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GEOGRAPHICAL MAP ANNOTATION
WITH SOCIAL METADATA IN A
SURVEILLANCE ENVIRONMENT
Elena Roglia
Tutor: Prof.ssa Rosa Meo
Unive...
Overview
SMAT-F1 Project
Second Level Exploitation of data
Objectives and research questions
Multidimensional data man...
Sistema di Monitoraggio Avanzato
del Territorio – SMAT
SMAT Project aims at studying and demonstrating a
surveillance syst...
4
5
SMAT architecture
6
SMAT-F1, is the first phase of
SMAT project and aims to
demonstrate an integrated use
of three Unmanne...
SMAT-F1 Architecture
7
SS&C
Before mission: mission planning, UAS tasks
allocation.
During mission: mission monitoring, data
collection from th...
Second Level Exploitation activity
analyze and organize data collected during
missions
prepare mission reports
correlat...
Metadata Retrieval and Search
Our goal is to add metadata to geo-referenced
objects related to missions stored in the SS&...
Geo-referenced Spatial Objects
• Target
• Airport
• Route Waypoints
• Executed Route Waypoints (Flown Points).
11
Objectives and Research Questions
12
How to specify the interesting
spatial objects according to
the different
dimensions
...
Multidimensional approach
13
SMAT Multidimensional Data Model
14
Mission Facts
Mission facts are stored in relationship with
dimensions:
1. Mission in which the fact occurs
2. UAV perform...
Metadata Facts
Metadata facts are stored in relationship with
spatial objects and involve the dimensions:
1. Spatial objec...
17
Query
Abstract
Language
Specification
Compiler
18
GUI
COMPILER
GeoNames
OpenStreetMap
Abstract Specification Language
19
20
ASL Compiler: Back – end phase
Optimization
• identify mission facts that meet the conditions imposed
• identify spatial o...
22
Constraints on dimensions +
Spatial Object Types
Compiler
{(ObjectID, MetadataID)} +
{(ObjectID, Spatial coodinates)}
MDR Tester
The set of constraints the user specifies in
her/his query is not available a priori but is
known only at run-...
Web Search Process
25
Volunteered Geographic
Information - VGI
26
“is the harnessing of tools to create,
assemble, and disseminate
geographic da...
Geographical Social Metadata
27
OpenstreetMap
28
OSM Elements
Nodes (lat/lon-
username-
timestamp)
Ways (list of nodes)
Relations (nodes,
way)
29
GeoNames
30
over 10 millions of geographical names
7.5 millions of unique features:
elevation, population, postal codes,
a...
Web Services
31
http://api.openstreetmap.org/api/0.6/map?bbox=7.639,45.190,7.643,45.192
http://ws.geonames.org/wikipediaBo...
Spatial
Coordinates
Bounding Box
GeoNames URL
preparation
Web
Services
request
XML File
OpenStreetMap
URL preparation
Web
...
33
34
Files Comparison Process
Different?
35
37
Map annotation with significant
tags
38
Method
39
Test
40
Case study: 1
41
The map of Turin city and its neighbourhood
102 distinct tags occurring at least 2 times
84 statistical...
Case study: 2
Very elegant and touristic district of Turin
28 distinct tags occurring at least 2 times
19 statistical si...
Case study: 3
Everest Area
14 distinct tags occurring at least 2 times
9 statistical significant tags:
natural:water, n...
Case study: 4
30 Random Map in Europe:
No significant features
44
45
Significance of absent tags
46
Frequency
computation for
all tags in the
neighbourhood
Mean µ and
standard
deviation σ
Fre...
47
Absent tag
amenity:car wash
µ= 0,1042
σ = 0,3713
Method Comparison
M.TomkoandR.Pulves.“Venice,cityofcanals:
Characterizing regionsthrough content
Classification”.Transacti...
Empirical Method
Given a tag category we compute:
P1= the ratio between its frequency and the
sum of tag frequencies in th...
50
tag category is significant
and it is over-represented
in the central cell
tag category is significant
and it is under-...
Classification problem
• (TP) number of significant tags that are significant
for both methods;
• (FN) the number tags tha...
