Contextual Generalisation Implementations in Topographic Mapping
Transkript
Contextual Generalisation Implementations in Topographic Mapping
Contextual Generalisation Implementations in Topographic Mapping: KartoGEN Project Dursun Er ILGIN, Bulent CETINKAYA, Serdar ASLAN, Y.Selim SENGUN, O.Nuri COBANKAYA Harita Genel Komutanligi, 06100 Ankara, Turkey Email: dilgin@hgk.msb.gov.tr Abstract Many generalisation approaches, methods, operators, and algorithms have been developed aiming to realize the automation of generalisation processes, and still much more is needed. The integration and implementation of those in a map production line stands as a critical issue with contextual generalization which makes automation even much more complex and a difficult task. In this paper; first, the role and the importance of the contextual issues that have to be taken into account in generalizing the topographic maps are identified and analysed. Next, some practical solutions are investigated and suggested to realize contextual generalisation and to obtain acceptable results. Then, some of the implementation of contextual generalisation has been realized in KartoGEN Project which aims to produce 100K scale topographic maps from 25K scale content data (TOPO25). Mentally related data layers have been evaluated through new approaches of real world object (RWO) classes and problems have been tried to be solved with contextual approaches, such as by considering the generalisation of contour lines closely together with hydrographic networks and by transferring building typed utility and facility features into settlement RWO classes rather than working with flattened thematic layering of FACC (Feature Attribute Coding Catalogue). Through these kinds of pragmatic solutions, automation ratios in generalisation have been increased and post generalisation editing needs have been decreased. Finally, the results have been evaluated, which are quite promising. KEYWORDS: Contextual Generalisation, Automation, Real World Objects, Topographic Maps. 1. INTRODUCTION: The desire to produce small scale maps and databases from master database, makes generalisation a significant issue to National Mapping Agencies (NMA). Generalization constitutes an essential and critical part in such a map production system. Generalization can be defined as a process of deriving smaller scale datasets with the desired specifications from larger scale spatial data sources or from datasets having much more detailed information (Aslan, et.al., 2004). Conventional generalisation production lines, once only done by experienced cartographers manually, are wanted to be replaced with semi- and fully automated flow lines in digital environments. Generalization is aimed to be used in digital map production systems with high standardization and automation (Itzhak, et.al., 2001). The cartographer’s knowledge and experience in generalizing a map is difficult to define in a computer, even after researchers throughout the world have laboured on this subject for last 30 years, although some interesting topics have recently been presented (Kilpelainen, 1999). Plenty of generalisation approaches, methods, operators, and algorithms have been developed to realize the automation of generalisation processes, and still much more is needed. The integration and implementation of those in a map production line stands as a critical issue with contextual generalization which makes the automation even much more complex and difficult task. 2. THEORY: Generalization is a vital issue for Cartography. The automation of generalization processes in the map production systems is extremely important for data providers due to enabling to speed up the production and to standardize the derived products. Contextual generalisation has to be taken into account continuously, almost in every steps of topographic map production line. Preserving the contexts between the map objects after generalisation is very important and contributes to the quality and coherence of the output data. Most of the generalisation operators, algorithms and methods developed are suitable for generalizing objects independently without considering contextual issues. Taking into account the contextual issues in generalisation processes requires explicit definitions of objects’ contextual relations which are desired to be preserved in the generalized data. Elaborate studies and abundant efforts are needed for implementing the generalisation operators, algorithms and methods harmonically in the generalisation processes of Topographic Mapping to produce small scale maps from master data set, and for taking care the contextual issues. For the automation, some problems listed below needs to be overcame and solved; • lack of written generalization rules, • difficulties in defining generalization rules explicitly so that not requiring any further interpretation, • subjective and complex structure of generalization, • automation and standardization level, • cartographic satisfaction level, • preserving the contexts between objects after generalization, etc. Data quality, data accuracy, data spatial resolution and data models are closely related with generalization and play an important role in defining and developing generalization algorithms and methods in applications. 