Scientific Research and Essays Vol. 5 (19), pp. 2889-2899, 4 October, 2010 Available online at http://www.academicjournals.org/SRE ISSN 1992-2248 © 2010 Academic Journals Full Length Research Paper

GIS and remote-sensing-based urban-information system design and development: A case study for

Kozlu, Zonguldak

Mehmet Alkan1* and Günnur Bulut2

1ZKÜ Engineering Faculty, Geodesy and Photogrammetry Department, 67100, Zonguldak, Turkey.

2Zonguldak City Government Agency, Zonguldak, Turkey.

Accepted 23 August, 2010

Urban growth is inevitable over the next two decades. The bulk of this growth will take place in less developed countries. This growth presents a formidable challenge for urban planners and managers. In this context, we consider some of the ways urban planners can make use of the recent developments in Geographic Information Systems (GIS) and Remote-Sensing (RS) technology to respond to this challenge. GIS projects were initiated in late 1980s in Turkey by both the public and private sectors. Currently, GIS and RS technology play very important roles for urban managers. At the present time, RS technologies are being continually improved; new satellites have been launched with imaging abilities enhanced by new techniques, allowing images to be obtained faster and at higher quality. Current applications use these imagery data for many purposes, among them automatic feature extraction from high-resolution satellite imagery. In this context, automatic and manual object extraction and GIS are the most important issues. In this study, the primary issue was the design and development of Urban Information Systems (UISs); another, more specific, goal was to evaluate the advantages and disadvantages of OrbView-3 and IKONOS imagery for their use in UIS. A test case, consisting of a UIS design, development and application, was executed for the Kozlu, Turkey area. Automatic and manual extraction methods were employed using pan-sharpened IKONOS and OrbView-3 images of the Kozlu urban test area. The NetCAD v5.0, eCognition v4.0.6 and MapInfo v9.0 software packages were used for automatic and manual extraction analyses. Digital reference-vector data were obtained from large-scale digital maps. The extracted results were then compared with a database of reference digital building and road information created from large-scale digital maps in a GIS software package. Thus, utilizing GIS-based analyses, the availability of UIS updating was tested with high-resolution imagery. Key words: Urban information systems, Geographic Information Systems (GIS), Remote-Sensing (RS), high-resolution imagery.

INTRODUCTION Municipalities need access to data, such as parcels, owners, buildings, road and infrastructure information, to provide for public administration. However, most Turkish municipalities do not have access to digital data for Turkey (Yomralıo�lu, 2000). For this reason, some of these municipalities have studied the use of GIS. *Corresponding author. E-mail: mehmetalkan44@yahoo.com. Tel: +90 372 2574010 - 1413. Fax: +90 372 257 2996.

Although GIS were developed worldwide in the 1950s and 1960s, the use of these systems in Turkey was initiated only in the late 1980s. Since then, the number of GIS projects, especially those carried out by different governmental authorities, has been gradually increasing. While some are national or regional, most are local projects (Çete et al., 2009).

Urban information systems (UISs) are one component of GIS; UIS play key roles for many municipalities (Durduran and Erdi, 2006; Yomralıo�lu, 2002). In Turkey, local GIS projects are generally carried out by governor-

2890 Sci. Res. Essays ships and municipalities. The Governorships of Sakarya, Kocaeli, Bursa, Ankara, Istanbul, Amasya and Trabzon established UIS at the province level (Yomralıo�lu, 2002). UIS can serve the needs of urban planning, building permitting, tax collection, transportation, infrastructure and mapping for governmental bodies. Thus, UIS are a very effective tool for manipulating, storing, collecting, tracking and displaying spatial and attributes data (Ishida, 2000; Çete, 2002; Yomralıo�lu, 2002; Yomralıo�lu and Çete, 2002).

