geographical map annotation with social metadata in a surveillance environment

GEOGRAPHICAL MAP ANNOTATIONWITH SOCIAL METADATA IN ASURVEILLANCE ENVIRONMENT

Elena RogliaTutor: Prof.ssa Rosa Meo

Università degli Studi di TorinoScuola di Dottorato in Scienza e Alta TecnologiaIndirizzo: Informatica

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Overview

SMAT-F1 ProjectSecond Level Exploitation of dataObjectives and research questionsMultidimensional data managementMetadata research, management and

visualizationMap annotation with significant tagsConclusions and future works

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Sistema di Monitoraggio Avanzato del Territorio – SMAT

SMAT Project aims at studying and demonstrating asurveillance 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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SMAT architecture

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.

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SMAT-F1 Architecture

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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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Second Level Exploitation activity

analyze and organize data collected during missions

prepare mission reportscorrelate dataallow visualization, re-processing and retrieval

of data according to the end-user needsprovide a mechanism to retrieve and search

metadata

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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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Geo-referenced Spatial Objects

• Target• Airport• Route Waypoints• Executed Route Waypoints (Flown Points).

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Objectives and Research Questions

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?

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Multidimensional approach

Metadata

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SMAT Multidimensional Data Model

Mission

UAV

Sensor

Target

Airport

Mission Facts

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Mission Facts

Mission facts are stored in relationship with dimensions:1. Mission in which the fact occurs 2. UAV performing the mission3. Payload sensor 4. Airport 5. Spatial target

Spatial dimensions

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Metadata Facts

Metadata facts are stored in relationship with spatial objects and involve the dimensions:

1. Spatial objects

2. Metadata creation time

TargetAirportRoute WaypointsFlown Points

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Query

Abstract Language Specificati

on

Compiler

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GUI

Metadata Retrieval

COMPILER

PostGis

GeoNames

OpenStreetMap

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Abstract Specification Language

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SELECT THE METADATA ASSOCIATED TO THESPECIFIED SPATIAL OBJECT TYPESINVOLVED IN THE MISSIONS SATISFYING ALL THE CONSTRAINTS

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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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Constraints on dimensions + Spatial Object Types

Compiler

{(ObjectID, MetadataID)} + {(ObjectID, Spatial coodinates)}

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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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Web Search Process

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Volunteered Geographic Information - VGI

“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

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Geographical Social Metadata

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OpenstreetMap

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OSM ElementsNodes (lat/lon-

username-timestamp)

Ways (list of nodes)Relations (nodes, way)

Key = Value

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GeoNames

over 10 millions of geographical names7.5 millions of unique features: elevation, population, postal codes,administrative division, time zone, etc.

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Web Services

MDR

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

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Spatial Coordinates

Bounding Box

GeoNames URL preparation

Web Services request

XML File

OpenStreetMap URL preparation

Web Services Request

OSM File

Well-Formed check

CacheStorage

Web Search Process

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COMPILER

Constraints, Object Types

ObjectID, CoordinatesObjectID, Metadata

Query

File Comparison

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HistoricalFiles

Files Comparison Process

New Filesfrom the Web

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Different?

Suggested metadata

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DEMO

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Map annotation with significant tags

tags on which the majority of the users agree

tags that annotate really typical features of the given area

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Method

Hypothesis: all the cells have the same law of features distribution

Central cell tags frequency computation

For each tag, frequency computation in the grid. Central cell excluded!

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Test

Sample: neighbouring cells

Mean µ and standard deviation σ of feature frequency is computed.

The frequency f of the feature in the central cell is compared with the distribution of frequencies in the sample.

Is f>µ+3σ?

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Case study: 1

The map of Turin city and its neighbourhood102 distinct tags occurring at least 2 times84 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.

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Case study: 2

Very elegant and touristic district of Turin28 distinct tags occurring at least 2 times19 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.

Case study 1

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Case study: 3

Everest Area14 distinct tags occurring at least 2 times9 statistical significant tags:

natural:water, natural:peak, natural:glacier, tourism:camp site,

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Case study: 4

30 Random Map in Europe:No significant features

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CASE STUDY 2

High frequency Significant

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Significance of absent tags

Frequency computation for all

tags in the neighbourhood

Mean µ and standard deviation σ

Frequency computation in the

central cell

Is f<µ-3σ?

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Absent tagamenity:car wash

µ= 0,1042σ = 0,3713

CASE STUDY 1

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Method Comparison

M. Tomko and R. Pulves. “Venice, city of canals: Characterizing regions through contentClassification”. Transactions in GIS, 7:295–314,2009.

Object category:over-representation (+)under-representation(-)

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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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P1>P2tag category is significant and it is over-represented in the central cell

P1<P2tag category is significant and it is under-represented in the central cell

ρ=P1/P2ρ<1

ρ>1 over-representation (+)

under-representation (-)

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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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Case study: 1

The map of Turin city and its neighbourhood137 significant tagsThreshold 0,125 0,167 0,333 0,5 1

# tags 161 161 156 151 133Correlation 0,823 0,823 0,862 0,897 0,893Precision 0,851 0,851 0,878 0,907 0,962Recall 1 1 1 1 0,934

ρ<1

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

ρ>1

FP

FN

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Case study: 2

Very elegant and touristic district of Turin38 significant tagsThreshold 0,125 0,167 0,333 0,5 1

# tags 70 70 68 68 52Correlation 0,667 0,667 0,682 0,682 0,821Precision 0,543 0,543 0,558 0,558 0,731Recall 1 1 1 1 1

ρ<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

ρ>1

FP

FN

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Other

• Hills of Turin

• Industrial area of Turin

• Everest

• Random Maps

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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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When the area to is stronglycharacterized, the empiricalmethod tends to produce more tags than those produced by proposed method, which acts, in general, as a morerestrictive filter for features

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Conclusions

Metadata Retrieval and Search ModuleAllow the SS&C operator to show historical

metadataSuggest new metadata as annotation of the

geo-referenced spatial objects

Map annotation with significant tags

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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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• 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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QUESTIONS?

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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.