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Computers & Geosciences 36 (2010) 1115–1122
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Computers & Geosciences
0098-30
doi:10.1
n Corr
E-m
jheo@yo
journal homepage: www.elsevier.com/locate/cageo
A new method for matching objects in two different geospatial datasetsbased on the geographic context
Jung Ok Kim a, Kiyun Yu a,n, Joon Heo b, Won Hee Lee c
a Department of Civil & Environmental Engineering, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-742, Koreab School of Civil & Environmental Engineering, Yonsei University, 262 Seongsan-ro, Seodaemun-gu, Seoul 120-749, Koreac Department of Civil Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 501-759, Korea
a r t i c l e i n f o
Article history:
Received 30 April 2009
Received in revised form
8 March 2010
Accepted 20 April 2010
Keywords:
Geographic context
Voronoi diagram
Object matching
Spatial similarity
Triangulation
04/$ - see front matter & 2010 Elsevier Ltd. A
016/j.cageo.2010.04.003
esponding author. Tel.: +82 2 880 1355; fax:
ail addresses: [email protected] (J.O. Kim), k
nsei.ac.kr (J. Heo), [email protected] (W.H
a b s t r a c t
Although several methods of handling object matching problems across different datasets have been
developed, there is still a need to design new approaches to address the diverse matching applications.
Such cases include those where the coordinate differences in datasets are significant, where the shapes
of the same objects are dissimilar, or even where the shapes are too similar for different objects. This is
especially true, as many large portals worldwide are opening their spatial databases to public access by
providing an open application programming interface (API). With this understanding, we propose in
this paper a new method for matching objects in different datasets based on geographic context
similarity measures. The proposed method employs and combines a set of concepts such as buffer
growing, Voronoi diagrams, triangulation, and geometric measurements. This approach is simple in its
algorithm but powerful in resolving situations when two datasets have significant coordinate
discrepancies. In addition, the concept is highly effective regardless of the shapes of objects. After
testing the method for the two major digital datasets in Korea, we found that the matching success rate
reached 99.4%.
& 2010 Elsevier Ltd. All rights reserved.
1. Introduction
The integration of heterogeneous spatial data, which includestheir conflating, sharing, and linking, is a very important researchtopic related to geographic information systems. New informationnot available from a single data source is available through theintegration of spatial data. Integration usually involves matchingthe common spatial objects in different datasets.
There have been various research projects on this topic. Yuanand Tao (1999) obtain centroids of polygon objects and calculatethe distances between these for matching. Beeri et al. (2005)compare distances and develop a location-based database to joinalgorithms for point datasets. Walter and Fritsch (1999) identifyand eliminate unlikely matching pairs using relational parameterssuch as topological information and feature-based parameterssuch as line angles. Gosseln and Sester (2004) apply severalsimilarity measures, such as the degree of overlap or theHausdorff distance (Rucklidge, 2004), to detect correspondingobjects. In order to compare the shape similarity of individualobjects, a turning function is used to describe the amount of
ll rights reserved.
+82 2 873 2684.
[email protected] (K. Yu),
. Lee).
change between two shapes (Arkin et al., 1991; Frank and Ester,2006; Longin and Lakamper, 2000).
In the cases cited, the matching of common objects in differentgeospatial datasets is for the most part obtained by usinggeometric methods, because spatial objects have geographiccoordinates and shape. Geometric methods imply a comparisonof the distance, angle, and shape contrast between objects. Inaddition, an overlay analysis using a buffer tool, map alignment(Saalfeld, 1988), or rubber-sheeting (Doytsher, 2000) betweentwo datasets are used as well.
Geometric methods are good at dealing with some cases butnot others. In some circumstances, too much mismatching orunder-matching in the result requires some human intervention.To be more specific, in Korea, large-scale apartment complexesare very common and they are composed of a number of spatialobjects of the same shape and size. In such cases, matchingattempted by geometric methods using, for example, overlay andshape similarity analyses is inaccurate. This is common when thedatasets used have coordinate discrepancies of hundreds ofmeters, which is common in many digital spatial databasesbecause of the adoption of different datum and plane coordinatesystems.
