geospatial information integration...satellite image terraserver geocoded houses constraint...

Geospatial Information Integration

Craig A. Knoblock

University of Southern California

Craig A. Knoblock University of Southern California 2

IntroductionHuge amount of geospatial data and related online sources now availableGeographic Information Systems (GIS) primarily support the overlay of different layersOpportunity: Geospatial Information Integration

Support the retrieval, fusion, reasoning and learning across the available sources


Geospatial Information IntegrationGeospatial Data RetrievalGeospatial Data Fusion

Vector / Image FusionMap / Image Fusion

Geospatial ReasoningAccurately geocoding addressesIdentifying streets and buildings in satellite imageryPredicting the location of moving objects

Geospatial LearningLearning thematic maps

Geospatial IntegrationConclusions


Geospatial Data Retrieval

Structured data: databasesSemi-structured data: web pagesSpatial Data:

Vector data: points, lines, polygons, …Images: satellite, and aerial imageryMaps

Text documentsAudio & Video: TV & Radio on the web

Heracles: Framework to integrate heterogeneous data


Geospatial Data SourcesImagery


ImageryMaps

Geospatial Data Sources


ImageryMapsVectors

Geospatial Data Sources


Geospatial Data SourcesImageryMapsVectorsElevations


Geospatial Data SourcesImageryMapsVectorsElevationsPoints


Semi-structured Data Sources

Property tax sites



Property tax sitesTelephone books



Property tax sitesOnline telephone booksRailroad schedules…

<IRANIAN_RAILWAYS><TRAIN><ROW>

<CITY>Tehran</CITY> <TIME>12:35</TIME>

</ROW>…<ROW>

<CITY>Esfahan</CITY> <TIME>19:45</TIME>

</ROW></TRAIN><TRAIN><ROW>

<CITY>Tehran</CITY> <TIME>14:00</TIME>

</ROW>…

</TRAIN></IRANIAN_RAILWAYS>


Accurately Geocoding Addresses

Los Angeles County Assessor’s Site Property Tax Records

Satellite Image Terraserver Geocoded Houses

Constraint Satisfaction

Initial Hypothesis Result After Constraint Satisfaction

Street Vector Data Corrected Tiger Line Files

610, Palm or 645,Sierra

645, Sierra or 639,Sierra


604 or 642

604 or 610

642, Penn or 636,Penn

630,Penn or 628,Penn

636,Penn or 630,Penn

628,Penn or 624,Penn624,Penn or 618,Penn



604 610 645, Sierra

642,644,646 Penn 639, Sierra

636,638,640 Penn

630,632,634 Penn

633, Sierra

629, Sierra628, Penn

624, Penn623, Sierra

Address Latitude Longitude642 Penn St 33.923413 -118.409809640 Penn St 33.923412 -118.409809636 Penn St 33.923412 -118.409809604 Palm Ave 33.923414 -118.409809610 Palm Ave 33.923414 -118.409810645 Sierra St 33.923413 -118.409810639 Sierra St 33.923412 -118.409810

Address # unitsArea(sq ft)Lot size642 Penn St 3 1793 135.72 * 53.33 604 Palm Ave 1 884 69 * 42610 Palm Ave 1 756 66 * 42645 Sierra St 1 1337 120 * 62639 Sierra St 1 1408 121*53.5

Data Extracted from On-line Site


Information in Street Sources


Address-range Method of GeocodingSierra StFrom: A ( 33.923413, -118.408709 )To: B ( 33.924813, -118.408809 )

Addresses on the Left: 601-699Addresses on the Right: 600-698

645: Left Side22nd out of the 50 addresses on the left side

Interpolate the address on the street

A

B

Craig A. Knoblock University of Southern California 18Address-range (traditional) method


Uniform lot-size methodGet the information of the street segment from the street data sourceQuery the property tax source to get the number of parcels on a street segmentAssume all lots are equal in dimensionApproximate the location of the address based on the number of lots

Craig A. Knoblock University of Southern California 20Uniform lot-size method


Corner lot problem

Number of dimensions on the street =number of lots on the street +

corner lot


Actual Lot-Size MethodGet the coordinates of the block from the street data sourceQuery the property source and get the dimension of every lot on the blockCompute the dimensions of the 16 possible orientationsCompare these with the true dimensionThe layout that most closely matches / least error is chosen as the layout


Finding the optimal layoutCalculate the actual length and breadth (width) of the block using the information in the street data source[length, width]

Truedim

257

257

480480


136

240

482575

256

240

420575

204

240

482533

324

240

420533

136

120

542575

256

120

482575

204

120

542533

324

120

482533

136

256

542482

256

482482

256

204

256

542440

324

256

440

136

375

482482

256

375

420482

204

375

482440

324

375

440

482

420

Truedim

257

257

480480

Craig A. Knoblock University of Southern California 26Actual lot-size method


ResultsChosing a region

El SegundoData Source

Conflated TIGER/LinesFetch Agent Platform to convert website data into XMLPrometheus 2.0 information mediatorGeocoded 267 addresses spanning 13 blocksActual lot-size method could not be applied to 3 blocks (58 addresses)None of the methods could be applied to one addressResults based on the remaining 208 addresses


N

Chosen area for goecoding


Comparison of Results(all errors are in meters) Address-range Uniform lot-size Actual lot-size

Average Error 36.85359 7.87149 1.62993

Standard Deviation 20.49335 9.92361 1.46958

Minimum Error 0.86578 0.07086 0.03487

Maximum Error 73.80526 56.64072 7.80242

Average percentage of improvement over traditional approach

Uniform lot-size method: 78.65%Actual lot-size method: 95.59%


Address Range Methodµ = 36.85 σ =20.49

Uniform lot-size Methodµ = 7.87 σ = 9.92

Actual lot-size Methodµ = 1.63 σ = 1.47

Error in meter

Pro

bab

ilit

y

Normal Distribution of the error


Combining Online Schedules with Vectors and Points [Shahabi et al., 2001]