2 3. CASE STUDY: Data collection is one of the most time-consuming and expensive, yet important of GIS tasks (Longley, et.al., 2001). Many data providers and map producers want to produce derived datasets from their detailed master datasets thorough generalization. Since digital datasets are in hands now, manual generalization methods don’t seem to be feasible anymore and have to be replaced with modern and automated ones in digital environment. Lack of sufficient generalization tools and user interfaces in software environment appropriate to specific generalization needs and existing master datasets make this a severe task for the map producers. Automation in generalization plays a crucial role in the map production system since it helps in speeding up and standardizing the generalization processes and the output products. Selection of generalization methods, algorithms, operators, parameters and workflow are highly depended on the source and target datasets and their specifications. Therefore the quality, accuracy and contents of the source dataset used as an input in generalization become a crucial key role in generalization. Most often, a data re-engineering should be needed before generalization processes to standardize the input data, remove data errors, and enhance the data contents. Another factor that has to be taken into account is that the input data should not be in sheet base files. Seamless input datasets stored in a database are preferable due to get rid of merging and union files and features that is needed before generalization processes. 3.1. KartoGEN Project: A generalization project, named KartoGEN, has been established in General Command of Mapping (Harita Genel Komutanligi-HGK) (GCM), the NMA of Turkey, in order to produce 1:50 000 and 1:100 000 scale Standard Topographic Maps (STM) using master geographic dataset TOPO25. For the time being, ArcGIS software is being used to produce derived datasets thorough generalization. 3.2. Input Data: In the KartoGEN project, TOPO25 is used as an input data which is a master geographic dataset used in the production of 1:25 000 scale STMs. TOPO25 data model has a close similarities with US Vector Map (VMAP) level 2 and FACC rulings. TOPO25 data model consists of 9 thematic layers and one annotation coverage, listed as below; Boundary (BND), Elevation (ELE), Hydrography (HYD), Industry (IND), Physiography (PHY), Population (POP), Transportation (TRA), Utility (UTI), Vegetation (VEG), 3 Annotation (TXT), And each of the thematic layers consists of 3 different geometry type coverages such as polygon, line, point. 3.3. Methodology: In order to implement generalization processes in the map production and to preserve contextual issues of map objects efficiently, the input data have been analyzed thoroughly by giving importance to mental and contextual relationships among objects. Being aware of data model not being perfect for generalisaiton, it is aimed to launch a map production line with the existing data to produce 1:100 000 scale STMs in a short time period. Practical solutions are found and implemented in the project, and some of them will be presented in this paper. Actually, the data modelling mentioned above was built on the approaches at 1980’s, and the real world was modelled as flattened. But for the generalization issues, the real world needs to be modelled with real world objects and object classes. In TOPO25 data model, building type objects comes from various different thematic layers and coverages, such as POP, IND, and UTI. In this study, the input real world objects have been put in one mental object class in order to handle it and realize generalization with its high contextual relationships. In the TOPO25 data model, the thematic layer entities with different geometry types are also separated into different binary data files (in ArcInfo coverages). For example, polygon geometry type objects such as forest, vineyards, culture vegetations, etc. and point geometry type objects such as trees are stored in different data layers. Thus, in generalisation processes, topology and consistency of those objects should be taken into account and the existing contexts should be preserved. 3.4. Implementing Contextual Generalisation in Topographic Mapping: Some of the practical solutions found and implemented in the KartoGEN project will be described below; In generalisation processes, population (POP) and transportation (TRA) layers are considered and handled together due to having close contextual relations among their objects. As mentioned above, building type objects has been separated among different thematic layers in the current data model. So, at first, those building type objects have been selected and then transferred into a settlement object class due to having strong context between them. As a result, the newly created settlement object class contains; Settlements, buildings, cemeteries, public buildings, etc. from POP layer, energy building, facilities, etc. from UTI layer, industrial buildings, facilities, etc. from IND layer. As investigating deeply the contexts between settlement object class objects and road objects, many cases of context can be listed. Building objects within certain distance from road objects should be parallel to the nearest road objects. This context should be preserved. In KartoGEN project, this is automatically done after generalisation processes of roads and settlements as shown in Figure 1. Figure 1a and 1b show pre- and post- generalisation situations, respectively. 