There have been recent studies on object matching usingattribute or semantic information in a geographic context. Theseuse spatial relationships between spatial objects (Cueto et al.,
Map 1
A
B
A
B
Map 2
Fig. 1. Geographic context of objects A and B in two maps. Their contexts are
represented as graphs.
J.O. Kim et al. / Computers & Geosciences 36 (2010) 1115–11221116
2000; Samal et al., 2004). Among these, one notable researchpaper by Samal et al. (2004) models geographic context using aproximity or star graph and a mechanism to compute thesimilarity of two geographic contexts. The total vector offset ofthe common objects in both maps was calculated by overlayingtwo proximity graphs. The total vector offset provided by theseobjects being matched is seen to be small. However, thisproximity graph method has a limitation: it selects all the objectswithin a certain radius as targets for the graph, but only some ofthem are used for comparison and thus unnecessary computationis required. Notwithstanding this limitation, the proximity graphapproach has paved a new way to deal with the matching ofobjects based upon their geographic context.
Notwithstanding the progress already made, there is still astrong need to develop other methods under the umbrella of thesame approach. This is especially important considering theapplication potential of object matching. For example, in Korea,many large portals recently released their spatial databases as anopen API strategy. When more spatial databases become open topublic access, more diverse object matching methods will berequired to cover various applications in the matching ofheterogeneous datasets. Therefore, it is with this understandingthat we propose in this paper a new object matching method. Weintroduce a simple but powerful method to analyze thegeographic context of the two objects to be compared. Bymeasuring the context similarity of two objects based on Voronoidiagrams (O’Rourke, 1998) and triangulation geometry, themethod we propose effectively matches two objects in hetero-geneous datasets.
This paper is organized as follows: Section 2 containsa detailed explanation of the proposed matching algorithm.Section 3 presents our experimental results. Our conclusions aregiven in Section 4.
2. Proposed algorithm
2.1. Matching based on geographic context
We propose a new matching method for common objectsusing their geographic context in different datasets. The geo-graphic context refers to the spatial relationship betweenneighboring objects (Samal et al., 2004). For example, as shownin Fig. 1, the geographic context of object A is similar in bothmaps, provided that the selected neighbor objects are the same inthe two maps. This is also true for object B. Here, even though theobjects A and B are hard to discern their shapes, they can bediscriminated in matching by analyzing their geographicrelations. From this point of view, the proposed method tries tomeasure the geographic context similarity of objects. In order toachieve this, a set of traditional concepts such as buffer growing,Voronoi diagrams, and triangulations are adopted and combined.A flowchart showing the process of the proposed method isshown in Fig. 2. This is followed by a detailed explanation.
In the first step, the centroid of an input polygon object iscalculated in the reference dataset and a buffer zone is created.Using such a centroid and corresponding coordinates, a bufferzone of the same radius is created in the target dataset. For thesecond step, a set of landmarks are found by comparing theattributes of objects within the buffer zones in both datasets. A setof centroids for the selected landmark objects are then derived togive a point dataset for the reference and target datasets. Thirdly,a Voronoi diagram is created for each dataset using the derivedlandmark point dataset. In this step, the core Voronoi cell is foundby overlaying the input polygon object over the reference Voronoidiagram. Here, the input polygon object belongs to the core
Voronoi cell. Based on this information, the candidate Voronoi cellis identified using the landmark in the core Voronoi cell where thecore and candidate cells share the same landmark. The candidateobjects for matching in the target dataset are then defined byoverlaying the candidate Voronoi cell over the target dataset. Inthe fourth step, a reference triangulation is constructed based onthe input polygon object and corresponding neighbor landmarksin the reference dataset. Similarly, a series of candidate triangula-tions are made based on the candidate objects and correspondingneighbor landmarks in the target dataset. Finally, the geographiccontext similarities are calculated based on a comparison oftriangulations, which leads to the identification of the matchingpair. In the sections below, we clarify these steps.