Case study: 1
52
The map of Turin city and its neighbourhood
137 significant tagsThreshold 0,125 0,167 0,333 0,5 1
# tags...
Case study: 2
Very elegant and touristic district of Turin
38 significant tags
53
Threshold 0,125 0,167 0,333 0,5 1
# tag...
Other
• Hills of Turin
• Industrial area of Turin
• Everest
• Random Maps
54
1. when statistical method does not identify
significant characteristics the classifier still
extracts significant tags, p...
56
Conclusions
Metadata Retrieval and Search Module
Allow the SS&C operator to show historical
metadata
Suggest new metada...
Future Work
• Spatial object annotation according to a
unique tagging system: adopting the tag
ontology provided by a uniq...
• The study of Data Mining methods for the
elaboration and the integration of Web
resources in order to make communicate t...
60
My pubblications
• E. Roglia, R.Meo, E.Ponassi, Geographical map annotation with significant tags
available from social ne...
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Geographical Map Annotation With Social Metadata In a Surveillance Environment

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Geographical Map Annotation With Social Metadata In a Surveillance Environment

  1. 1. GEOGRAPHICAL MAP ANNOTATION WITH SOCIAL METADATA IN A SURVEILLANCE ENVIRONMENT Elena Roglia Tutor: Prof.ssa Rosa Meo Università degli Studi di Torino Scuola di Dottorato in Scienza e Alta Tecnologia Indirizzo: Informatica
  2. 2. Overview SMAT-F1 Project Second Level Exploitation of data Objectives and research questions Multidimensional data management Metadata research, management and visualization Map annotation with significant tags Conclusions and future works 2
  3. 3. Sistema di Monitoraggio Avanzato del Territorio – SMAT SMAT Project aims at studying and demonstrating a surveillance system, to support: prevention and control of a wide range of natural events (fires, floods,landslides) environment protection against human intervention (traffic, urban planning, pollution and cultivation) 3
  4. 4. 4
  5. 5. 5
  6. 6. SMAT architecture 6 SMAT-F1, is the first phase of SMAT project and aims to demonstrate an integrated use of three Unmanned Air Vehicle (UAV) platforms inside of a primary scenario, relevant for the Piedmont Region.
  7. 7. SMAT-F1 Architecture 7
  8. 8. SS&C Before mission: mission planning, UAS tasks allocation. During mission: mission monitoring, data collection from the CSs, operator support in the interaction with the system After mission: conclusive report and Second Level Exploitation of data. 8
  9. 9. Second Level Exploitation activity analyze and organize data collected during missions prepare mission reports correlate data allow visualization, re-processing and retrieval of data according to the end-user needs provide a mechanism to retrieve and search metadata 9
  10. 10. Metadata Retrieval and Search Our goal is to add metadata to geo-referenced objects related to missions stored in the SS&C database Metadata are annotations provided by users of an open, collaborative system (see later!) The retrieval of annotations occurs by web services exported by the collaborative systems 10
  11. 11. Geo-referenced Spatial Objects • Target • Airport • Route Waypoints • Executed Route Waypoints (Flown Points). 11
  12. 12. Objectives and Research Questions 12 How to specify the interesting spatial objects according to the different dimensions involved? How to search relationships between already stored data? How to extract significant features in maps? How to enrich maps? How to generate a metadata retrieval and search module able to answer the requirements?