4 The coordinates and directions of the map features can be changed after the processes of some generalization operators such as simplification, smoothing, and refinement. For esthetic purposes some feature rotation operations are needed. Figure 1c shows automatic rotation of buildings so as to become parallel to nearby roads. This is just needed for cartographic purposes and cartographic satisfaction. (a) (b) (c) Figure 1: Rotating automatically the buildings to preserve buildings being parallel to road linear objects after generalisation. Some contextual relations that exist between the objects can be damaged after simplifying and smoothing processes of linear objects. For example, a building at the right of the road can be on the other side of the road after the road simplification process. This has to be taken into account in generalisation processes. In KartoGEN project, this contextual generalisation part is implemented automatically as depicted in Figure 2. Figure 2a shows pre-generalisation data. Figure 2b shows the data after the simplification and smoothing processes of road type objects. Due to area being smaller than certain criteria, the polygon geometry type cemetery object collapsed into point geometry type cemetery object as shown in Figure 2c. For the contextual issues, point geometry type cemetery is shifted at certain distance towards the right of the road so as to preserve being staying at the right side of the road as it is used to be in pre-generalisation situation. This is shown in Figure 2d. 5 (a) (b) (c) (b) Figure 2: Preserving the context between cemetery and road objects after various generalisation processes. Special attentions have to be taken in various cases in order to realize contextual generalisation. Generalisation of polygon shaped cemetery objects through which roads passes should be handled differently as shown in Figure 3. Polygon shaped cemetery objects having area below certain criteria are not wanted to be shown as a polygon shaped object in the output data. In collapse operation, they should be collapsed to point shaped objects. In the example shown in Figure 3, the polygon shaped cemetery objectis divided in two parts using the road objects, and then the collapse operation is applied. As a result, the polygon part on the left side of the road collapsed into point due to its area being smaller than the specified criteria while the other polygon on the right side of the road preserves its geometry type. (a) (b) Figure 3: Preserving the context between cemetery and road objects after collapse operation. 6 Due to having strong contextual relations and for aiming to have better and cartographically acceptable results, generalisation of hydrographical networks and elevation data objects (contours from ELE and geographic control points from UTI) have been carried out closely together. As a result, the characteristic lines of the terrain, such as V shaped parts of contours on rivers have been preserved automatically. In the generalized data, the logical and topological consistency between rivers and contours has been preserved. This implementation has decreased post-generalisation conflicts and thus post-editing needs. Figure 4a and 4b show the pre- and post generalisation situations. (a) (b) Figure 4: Preserving the context between river and contour objects after generalisation. 4. CONCLUSION: Automation of generalisation needs explicitly defined generalization rules and their applicable definitions, generalisation processes and their defined orders. Theoretical ideas should be transferred to actual production environments to realize automation. In this case, geographic data model comes across as a crucial point that has to be taken into account. It directly affects the generalization methods applied and the way of transforming the generalization rules into codes in actual digital production systems. The better modelling the world as RWOs, the easier becoming the transfer of the generalisation rules from cartographers mind to algorithms. Object oriented data model seems to be better in modelling the RWOs. The content and quality of source master data is also extremely important and it directly affects the automation, generalization methods applied, processes and the production line. Reengineering the input data before generalization could affect the automation, generalization and the quality of the output product. There is still need for lots of more sophisticated and applicable algorithms for generalization and its automation. In this paper, some practical solutions for realizing contextual generalisation are suggested that can be applied in a map production. Contextual generalisation is a key element in generalisation and has to be taken into account in each steps of topographic mapping through generalisation. Contextual generalisation contributes directly the quality and coherence of the output generalized data. 7 5. REFERENCES: Aslan S., Cetinkaya B., Ilgin D.E., Yildirim A., 2004. “Some intermediate results of KartoGEN generalization project in HGK”, ICA Commision on Generalisation and Multiple Representation – Research Workshop, Leicester, UK. Aslan S., 2003. “Generalisation of Buildings and Settlement area in Topographic Maps”, M.Sc. 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