2.2. Selection of landmarks
A comparison using the semantic information of datasets isalso referred to as an attribute method. Provided both objectshave a common attribute field and that the semantics of datavalues in that field are the same for both the objects, the objectsmay be matched. In this paper, the matched object pairs arecharacterized as landmarks. Two datasets are analyzed to definethe common attribute field. In Fig. 3, the attribute field ‘‘Name’’ indataset A has a semantic correspondence with ‘‘BD_NM’’ indataset B, such that they constitute a common attribute field.Once the common attribute field is defined, the next step is tocompare the data value of each object in dataset A with that ofeach object in dataset B. Only those objects that have correctsemantic matching are chosen as landmarks, so partially matchedobjects are not selected. In addition, only one-to-one relationshipsare selected, thus excluding landmarks with one-to-manyrelationships.
The selection process is an important part of the proposedmethod. Here, we describe an approach to determine landmarksautomatically in different geospatial datasets. As shown in Fig. 4,the automatic mechanism for selecting landmarks consists of
Reference
Dataset
Target
Dataset
Calculate centroid of
input polygon object
Create buffer zone Create buffer zone
centroid info.
• Extract common landmarks by comparing name attribute
• Calculate centroid of landmarks
• Build point dataset for landmarks
Voronoi cell with input
polygon objectSelect candidate objects
VoronoiDiagram
cell info.
• Create Reference triangulation
• Save ID, area, perimeter of Δ• Create Candidate triangulation
• Save ID, area, perimeter of Δ
Compare geographic context similarity
Object matching
Fig. 2. Flowchart of proposed method.
ID
11 Polygon ABC 10
12
13 Polygon GHI 6
14
15 Polygon AB 2
ID
21 Point DEF 457001
22
23 Point GHI 457010
24
25 Point ABD 457021
Semantic correspondence: landmark
Dataset A Dataset B
Shape Name Floor ….
Polygon DEF 2
Polygon JKL 3
Shape BD_NM RD_SN ….
Point ABC 457002
Point DEF 457011
Fig. 3. Selection of landmarks from two datasets. Landmarks are matching pairs that have same name attribute in two geospatial datasets.
J.O. Kim et al. / Computers & Geosciences 36 (2010) 1115–1122 1117
three steps: blocking the datasets, selecting the landmarks, andverifying the landmark table.
2.2.1. Step 1: Blocking the datasets
The proposed automatic selection process starts with blockingthe two datasets; i.e., the reference and the target datasets.Thus, the blocking step (Kang et al., 2007; Bilenko et al., 2006)partitions the two datasets from which two regions are derived(Fig. 4, STEP 1). To derive the conjugate regions, an input objectwith location and preset radius are defined and applied to thereference dataset. The corresponding location of the input objecton the target dataset is then selected followed by application ofthe preset radius to create a buffer zone. This buffer zone is the
conjugate region on the target dataset. Once these regions arederived, the datasets within these regions constitute two sets ofcandidate landmarks, one from the reference dataset and theother from the target dataset.
2.2.2. Step 2: Selecting the landmarks
A landmark can be a building or any spatial object that is easilynoticed. It is likely to have the same name in different geospatialdatasets. Therefore, in the second step, the landmarks are selectedby comparing the name attribute of spatial objects within twocandidate landmarks (Fig. 4, STEP 2, upper figure). As the nameattribute is a string, string-matching algorithms (for example,Duda et al., 2001; Cormen et al., 2001; Charras and Lecroq, 2004)
Reference dataset
Target dataset
Blocking –Reference dataset
STEP 1 STEP 2
Blocking –Target dataset
Compareattributes
Createlandmarks table
REF_IDREF_ID TAR_IDTAR_ID REF_IDREF_ID TAR_IDTAR_ID
INP.INP. NEARNEAR DIST.DIST. ANG.ANG. VERI.VERI.
1 26 1 1 11 22 1 02 12 0 0 02 47 0 0
Updatelandmarks table
Verify
NAMENAME NAMENAME
Fig. 4. Schematic overview of automatic selection approach.