Besides governorships, the other local authorities which most commonly use GIS in Turkey today are municipalities. They are the leading institutions charged with providing services to urban populations. Their responsibilities are growing as most of the population lives in urban areas and the rate of migration from urban to rural areas is continually increasing. According to the statistics, the volume of data for urban areas doubles every year (Takase et al., 2004; Yomralıo�lu and Çete, 2002). Thus, the use of computerized systems is inevitable for the municipalities to realize these complex tasks and to manage the huge amount of data effectively. Hence, many Turkish municipalities are trying to establish and use UIS as an efficient tool for collecting, storing, manipulating and displaying spatial data (Çete et al., 2009).

Current developments in remote-sensing and image-processing technologies have specifically provided the opportunity for the observation of large areas in detail and, consequently, the rapid production of reliable and extended recent data. Thus, the fast development in urban areas can be followed and strategies for directing this development can be formed. In this respect, automatic object-extraction approaches have recently become necessary for large-scale topographic mapping from these images, determining the changes of topo-graphy and revising the existing map data. For mapping from high-resolution imagery or GIS database con-struction and its updating, automatic object-based image analysis has been generally used for remote-sensing applications in recent years. Additionally, as the products obtained from automatic object-based extractions are GIS-based, they can be integrated into GIS, queried and various strategic analyses can be made (Büyüksalih et al., 2006; Jacobsen, 2003; Marangoz et al., 2007).

The information content and its geometric accuracy are important for the generation of quality maps with the required features and geometry. Even if the maps today are usually only graphical representations of geoinfor-mation systems, GIS are related to the accuracy speci-fication, the contents and the degree of generalization for a representation scale. Maps generally have a horizontal standard deviation of approximately 0.25 mm in the representation scale. For information content, the heuristic of a ground-sample distance (GSD) of 0.05 up to 0.1 mm in the representation scale is commonly accepted (Doyle, 1984). This means that with the 1-m

GSD of IKONOS images a map with a scale of 1:10,000 can be generated (1 m / 0.1 mm = 10,000). OrbView-3 has a pixel size of 2 m; neighboring pixels are over-sampled by 50%, so the distance from one projected pixel center to the next is also equivalent to a 1-m GSD. Of course, with such a technique, the image quality cannot match the case where the pixel size is identical to the GSD. For this reason, more details can be identified in an IKONOS images than in OrbView-3 images. Therefore, the better resolutions of Quickbird, Worldview-1 and Geoeye-1 lead to more visible details (Jacobsen et al., 2008; Topan et al., 2004). In the Zonguldak test area, the results based on IKONOS and OrbView-3 were similar; only very few details could not be mapped with OrbView-3 (Topan et al., 2006).

In this study, automatic object-based classification of buildings and roads in the Zonguldak study area of Turkey was realized using the eCognition v4.0.6 soft-ware. The classification procedure was implemented using a pan-sharpened QuickBird image of the area of interest. Such an image can also easily be formed by the pan-sharpening module of the PCI Geomatica 9.1.1 system. Several tests were carried out to verify successful segmentation then classification was enabled by entering different parameters into the software used.

The aims of this study were to design and develop a UIS and to test the capacity for feature extraction from high-resolution GIS images for use in the UIS. The results obtained were transformed into a vector format and integrated into a database. These vectors were produced automatically and were compared with the reference-vector maps of the study area at a scale of 1:5,000 and the results acquired from an on-screen manual digitizing method. The success of the object-oriented image analysis was tested by GIS software; the results are presented and discussed. The construction of GIS-based analysis and comparisons with raster and vector data of the test area was of crucial importance in terms of evaluating the system. METHODOLOGY Study area and datasets Kozlu, the selected study area, is a town in Zonguldak, a province in the northwest of the Black Sea region of Turkey (Figure 1.) Kozlu was founded in 1941 and is 4 km from the city of Zonguldak, the capital of the province. The total area of the town of Kozlu is 570,870 m2, with a population of 35,132 according to the 2007 census (URL-1, 2007). The Kozlu government consists of seven districts and 21 villages (Figure 2); Kozlu has 11,457 buildings.

The Kozlu area has a rolling topography, with steep and rugged terrain in some parts. The partly built city area is located along the sea coast and there are some agricultural lands and forest in the inner part of the region.

