PhD dissertation

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. 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
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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)
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4. 4

5. 5

6. SMAT architecture
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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. SMAT-F1 Architecture
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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.
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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
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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
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11. Geo-referenced Spatial Objects
• Target
• Airport
• Route Waypoints
• Executed Route Waypoints (Flown Points).
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12. Objectives and Research Questions
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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. Multidimensional approach
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14. SMAT Multidimensional Data Model
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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
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Spatial dimensions

16. Metadata Facts
Metadata facts are stored in relationship with
spatial objects and involve the dimensions:
1. Spatial objects
2. Metadata creation time
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Target
Airport
Route Waypoints
Flown Points

17. 17
Query
Abstract
Language
Specification
Compiler

18. 18
GUI
COMPILER
GeoNames
OpenStreetMap

19. Abstract Specification Language
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20. 20

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
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22. 22
Constraints on dimensions +
Spatial Object Types
Compiler
{(ObjectID, MetadataID)} +
{(ObjectID, Spatial coodinates)}

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
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24. Web Search Process
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25. Volunteered Geographic
Information - VGI
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“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. Geographical Social Metadata
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27. OpenstreetMap
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28. OSM Elements
Nodes (lat/lon-
username-
timestamp)
Ways (list of nodes)
Relations (nodes,
way)
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29. GeoNames
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over 10 millions of geographical names
7.5 millions of unique features:
elevation, population, postal codes,
administrative division, time zone, etc.

30. Web Services
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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. 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
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32. 33

33. 34
Files Comparison Process

34. Different?
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35. 37

36. Map annotation with significant
tags
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37. Method
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38. Test
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39. Case study: 1
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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. 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.
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Case study 1

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,
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42. Case study: 4
30 Random Map in Europe:
No significant features
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43. 45

44. Significance of absent tags
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Frequency
computation for
all tags in the
neighbourhood
Mean µ and
standard
deviation σ
Frequency
computation in
the central cell

45. 47
Absent tag
amenity:car wash
µ= 0,1042
σ = 0,3713

46. Method Comparison
M.TomkoandR.Pulves.“Venice,cityofcanals:
Characterizing regionsthrough content
Classification”.Transactions inGIS,7:295–314,2009.
Object category:
over-representation (+)
under-representation(-)
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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.
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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. 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.
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50. Case study: 1
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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. Case study: 2
Very elegant and touristic district of Turin
38 significant tags
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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. Other
• Hills of Turin
• Industrial area of Turin
• Everest
• Random Maps
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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.
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54. 56

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
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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).
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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.
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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.
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