J.O. Kim et al. / Computers & Geosciences 36 (2010) 1115–11221118
are used to compare the name attributes. If a name of thereference candidate landmark is equal to that of any targetcandidate landmark, the objects are determined as a matchingpair. The detailed matching conditions of two names are exactstring matches and a one-to-one relation. If matching pairssatisfying these conditions are found, the matching results arestored with their identification number and location informationin the landmark table (Fig. 4, STEP 2, lower figure).
2.2.3. Step 3: Verifying the landmark table
The final step is to verify the location accuracy of the selectedlandmark in the previous step. This step is necessary in theproposed automatic selection process because only a correctlandmark is used to derive the Voronoi diagram, triangulation,and the geographic context similarity, which play an importantrole in the matching objects. They are described in Sections 2.3and 2.4. Thus, the landmark has to be based on correspondingobject pairs that have the same or similar locations as well as thesame name. In order to verify each pair of landmarks in thelandmark table, we propose a modified fingerprint recognitionapproach (van Wamelen et al., 2004). The proposed method usesthe relative location, such as distance and angle, between alandmark and its neighbor landmark (Fig. 4, STEP 3, upper figure)that can be applied as follows.
01:
Search the k(r) nearest neighbor landmarks of a referencelandmark r in the landmark table.02:
Find a corresponding target landmark t of a landmark r andthe corresponding k(t) landmarks of the k(r) landmarks in thelandmark table.03:
Compute the angle areference and distance breference betweenlandmark r and landmark k(r).04:
Compute the angle atarget and distance btarget between land-mark t and landmark k(t)05:
Da¼9areference�atarget9oTa, where Da is the angle differencebetween the reference and target; a is the angle between astraight line connecting a landmark and its neighbor and thex-axis, and Ta, is a threshold.06:
Db¼9breference�btarget9oTb, where Db is the distance differ-ence between the reference and the target; b is the Euclidiandistance between a landmark and its neighbor, and Tb is athreshold.
Here, if a landmark r and landmark t satisfies all the criteria,they will be confirmed as the landmark, while others will berejected and excluded. The landmark table is then updated (Fig. 4,STEP 3, lower figure).
2.3. Voronoi diagrams and triangulations
In the literature (for example, Samal et al., 2004), thegeographic context is usually represented by a proximity graphthat defines objects as points connected by lines. The graph isevaluated using parameters such as distance, angle, and direction.Rather than using such a proximity graph, in this paper, theVoronoi diagram, triangulation, and the corresponding geometricmeasurements are used.
There are two advantages in using the Voronoi diagram.Firstly, this allows the definition of a set of contiguous polygons.As shown in Fig. 5(a), polygons having landmarks from L1 to L7are the contiguous polygons. By defining these features, we canselect several critical landmarks. In other words, it is possible tofilter out those landmarks having a minor influence whenanalyzing the geographic context of the input polygon objectand the surrounding landmarks. Contiguous polygons commonlyfigure in computational geometry, together with algorithmicconsiderations (for example, O’Rourke, 1998). Secondly, theVoronoi diagram allows the definition of a limited number ofcandidate objects in the target dataset. From Fig. 5(b), the objectslabeled from C1 to C7 are the candidate objects in the targetdataset. Identification of the candidate objects occurs as severalsubprocesses. A Voronoi diagram consists of a set of Voronoi cellsthat contain corresponding landmarks. A cell with the inputpolygon object is called the core cell. By using the landmark in thecore Voronoi cell, the corresponding candidate Voronoi cell in thetarget dataset is selected. The two Voronoi cells share the samelandmark. Once the candidate cell is defined, the candidateobjects that are completely or partially within it are chosen. Theselection of candidate objects renders other objects in the target
L1
L2
L3 L4
L5
L6
L7
C1
C2
C3
C7
C6
C5
C4
Fig. 5. Identification of candidate objects and derivation of triangulation with critical landmarks: (a) critical neighbor landmarks (left), (b) candidate objects (middle), and
(c) triangulation (right).
J.O. Kim et al. / Computers & Geosciences 36 (2010) 1115–1122 1119
dataset immune to comparison. This helps to reduce the searchspace in the target dataset.