  13. 13. Multidimensional approach 13
  14. 14. SMAT Multidimensional Data Model 14
  15. 15. Mission Facts Mission facts are stored in relationship with dimensions: 1. Mission in which the fact occurs 2. UAV performing the mission 3. Payload sensor 4. Airport 5. Spatial target 15 Spatial dimensions
  16. 16. Metadata Facts Metadata facts are stored in relationship with spatial objects and involve the dimensions: 1. Spatial objects 2. Metadata creation time 16 Target Airport Route Waypoints Flown Points
  17. 17. 17 Query Abstract Language Specification Compiler
  18. 18. 18 GUI COMPILER GeoNames OpenStreetMap
  19. 19. Abstract Specification Language 19
  20. 20. 20
  21. 21. ASL Compiler: Back – end phase Optimization • identify mission facts that meet the conditions imposed • identify spatial objects based on these facts • identify metadata associated with these spatial objects Code Generation • SQL query statement generation 21
  22. 22. 22 Constraints on dimensions + Spatial Object Types Compiler {(ObjectID, MetadataID)} + {(ObjectID, Spatial coodinates)}
  23. 23. MDR Tester The set of constraints the user specifies in her/his query is not available a priori but is known only at run-time. The number of possible combinations is exponentially large Automatic procedure to test Compiler 24
  24. 24. Web Search Process 25
  25. 25. Volunteered Geographic Information - VGI 26 “is the harnessing of tools to create, assemble, and disseminate geographic data provided voluntarily by individuals” Goodchild, M.F., 2007. Citizens as sensors: the world of volunteered geography. Journal of Geography, 69(4):211-221
  26. 26. Geographical Social Metadata 27
  27. 27. OpenstreetMap 28
  28. 28. OSM Elements Nodes (lat/lon- username- timestamp) Ways (list of nodes) Relations (nodes, way) 29
  29. 29. GeoNames 30 over 10 millions of geographical names 7.5 millions of unique features: elevation, population, postal codes, administrative division, time zone, etc.
  30. 30. Web Services 31 http://api.openstreetmap.org/api/0.6/map?bbox=7.639,45.190,7.643,45.192 http://ws.geonames.org/wikipediaBoundingBox?north=45.192&south=45.18 &east=7.64&west=7.63
  31. 31. Spatial Coordinates Bounding Box GeoNames URL preparation Web Services request XML File OpenStreetMap URL preparation Web Services Request OSM File Well-Formed check Cache Storage 32
  32. 32. 33
  33. 33. 34 Files Comparison Process
  34. 34. Different? 35
  35. 35. 37
  36. 36. Map annotation with significant tags 38
  37. 37. Method 39
  38. 38. Test 40
  39. 39. Case study: 1 41 The map of Turin city and its neighbourhood 102 distinct tags occurring at least 2 times 84 statistical significant tags: highway: secondary, highway:pedestrian, highway: cycleway historic:monument, leisure:garden, amenity:fountain amenity:parking, amenity:atm, amenity:school, amenity:car sharing, amenity:hospitals, railway:station, shop:supermarket.
  40. 40. Case study: 2 Very elegant and touristic district of Turin 28 distinct tags occurring at least 2 times 19 statistical significant tags: amenity:fountain, amenity:parking, amenity:theatre, historic:monument, tourism:museum, railway:tram, amenity:place of worship, highway: pedestrian, amenity:bicycle rental, amenity:restaurant  amenity:atm, amenity:university,amenity:school, amenity:library, amenity:car sharing, amenity:hospitals, railway:station, amenity:pharmacy, railway:construction, shop: supermarket, shop:bicycle. 42 Case study 1
  41. 41. Case study: 3 Everest Area 14 distinct tags occurring at least 2 times 9 statistical significant tags: natural:water, natural:peak, natural:glacier, tourism:camp site, 43
  42. 42. Case study: 4 30 Random Map in Europe: No significant features 44
  43. 43. 45
  44. 44. Significance of absent tags 46 Frequency computation for all tags in the neighbourhood Mean µ and standard deviation σ Frequency computation in the central cell
  45. 45. 47 Absent tag amenity:car wash µ= 0,1042 σ = 0,3713
  46. 46. Method Comparison M.TomkoandR.Pulves.“Venice,cityofcanals: Characterizing regionsthrough content Classification”.Transactions inGIS,7:295–314,2009. Object category: over-representation (+) under-representation(-) 48
