area

26 Simplification of Buildings for the Tolerance & Threshold Area

27 Simplification of Buildings for the Tolerance & Threshold Area

28 Simplification of Buildings for the Tolerance & Threshold Area

29 Simplification of Buildings for the Tolerance & Threshold Area

30 Delhi saraojini nagar (Modified)

31 Enter Tolerance = 10 Enter Threshold Area = 50

32 Enter Tolerance = 10 Enter Threshold Area = 100

33 Enter Tolerance = 10 Enter Threshold Area = 200

34 Enter Tolerance = 10 Enter Threshold Area = 300

35 Original GML Data

36 Enter Tolerance = 10 Enter Threshold Area = 50

37 Enter Tolerance = 10 Enter Threshold Area = 60

38 Enter Tolerance = 10 Enter Threshold Area = 70

39 Enter Tolerance = 10 Enter Threshold Area = 80

40 Enter Tolerance = 10 Enter Threshold Area = 90

41 Enter Tolerance = 10 Enter Threshold Area = 100

42 Delhi Saraojini Nagar (Modified)

43 Aggregation Aggregation is the task of grouping a selected set of like entities to form one entity by simplifying its representation over the original footprint. Aggregation may be performed for a number of reasons: a.when the density of buildings within an area is high, resulting in conflicts such as overlapping symbols. b.when one or more buildings are too small to be represented individually..

44 44 Constraints Due to these relations among different objects in proximity

45 Structure recognition: Constraints for 3D buildings Micro Level: Accuracy Orientation Shape Size Functionality Look Preserve its initial position Preserve its main orientation Preserve its orthogonality Exceed the minimum volume limit Preserve important buildings Preserve texture, color and exterior outlook 45

46 Structure recognition: Constraints for 3D buildings Micro Level: Accuracy Orientation Shape Size Functionality Look Preserve its initial position Preserve its main orientation Preserve its orthogonality Exceed the minimum volume limit Preserve important buildings Preserve texture, color and exterior outlook Meso Level: Topology Proximity Connectivity, adjacent and inclusion The relative distance between buildings relationships 46

47 Structure recognition: Constraints for 3D buildings Micro Level: Accuracy Orientation Shape Size Functionality Look Meso Level: Topology Proximity Macro Level: Preserve its initial position Preserve its main orientation Preserve its orthogonality Exceed the minimum volume limit Preserve important buildings Preserve texture, color and exterior outlook Connectivity, adjacent and inclusion relationships The relative distance between buildings Density distribution should be maintained 47

48 48 Rules based on these constraints

49 Aggregation Rules Linkage rules: Based upon spatial relations such as: Semantic rules: Belong to same class Orientational rules Define the historical or local importance of the building. Contextual rules based upon different views. Structural rules Apply to group of buildings forming a perceptual structure - If d(oi,oj) < Δdmin then If h(oi,oj) < Δhmin then If roof_typei = roof_typej then If ΔA(oi,oj) < ΔAmin then aggregate Else if Ai < Amin and Oi is important exaggerate Else unchanged then 49

50 Start Read input data Are both buildings commercial or residential? No Read roads Select road No Is a road? Yes Compute adjacent buildings on both sides Select buildings on next side Select remaining buildings Is building important? Angle< MinAngle Yes Buildings belong to same owner? Yes No Yes No Yes No Compute Delaunay triangulation Select two nearby buildings Distance<ProxDist No Alignment <MinAlignment Apply aggregation Yes No Building sizediff<minsize Is geometry of buildings alike? If both buildings are KACHCHA or PAKKA?. Yes Yes Yes No No No No No No Are all road side buildings aggregated? Yes Remaining buildings scanned? (L1) Remaining road scanned? Yes Yes Both buildings are old or new? Buildings of same type (simple or complex) Yes Yes No No Save and display result End Yes

51 Results

52 Results

53 Results

54 Results

55 Selection Simplification Aggregation Typification Displacement Amalgamation

56 Typification It is based on Kohonen Feature Nets, a neural network learning technique. The prominent propertyof this unsupervised learning method is the fact that the neurons are adapted to a new situation (the attractors), while keeping their spatial ordering - topology 56

57 Typification Original spatial distribution 57

58 Typification A subset of the original objects is chosen to represent the new situation. A simple, random selection of objects yields a reduced number of objects However it will usually not represent the original spatial distribution 58

59 Typification 59 Original Typified

60 60 Another Example

61 61 Another Example

62 62 Application

63 63 Applications: Navigation

64 64 Applications: Tourist Mobile Maps

65 Applications: Tourist Mobile Maps City planing One can use the online city model to do urban planning and instantly present their plans to the public. 65

66 Applications: Tourist Mobile Maps Education Children and adults alike can learn about other people s cultures and lands, by visiting their cities around the world online 66

67 Applications: Tourist Mobile Maps Wireless Network Design Engineers will be able to do design directly on the online city model, instead of relying on field survey, to optimize communication networks 67

68 Applications: Tourist Mobile Maps Disaster Management The digital city can help us for emergency management, terrorism, flooding, severe weather, urban fire, wildfire and personal disaster preparedness 68

69 Papers published Raheja, J., and Meng, L. (2002): Rules and constraints for 3D generalization of urban area, Acta Simica Geographica, Beijing 11, Lal, J. (2003): "Using Genetic Algorithms to Displace Conflicting Objects in 3D", Visualisierung und Erschließung von Geodaten: Tagungsband zum DGfK Seminar GEOVIS 2003 (Aktuelle Entwicklungen in Geoinformation und Visualisierung), GEOVIS 2003, Hannover, Lal, J. Meng. L. (2003): "Aggregation on the Basis of Structure Recognition". Fifth Workshop on Progress in Automated Map Generalization. International Cartographic Association Commission on Map Generalization, IGN, Paris 2003 Lal, J. & Meng, L. (2004): 3D building recognition using artificial neural network.ica Workshop on Generalization and Multiple representation, Leicester, UK, August 20-21,

70 Thank You!! Dr. J.L.Raheja DSG Group, CEERI, PILANI Mob