The processes to define candidate objects in the target datasetfor matching are as follows.
01:
Define VDðRef Þ ¼ VCr1 [ VCr2 [ VCr3 [ . . . [ VCr
n,VDðTarÞ ¼ VCt
1 [ VCt2 [ VCt
3 [ . . . [ VCtn;
And {landmark1, landmark2 y landmarkn}AVD (Ref),{landmark1, landmark2 y landmarkn}AVD (Tar).
02:
Overlay (centroid of input polygon object, VD (Ref))-core VCri ,where landmarkcAcore VCri .
03:
Find landmarkc¼ landmarkt,where landmarktAcandidate VCtj .
04: Overlay (target dataset, candidate VCtj )-candidate objects formatching.
In this procedure, VD (Ref) is the reference Voronoi diagram,VD (Tar) is the target Voronoi diagram, VCr represents referenceVoronoi cells, VCt represents target Voronoi cells, landmarkc islandmark in core Voronoi cell (core VCr), landmarkt is landmark incandidate Voronoi cell (candidate VCt).
Once the Voronoi diagrams in both the datasets are delineated,the next step is to derive the triangulations. As shown in Fig. 5(c),an object in a Voronoi cell and a total of n surrounding landmarksmake a total of n triangles in a triangulation. In the referenceVoronoi diagram, a reference triangulation is made around theinput polygon object, whereas a number of candidate triangula-tions are made in the target Voronoi diagram, as there are anumber of candidate objects. Using the area and perimeter of thetriangles in both the reference and candidate triangulations, theratios of the areas and perimeters are then calculated andcompared.
2.4. Calculating geographic context similarity
In this section, a method to analyze the geographic contextsimilarity using the areas and perimeters of the reference andcandidate triangulations is introduced. Fig. 6 shows a referencetriangulation RT¼{rt1, rt2 y rtn}, where rtn is a single trianglecomposing RT and n is the number of triangles. An rtn in thereference triangulation has the identification number, area, andperimeter as attributes: rtn¼{Ref_ID, Ref_arean, Ref_perimetern}.Likewise, there is a candidate triangulation CTi ¼ fcti
1,cti2 . . . cti
ng,where i indicates ith candidate triangulation, ctn is a singletriangle composing CTi and n is the number of triangles of the CTi.
The ith ctn in the candidate triangulation also has attributes ofcti
n ¼ fCan_ID, Can_areain, Can_perimeteri
ng.If two differently sized triangles have the same shape, the ratio
of their areas is equal to the square of the ratio of their perimeters.Thus, for triangles x and y, the area ratio has the followingrelationship with the perimeter ratio:
ðareax=areayÞ ¼ ðperimeterx=perimeteryÞ2: ð1Þ
If the two triangles have the same shape then the difference E
between the area ratio and squared perimeter ratio equals zero.
E¼ 9ratio of area-ðratio of perimeterÞ29: ð2Þ
As the dissimilarity between the two triangles increases, E alsoincreases. Using such a relationship, we deduced the geographiccontext similarity of the two triangulations by calculating thetotal E using all the rt in the reference triangulation and the entirect in the candidate triangulation
Total E¼Xn
i ¼ 1
Ei, ð3Þ
where n indicates the total number of triangles in a triangulation.The total E is calculated for the reference triangulation and all ofthe candidate triangulations. The minimum total E indicates thecandidate triangulation most similar to the reference triangula-tion, which in turn indicates the matched objects.
3. Experiment and results analysis
3.1. Datasets used
The datasets used in this paper are the Digital TopographicMap (Topo Map) Version 2.0 and KLIS-rn (Korea Land InformationSystem-road name) in Korea. The Topo Map is a nationaltopographic base map that has a multilayered data structurewith point, line, polygon object, and corresponding attributetables. The KLIS-rn is produced to accommodate the new addresssystem based on street name and building number to assign anaddress to each building object. (The traditional address systemwas based on the parcel numbers of a cadastral survey.) Some ofdetails of the datasets used are explained in Table 1.