In the upper part of the map shown in Figure 3, the Black Sea is at the bottom and above is the central part of Zonguldak city, which covers an area of nearly 15 × 15 km, with elevations ranging up to 450 m. When the images were first received, they were analyzed to

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Figure 1. Location of Zonguldak province.

Figure 2. Districts of Kozlu (Bulut, 2008).

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Figure 3. Subset of pan-sharpened IKONOS image of the Kozlu-Zonguldak study area.

select suitable GCPs with a uniform distribution. As a result of this determination, 43 distinct GCPs were measured by GPS survey with an accuracy of about 3 cm. As these points could be seen very well in the images, they were selected as building corners, crossings, etc. Because of the fine resolution of the QuickBird imagery, many cultural features can be identified and used as GCPs. The manual measurements of GCP image coordinates were carried out using the GCP Collection Tool in the PCI Geomatica-OrthoEngine software package. After geometric correction of the QuickBird imagery (Jacobsen et al., 2005), it was enhanced by applying a pan-sharpening method (Zhang and Wang, 2004) included in the PCI system. This method makes it possible to simultaneously benefit from the sensors’ spectral capabilities and their high spatial resolution. Figure 3 shows a subset of the pan-sharpened IKONOS image of the Zonguldak study area taken in June 2008.

In this study, a subset of the pan-sharpened image was used, which includes the buildings and road features we were planning to automatically extract. The characteristics of this area, shown in Figure 3, are to have a variable topography and to be more urbanized area in Zonguldak. Observing the details of the image, many buildings with different shapes and a disordered road network are visible. Additionally, some of building roofs are different from each other, and some of the road network is shadowed by building features and vegetation. Design and development of UIS Here, the first requirement analysis of the UIS is given. The selection of a data model and the design of a database for the UIS are then explained.

Requirement analysis Requirement analysis is the first phase of the design and development of a database, consisting of selecting and defining the data as well as query content and formats, which determine the database structure (Cömert and Bostancı, 1999). Here, they were selected for the design and development of a UIS for Kozlu Municipality; this resulted in a long list of data and queries, including the following: 1. Parcels, parcel information, buildings, building information, and address information 2. Government buildings and information, 3. Condominium registry information, 4. Buildings numbers of each parcel, 5. Building license information, 6. Real estate taxes and water-invoice information, 7. Parcels without buildings, 8. Number of buildings on each parcel , 9. Areas of parcels and buildings, 10. Division of parcels and owners of the lots, 11. Real estate belonging to each owner, 12. Condominiums, 13. Buildings with building licenses, 14. Government-agency areas, 15. Parcels unsuitable for a zoning plan, 16. Tax-payment information. Database design In the Municipality system of Turkey, spatial objects consist of

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PARCEL

Object id City Town District Map Number

Section Number

Parcel Number

….. Area tmin

PARCEL – OWNERSHIP HISTORY Object id Owner id Lot Obtained by Restraint ….. tmin tmax

CONDOMINIUM OWNERSHIP HISTORY Object id Owner id Lot Obtained by ..… tmin tmax

OWNERS Owner id Name Last name Father’s name Birth date Birth place Sex ….. tmin

CONDOMINIUM – TAX Object id Owner id Tax_id Tax Debt Debt date Paid of tax debt Paid date

Figure 4. A portion of the database schema.

parcels and buildings constructed on the parcels. These spatial objects are represented with the “polygon” data type in the database. Additionally, roads are also spatial objects and are represented with the “line” data type in the database. Other information includes attribute data such as parcels, buildings, roads, addresses, and tax information.

In the database design, parcels and condominium data were maintained in different tables. Figure 4 shows a portion of the database schema. In these tables, relatively constant data, such as parcel area, owner’s name and surname and condominium number, are stored. Thus, the only relevant information is changed when, e.g., a parcel is divided or a condominium is sold, rather than general information; in this way, repetition of data is avoided.