How do we efficiently determine which trains will pass a given point or region

Railroad vectors specify all possible paths of the trainsStations show the locations of the stopsSchedules provide the detailed timetable and stops

StationsRailroads

Schedules


Integrating Schedules with Vector DataApproach:

Create a wrapper for the online schedule and download it to a databaseMatch the names of the stations in the online schedule with the names of the stations in the gazetteer

Exploits work we have done on record linkage across sourcesAlign the points in the gazetteer with the vector data of the railroadsFind the shortest paths between the stationsCompute the trains that will pass a given region within some time interval

Determines how much real paths can deviate from the shortest distance between two points to compute this efficiently


Integrating Schedules with Vectors


Current TimeTime train enters area Time train exits area

Moving objectsSystem efficiently computes trains going through user selected area and time interval

Spatio-Temporal Integration


Learning Thematic MapsThe California county mapThe California county map


ApplicationThere is a map that partitions the 2-d space into disjoint regionsEach region is assigned a label

e.g., zip code areasa = 90007b = 90006, …

The label of each point is equal the label of its surrounding region

Original Map


Goal:Goal: find the best find the best approximationapproximation to the to the original maporiginal map

Given:Given: a set of data a set of data points and their points and their corresponding labelscorresponding labels

ApplicationProblem:

No access to the original map

Approximate MapOriginal Map


Classification Methods:Nearest Neighbor

For each point X in the training set, find a polygon including all points in the 2-d space which are closer to Xthan any other point in the set (Voronoi Diagram)


Classification Methods:Support Vector Machine

SVM is a classifier derived from statistical learning theory by Vapnik

Class 1

Class 2

Many decision boundaries Many decision boundaries can separate classescan separate classesWhich one should we Which one should we choose?choose?The decision boundary The decision boundary should be as far away from should be as far away from the data of both classes as the data of both classes as possiblepossible

We should maximize the marginWe should maximize the marginm


Experiments : Performance Measures

Area-based measures:

)area()area(

RetrievedRelevantRetrievedPrecision ∩

=

)area()area(

RelevantRelevantRetrievedRecall ∩

=


Experiments : Results••The zip map with USGS data The zip map with USGS data (area(area--based precision)based precision)

•• The performance is degradingThe performance is degrading

•• No more than 85% precisionNo more than 85% precision

•• RBF SVM outperformsRBF SVM outperforms


Experiments : Results••The zip map with USGS data The zip map with USGS data (area(area--based recall)based recall)

•• Recall is getting betterRecall is getting better

•• Up to 87% recallUp to 87% recall

•• RBF SVM still outperformsRBF SVM still outperforms


Building a Geospatial Mediator (PrometheusGeo)

Develop an general integration framework that can represent and integrate:

Imagery, maps, vector, elevation, and other geospatial typesDatabases and text sources with a geospatial extentExploit the various fusion and extraction operations described earlier

OpenGISGML (Geography Markup Language)

XML encoding for the transport and storage of geographic informationWMS (Web Map Service)

Support of the creation and display of maps of information that come from multiple sources that are both remote and heterogeneous

WFS (Web Feature Service)Support creation and display of features from various information sources


Example

JPL Map Server Capabilities File

GetCapabilities

Maps

JPL MapServer

Type = ‘Global_mosaic’Lat in (-90, 90)Long in (-180, 180)

JPL MapServer

Type = ‘US_Landsat’Lat in (23, 50)Long in (-127, -66)

…….

Mediator Model


Example

JPL Map Server

Maps

JPL MapServer

Type = ‘Global_mosaic’Lat in (-90, 90)Long in (-180, 180)

JPL MapServer

Type = ‘US_Landsat’Lat in (23, 50)Long in (-127, -66)

…….

Mediator Model

MediatorFind all available maps for Belgrade (44.82,21.41)

Obtain relevant maps by querying the model

GetMapRequests


Representation and Querying Processing in PrometheusGeo

Focus on querying and integrating geospatial layers GAV/LAV representation of sources is supportedBuilds on the Prometheus query representation and query processingAdd an explicit representation of quality where all sources have an associated quality across multiple dimensions (accuracy, resolution, time, etc.)User can specify their own quality metric


Mediation in MIX [Gupta et al.]Mediator defined by building an structured representation of both GIS and image sourcesGlobal integration model

Each term in the global model is described as a view on the sources

Mediator relations defined by:Containment conditionsSpatial or temporal joinsLogical associations

Queries and results in XML


ExampleProduce a table of aerial imagery and photographs of houses broken down by 5-year increments and Total Assessed Value


Result


Query Processing in VirGIS[Essid et al., 2004]

Focus on the mediation of geospatial vector dataAlso builds on the OpenGIS standards: GML, WFS, etc.Uses a LAV model with the restriction that all mappings have to be one-to-oneDoes support detailed querying of the vector layers


ExampleExtract all 2-lane roads that cross bridges and have a length of up to 1000 metersFor $x in document(bridge), $y in document(road)where cross ($x/geometry, $y/geometry) = true

and $x/length > 1000 and $x/lanes = 2return $xDistributes the query across the different vector sourcesCombines the results and returns it in XML


ConclusionsGoal is to move beyond current GIS systems that provided very limited integration capabilitiesExploits all related sources of information to enable the integration of and reasoning about geographic data Working towards a comprehensive integration framework for geospatial information

Support the rapid retrieval, fusion, and reasoning of geospatial data to provide new insights

geospatial information integration...satellite image terraserver geocoded houses constraint...

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