Reference Triangulation (RT) Candidate Triangulations (CTs)
ct1ct2
ct3
ct4 ct5ct6
ct7
CT1 CT2 CT3 CT4
CT5 CT6 CTi
rt1rt2
rt3
rt4
rt5rt6
rt7
····
ct1ct2
ct3
ct4 ct5ct6
ct7ct1
ct2
ct3ct4 ct5
ct6
ct7
ct1ct2
ct3
ct4ct5
ct6
ct7
ct1ct2
ct3
ct4 ct5ct6
ct7
ct1
ct2
ct3ct4 ct5
ct6
ct7
ct1ct2
ct3
ct4 ct5ct6
ct7
Ref_ID Ref_area Ref_perimeter
1 5057.82 337.61
2 4915.07 334.12
3 5064.78 327.02
4 2293.87 233.98
5 1485.78 185.66
6 857.86 166.90
7 4059.77 352.41
Fig. 6. Examples of calculation of geographic context similarity. Reference triangulation (upper left), its attribute table (lower left), and candidate triangulations (right).
Table 1Specifications of test datasets used.
Reference dataset Target dataset
Name KLIS-rn Topo Map (V2.0)
Provider Ministry of Public Administration and Security Korea National Geographic Information Institute (NGI)
Format Shape NGI
Scale 1:5000 1:1000 and 1:5000
Layer code of interest Building B001 (building)
Attributes building number, road number, name, floor, zip code, etc. name, category, type, usage, annotation, unique feature identifier (UFID)
J.O. Kim et al. / Computers & Geosciences 36 (2010) 1115–11221120
3.2. Analysis
The KLIS-rn is based on the Bessel system, whereas the Topo Mapis based on the Geodetic Reference System 80 (GRS80). Conse-quently, there are coordinate differences for the same object with amaximum value of up to 300 m (Cho et al., 2008). Therefore, in thispaper the minimum buffer radius for the landmarks search was setto 300 m. In the case of insufficient landmarks being available forextraction from both the datasets, the buffer radius was increased.Such an approach is usually called buffer growing. A sufficientnumber of landmarks allow the designation of a set of contiguouspolygons, with none missing from around the input polygon object.In these experiments, the buffer radius was increased up to 100 m.
Once the number of landmarks is extracted, Voronoi diagramsare created for both the datasets. Thereafter, the core Voronoi cellin the reference diagram and the candidate cell in the targetdiagram are selected. From the candidate Voronoi cell, a numberof candidate objects are detected. For the input polygon object, areference triangulation is then produced in the reference dataset.In the target dataset, a number of candidate triangulations areproduced for each of the candidate objects. From these triangula-tions, a series of computations for geographic context similaritiesare processed. Thus, for the input polygon object, each of thecandidate objects is compared, and the corresponding geographiccontext similarity value is computed. A total of 346 input polygonobjects were selected and tested. Fig. 7 shows several inputpolygon objects (from A to E in successive columns) andcorresponding candidate objects (from 1 to 7 in successiverows) along with their geographic context similarity values.
As shown in Fig. 7, the minimum value along each rowindicates the matched object. For example, in the case of inputpolygon A, the minimum value along the first row is 0.095 andobject number 5 is the one matched. Test results for the 346 inputpolygons show that the matching rate is a satisfactory 99.4%. Onlytwo out of 346 cases are mismatched (Table 2a). Of the 346 cases,30 have a one-to-many relationship. This implies that an object inthe target dataset is broken into many objects in the referencedataset. In addition, among the 344 matched cases, 15 havedifferent object shapes and 13 cases have very similar objectshapes (Table 2b). This implies that the method works wellregardless of object shape similarity. In addition, to see whetherthe proposed method works for different scales of datasets, a1:1000 scale target dataset was used and tested. The twomismatched objects previously mentioned are from this 1:1000scale target dataset (Table 2c).