Concerning the “keys” to the tables, composite keys were used. The components of the keys are “Object_id” for parcels and condominiums, Owner_id and stored date (tmin) (Oosterom and Lemmen, 2001). Object_id is a compound value consisting of the codes of the province (two characters), district (two characters), neighborhood (village) (three characters), block (five characters), parcel (five characters) and condominium (three characters). Owner_id is the unique identity-card number of the citizens of the Turkish Republic.

Recently, quite a lot of attention has been paid to methods for the storage and manipulation of temporal data. However, all of the available storage algorithms are very complex, so a simple solution was chosen via the use of tmin and tmax. When a new object or attribute is inserted in the database, it is assigned the current time as the value for tmin and tmax is a special value (for instance, if the parcel owner changed on 01/01/2009) or a future value. Additionally, tax debt is the total debt for any year of each property. The debt date is the time of the tax assessment, each payment of tax debt is associated with the property and the paid date is the date of final payment of the tax debt. Updating the database using high-resolution imagery Mapping today is by GIS data acquisition, and maps are just one form of geoinformation output. Mapping by ground survey is considered too time consuming and sometimes access to some areas is dangerous or impossible. In addition, several countries place restrictions on the use of aerial images, even if this may no longer be justified. With the very high-resolution satellite images now available, of up to a 50 cm GSD, a topographic mapping up to

the scale of 1:5,000 is possible. GIS is of course based directly on ground coordinates, so no scale of mapping is fixed, but the accuracy and especially the information details determine the representation scale for GIS. GIS database updating is also an important issue for the application of GIS projects. For this reason, very high-resolution satellite images make it very important to update the GIS database. One of the components of this work is to investigate whether or not spatial images are useful for the UIS database.

Zonguldak city was utilized for testing imagery-application areas including image resolution. Currently, GIS applications require very high-resolution imagery for accuracy. Therefore, IKONOS and OrbView-3 very-high-resolution satellite images were selected for updating the GIS database in this study. With higher resolution, more details can be identified in the satellite images. In the Zonguldak test area, the OrbView-3 and IKONOS images were compared for their resolution and in database updating. RESULTS and DISCUSSION Example queries for the UIS Pursuant to the requirement analysis performed in this work, a database design was carried out using the Entity-Relationship (ER) model. The conceptual schema of the ER model was then mapped onto a relational schema for implementation. Temporal data are traced by “tmin” and “tmax” attributes represented by attribute data stored in the MapInfo 9.0 v. software. The relationship between attribute and spatial data is established over “Object_id” (in all tables). Attribute queries are done via SQL. When spatial data are involved in the query, then MapBasic, the API of MapInfo, was employed. Some of the important queries for UIS will be shown in this part. These queries help the municipality in tracking, controlling and accessing any data; some of the queries are shown in Figures 5 and 9.

Figure 5 shows the real estate attributes belonging to an owner; this example is lists all of the real estate

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Figure 5. Real estate information belonging to an owner.

Figure 6. Water-tax debt information for 2007.

information of any owner. Municipalities need this infor-mation for all of the owners for tax collection.

Water-tax debt tracking is possible for the UIS. Figure 6 shows the water-tax debt of the condominium owners.

Attributes and geometric information about parcels without buildings can be seen in Figure 7.

Another query determined the school area and other information in one district, Fatih; the results are shown in Figure 8.

The final query was to determine the number of buildings in each district. Figure 9 shows the building numbers by district. Comparing application data for the GIS database GIS applications need to updates frequently. UIS graphi-cal data updates depend on the buildings and parcel shapes. It is possible to use high-resolution imagery to

update building data in the UIS. This procedure is faster and more economical than traditional surveying. Next, we compare building information extracted from images to reference topographic data.

The subset of pan-sharpened images which includes the buildings we planned to manually and automatically extract was used. The characteristics of this area, shown in Figure 3, are a variable topography and it is one of the more urbanized areas in Zonguldak. Looking at the details of the image, many buildings with different shapes can be observed, in no particular order. Additionally, the building roofs are different from each other and some of the road network is shadowed by building features and vegetation.