  47. 47. Empirical Method Given a tag category we compute: P1= the ratio between its frequency and the sum of tag frequencies in the central cell. P2=the ratio between its frequency and the sum of tag frequencies in the neighbourhood cells. 49
  48. 48. 50 tag category is significant and it is over-represented in the central cell tag category is significant and it is under-represented in the central cell over-representation (+) under-representation (-)
  49. 49. Classification problem • (TP) number of significant tags that are significant for both methods; • (FN) the number tags that are significant for proposed method but not for the empirical method; • (FP) the number tags that the empirical method defined to be significant but proposed method finds to be not significant; • (TN) the number of tags that both methods define to be not significant. 51
  50. 50. Case study: 1 52 The map of Turin city and its neighbourhood 137 significant tagsThreshold 0,125 0,167 0,333 0,5 1 # tags 161 161 156 151 133 Correlation 0,823 0,823 0,862 0,897 0,893 Precision 0,851 0,851 0,878 0,907 0,962 Recall 1 1 1 1 0,934 Threshold 1,2 1,4 1,6 1,8 2 2,2 2,5 # tags 125 119 118 115 113 105 100 Correlation 0,897 0,871 0,864 0,845 0,830 0,780 0,749 Precision 0,992 1 1 1 1 1 1 Recall 0,905 0,869 0,861 0,839 0,825 0,766 0,73 FP FN
  51. 51. Case study: 2 Very elegant and touristic district of Turin 38 significant tags 53 Threshold 0,125 0,167 0,333 0,5 1 # tags 70 70 68 68 52 Correlation 0,667 0,667 0,682 0,682 0,821 Precision 0,543 0,543 0,558 0,558 0,731 Recall 1 1 1 1 1 Threshold 1,2 1,4 1,6 1,8 2 2,2 2,5 # tags 48 44 43 40 40 39 37 Correlation 0,806 0,823 0,835 0,844 0,844 0,858 0,823 Precision 0,75 0,795 0,814 0,85 0,85 0,872 0,865 Recall 0,947 0,921 0,921 0,895 0,895 0,895 0,842 FP FN
  52. 52. Other • Hills of Turin • Industrial area of Turin • Everest • Random Maps 54
  53. 53. 1. when statistical method does not identify significant characteristics the classifier still extracts significant tags, producing many false positives as characteristics of the area. 2. when proposed method identifies significant features:  if their number is low, the classifier continues to produce an high number of false positives  if their number is high, the classifier improves in performance, reducing the number of false positives, but can make some mistakes producing false negatives. 55
  54. 54. 56
  55. 55. Conclusions Metadata Retrieval and Search Module Allow the SS&C operator to show historical metadata Suggest new metadata as annotation of the geo-referenced spatial objects Map annotation with significant tags 57
  56. 56. Future Work • Spatial object annotation according to a unique tagging system: adopting the tag ontology provided by a unique system as a referential knowledge base and then trying to learn the correspondences between tags in the different systems • Recognition of related annotations which appear to be different (different nouns or synonymous referred to the same concept). 58
  57. 57. • The study of Data Mining methods for the elaboration and the integration of Web resources in order to make communicate the world of ”Internet of Things” with the world of ”Semantic Web”. • The study and the application of an algorithm that suggests the area most characterized in order to apply the proposed statistical method. 59
  58. 58. 60
  59. 59. My pubblications • E. Roglia, R.Meo, E.Ponassi, Geographical map annotation with significant tags available from social networks, Chapter in XML Data Mining: Models, Methods, and Applications, A.Tagarelli (ed.), 26 pp, Idea Group Inc., to appear in February 2011. • E. Roglia, R.Meo, A SOA-Based System for Territory Monitoring, Chapter in Geospatial Web services:Advances in Information Interoperability, Peisheng Zhao and Liping Di (eds.), 27 pp, Idea Group Inc., October 2010. ISBN: 978-1609601928. • E.Roglia, R.Meo, A Composite Wrapper for Feature Selection, in Proceedings of Workshop on Data Mining and Bioinformatics in AI*IA - Intelligenza Artificiale e Scienza della Vita (DMBIO08) Cagliari (Italy), 13 September, 2008. • E.Roglia, R.Cancelliere, R.Meo, Classification of Chestnuts with Feature Selection by Noise Resilient Classifiers, in Proceedings of the 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN08) Bruges (Belgium), 23-25 April, 2008. 61

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