For the two mismatched cases, we analyzed the valueof geographic context similarity. As shown in Fig. 8, in boththe cases, the minimum geographic context similarity valuehas a relatively small difference from the next minimum value.To see the impact of an increasing number of landmarks, weadded several such objects in both the cases and the resultsshow that their differences were reduced and the rankwas reversed. Correct matching is thus indicated. From this test,it is concluded that if the difference between a minimumand the next minimum value is within a threshold, it isdesirable to increase the number of landmarks. This impliesthat the identification of an appropriate threshold value isnecessary.
Candidate objects
Inpu
t ob
ject
s
0.779 0.709 2.021 0.791 0.095 0.712 4.768
0.814 0.200 2.394 1.229 1.125 1.366 5.088
C 0.920 0.867 2.730 0.061 0.739 1.692 2.946
0.703 0.819 1.412 1.239 0.551 0.140 5.481
1.480 1.426 0.119 3.123 2.402 2.575 10.340
1 2 3 4 5 6 7
A
B
D
E
Best fit (min. value) Correct match
Fig. 7. Geographic context similarity between two datasets.
Table 2Results of matching test datasets.
(a) Matching cardinality
Input polygon object Target object Number of test objects Number of correct matches
1 1 316 314
N 1 30 30
Total number of test objects 346 344
(b) Shape
Reference data Target data Number of tests Number of correct matches
Different shapes for the same object 15 15
Same shape for the different objects 13 13
(c) Scale
Reference dataset Target dataset Number of test objects Number of correct matches
1:5000 1:5000 240 240
1:5000 1:1000 106 104
Candidate objects
I
Inputobjects
A
critical landmarks used 0.362 0.490
added landmark 0.281
B
critical landmarks used
0.108 0.091
added landmark 0.174
Best fit (min. value) Correct match
0.498
0.485
J
Fig. 8. Mismatched case and calculation of geographic context similarity by
adding a landmark.
J.O. Kim et al. / Computers & Geosciences 36 (2010) 1115–1122 1121
4. Conclusion
In this paper, we proposed a new object matching methodbased on geographic context similarity measures. For this, a set oftraditional concepts such as buffer growing, Voronoi diagrams,triangulation, and geometry measures have been adopted andcombined. The proposed method was tested for the DigitalTopographic Map and KLIS-rn datasets in Korea, in which theformer was the target dataset and the latter was the referencedataset. Once a set of common landmarks was extracted fromthese two datasets, Voronoi diagrams from the reference andtarget dataset were generated. From the landmark in the coreVoronoi cell, the candidate Voronoi cell was identified, and thus aset of candidate objects in the target dataset were defined. Thisprocess was followed by delineation of reference and candidatetriangulations for computation to examine the geographic contextsimilarities between the triangulations.
J.O. Kim et al. / Computers & Geosciences 36 (2010) 1115–11221122
From the test results, it was found that two out of 346 selectedobjects were mismatched. The result shows a success rate of up to99.4%. The algorithm worked well regardless of the object shapes.Furthermore, the algorithm was also effective for different scalesof datasets. The proposed method is very simple in concept butsufficiently powerful to match objects from different datasets thathave significant coordinate discrepancies. Through the adoptionof the Voronoi diagram, in particular, the method requires a verylimited number of landmarks and a small object search region.
Although the method is believed to be very useful, there areissues for further investigation concerning its practical applications.One matter is the selection of landmarks. In this paper, a set ofcommon landmarks was selected manually from within twodatasets, but this is not acceptable in practical applications. A newapproach is required to automate the landmark selection process. Afurther issue is the positioning method of the proposed approachwhen compared with that derived by the proximity graph method ofSamal et al. (2004). We must be aware of those cases in which theproposed method is particularly useful in contrast to the proximitygraph method. Having such knowledge will guide users to applyeither method appropriately, so that they are used in a comple-mentary manner. These two research topics are being addressedjointly and the results will be presented in the near future.
Acknowledgement
This research was supported by a grant (07KLSGC04) fromCutting-edge Urban Development of the Korean Land SpatializationResearch Project funded by the Ministry of Construction & Transpor-tation of the Korean government. In addition, the authors appreciatethe support of the Integrated Research Institute of Construction andEnvironmental Engineering at Seoul National University, Korea.
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