Automatic object-based feature-extraction results and manual on-screen digitizing results were compared with reference vectors from 1:5,000 scale topographic maps using GIS software. First, the vector results of the object-based feature extraction of buildings and road networks

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Figure 7. Attributes and geometric information for parcels without buildings.

from images were compared and superimposed with reference vectors (Figure 10). OrbView-3 imagery has only panchromatic bands. Automatic object extraction depends on the object base and did not yield good results for this imagery.

By counting the extracted buildings in the study area using GIS software, it was seen that 85% of the buildings were extracted automatically. In the segmentation phase of the object-based feature extraction, the most suitable segmentation results were selected and, therefore, this resulted in more successful building extractions. The extracted buildings were properly shaped and similar to their real forms. Looking at Figure 10 in detail, some new buildings have been constructed and some buildings have been demolished in the time since the reference map was constructed.

For the purposes of this study, an operator-digitized

building vector was manually extracted from the IKONOS and OrbView-3 imagery. The resulting building vectors acquired by the manual on-screen digitizing method were compared with the 1:5,000 scale topographic maps, as shown in Figures 11 and 12. For comparison, the IKONOS and OrbView-3 building vectors were also compared with each, as shown in Figure 13.

Automatic and manual digitizing methods each had some problems with all the features of the buildings, one being the spatial positions of the buildings compared with the reference base maps. OrbView-3 imagery was useful only for manual extraction because it has only panchro-matic bands; e-Cognition software is generally effective only for multispectral imagery.

Manual digitizing of the buildings from IKONOS and Orbview-3 images extracted the correct shapes using the advantage of a 1-m GSD. By counting the digitized

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Figure 8. Query result for school features of the Fatih district.

Figure 9. Building numbers by district.

buildings in the study area using GIS software, it was seen that, 90% of the buildings were manually digitized.

By counting the extracted buildings in the study area using GIS software, it was seen that 87% of buildings were extracted automatically. In the segmentation phase of the object-based feature extraction, the most suitable segmentation result was selected and, therefore, this resulted in more successful building extractions. The

extracted buildings were properly shaped and similar to their real forms.

By comparing the center lines of the road network to the reference vectors and object-based results, it was seen that 70% of the road network was extracted automatically. The reason for this low value was problems caused by the shadows from buildings in this image and the proximity of the buildings. The other

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Figure 10. GIS-based analysis of the results of the object-based extraction of buildings from IKONOS images using reference vectors.

Figure 11. Analysis of on-screen manual digitizing results for buildings from the IKONOS image using reference vectors (blue lines are the imagery, the green are the reference vectors).

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Figure 12. Analysis of on-screen manual digitizing results for buildings from the OrbView -3 image using reference vectors (red lines are the imagery, the yellow are the reference vectors).

Figure 13. Comparison of the IKONOS and OrbView-3 results (green is IKONOS, magenta is OrbView-3).

reason is the low extraction capability of eCognition software package for linear objects (Marangoz et al., 2007). Conclusions UIS are one of the most common GIS applications in Turkey, where the need for UIS projects has been increasing since the 1990s. Effective and useful UIS are a very important component of city administration. The main advantages of UIS are in data-management and decision-making responsibilities. UIS are going to become more commonly used in the future. Therefore, municipalities should make recommendations for UIS requirements.

In this study, we design and developed a UIS for the Kozlu municipality. First, we performed a requirement analysis for the system. Database and GIS design and development were then executed based on the requirement analysis. This UIS was designed to be capable of not only tracking data but also handling queries, as explained in the description of the requirement analysis. A number of the capabilities of the designed UIS were tested, with examples given in the discussion.

The feature-extraction approaches and their implemen-tation for updating the UIS using high-resolution images for the generation of topographic line mapping were evaluated. Thus, we were able to decide which data were applicable for which database updates. By counting the extracted buildings in the study area using GIS software, it was seen that 85% of the buildings were extracted automatically, whereas 90% of the buildings were extracted using manual digitizing. Based on these results, feature extraction should be generally useful in GIS applications. REFERENCES Bulut G (2008). Urban Information System Design and Development:

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