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The geographic data and knowledge collection and dissemination via authoritative professionals only – characterized as the top- down scheme – has been shifting in the past few years to the bottom-up scheme, in which citizens and laymen generate data they later use as information in various applications and services. This is a new era in the history of human mapping efforts, mainly in terms of data collection, but also for knowledge production. This neogeography revolution is fundamentally transforming how geographic data are acquired, maintained, analyzed, visualized, and consequently – used. With today’s technology, availability, access and ease of use there is a potential of a geographer within everybody. In view of these changes in the mapping and land surveying domain over the past few years, Commission 3 has undertaken the mission to prepare this publication within the framework of FIG, aiming to emphasize the accessibility, the potential, and the use of crowdsourcing and VGI as abasic tool for land surveyors and mappers professionals. During the 2011–2014 and 2015–2018 periods, FIG Commission 3 has addressed this phenomenon of shifting from the top-down mapping scheme to the bottom-up one. Its particular focus has been on SIM Infrastructure, Technical Aspect of SIM, and on Crowdsourcing and VGI. In this effort, FIG Commission 3 has established valuable collaborations in an effort to adopt a multi-sector approach, and bring together people with relevant expertise, such as academics, experts from the public sector as well as from the private sector, to share experience and knowledge on crowdsourcing and VGI. FIG Commission 3 cooperates closely with UN-agencies(UN ECE WPLA, and UN-HABITAT and GLTN), UNESCO, the World Bank, ISPRS and other sister associations. This publication integrates the output of research studies done by Commission 3 working groups and resolutions from the past annual workshops(Paris 2011, Athens 2012, Skopje 2013, Bologna 2014, Malta 2015, Iasi 2016, Lisbon 2017 and Napoli 2018).

The developed method allows the user to integrate polygonal or linear datasets. Most existing approaches do not work well in the case of partial equality of polygons or polylines. The suggested method consists of two phases: searching for counterpart boundaries or polylines by a triangulation, and rectifying objects without correspondent polylines by a transformation and a shortest path algorithm. Data covering the Haifa region of Israel have been used for evaluation of the approach. City Planning datasets have been rectified by precise cadastre data. Positional accuracy of the City Planning datasets has been increased significantly. Average distance between segments of the datasets has been decreased in almost five times. Standard deviation has been decreased by thirty-five percent. In addition, more complete road layer of OpenStreetMap covering the city has been rectified by a more precise statutory road layer. Positional accuracy of the rectified layer has been improved significantly. The rectified layer has been utilized to prepare a large-scale map depicting roads with individual widths and statutory buildings. OpenStreetMap rasterization rules have been applied for road widths calculation. The prepared map depicts real-size buildings and roads’ widths in scale., {"references":["A. Noskov and Y. Doytsher, \"A Linear Approach for Spatial Data Integration,\" GEOProcessing 2016, Venice, Italy, 2016, pp. 93-99","R. Abdalla, \"Geospatial Data Integration\", Introduction to Geospatial Information and Communication Technology (GeoICT), Springer International Publishing, 2016, pp. 105- 124","A. Arozarena, G. Villa, N. Valcárcel, and B. Pérez, \"Integration of Remotely Sensed Data Into Geospatial Reference Information Databases. Un-Ggim National Approach,\" ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 721-5, 2016","S. Belongie, J. Malik, and J. Puzicha, \"Shape matching and object recognition using shape contexts,\" IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(4), 2002, pp. 509—522","J. Bennett, \"OpenStreetMap - Be your own cartographer,\" ISBN: 978-1-84719-750-4, Packt Publishing, 2011","A. Bronstein, R. Kimmel, M. Mahmoudi, and G. Sapiro, \"A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching,\" International Journal of Computer Vision, vol. 89(2-3), 2010, pp. 266-286","X. Chen, \"Spatial relation between uncertain sets,\" International archives of Photogrammetry and remote sensing, vol. 31(B3), Vienna, 1996, pp. 105-110","S. Filin and Y. Doytsher, \"The detection of corresponding objects in a linear-based map conflation,\" Surveying and land information systems, vol. 60(2), 2000, pp. 117-127","D. Foster and C. Mayfield, \"Geospatial Resource Integration in Support of Homeland Defense and Security\", International Journal of Applied Geospatial Research (IJAGR), vol. 7(4):53-63, 2016","H. Kim and C. Chung, \"Geo-spatial data integration for subsurface stratification of dam site with outlier analyses,\" Environmental Earth Sciences, vol. 75(2), 2016","A. Kipf and A. Kemper, \"An Integration Platform for Temporal Geospatial Data. Digital Mobility Platforms and Ecosystems,\" 2016","M. Landa, \"GRASS GIS 7.0: Interoperability improvements,\" GIS Ostrava, Jan. 2013, pp.21-23","T. Ma and J. Longin, \"From partial shape matching through local deformation to robust global shape similarity for object detection,\" Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, 2011, pp. 1441-1448","A. Noskov and Y. Doytsher, \"A Segmentation-based Approach for Improving the Accuracy of Polygon Data,\" GEOProcessing 2015, Portugal, 2015, pp. 69-74","A. Noskov and Y. Doytsher, \"Triangulation and Segmentation-based Approach for Improving the Accuracy of Polygon Data,\" International Journal on Advances in Software, vol. 9 (1-2), 2016","C. Parent and S. Spaccapietra, \"Database integration: the key to data interoperability,\" Advances in Object-Oriented Data Modeling, M. P. Papazoglou, S. Spaccapietra, Z. Tari (Eds.), The MIT Press, 2000","K. Piętak, J. Dajda, M. Wysokiński, M. Idzik, and L. Leśniak, \"Geospatial Data Integration for Criminal Analysis\", Man– Machine Interactions 4, Springer International Publishing, 2016, pp. 461-471","E. Rahm and P. Bernstein, \"A survey of approaches to automatic schema matching,\" The International Journal on Very Large Data Bases (VLDB), vol. 10(4), 2001, pp. 334– 350","Saalfeld, \"Conflation-automated map compilation,\" nternational Journal of Geographical Information Science (IJGIS), vol. 2 (3), 1988, pp. 217–228.","B. Schmitzer and C. Schnorr, \"Object segmentation by shape matching with Wasserstein modes,\" Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer Berlin Heidelberg, 2013","M. Shapiro and J. Westervelt, \"r. mapcalc: An algebra for GIS and image processing\", Construction Engineering Research Lab (ARMY), Champaign IL; 1994","X. Shu and X. Wu, \"A novel contour descriptor for 2D shape matching and its application to image retrieval,\" Image and vision Computing, vol. 29.4, 2011, pp. 286-294","P. Shvaiko and J. Euzenat, \"A survey of schema-based matching approaches,\" Journal on Data Semantics IV, Springer Berlin Heidelberg, 2005, pp. 146-171","N. Tsendbazar, S. Bruin, S. Fritz, and M. Herold, \"Spatial Accuracy Assessment and Integration of Global Land Cover Datasets\", Remote Sensing, vol. 7(12):15804-21, 2015","G. Wiederhold, \"Mediation to deal with heterogeneous data sources,\" Interoperating Geographic Information System, 1999, pp. 1–16","R. Xie, Y. Liu, X. Li, L. Yu, \"A Framework of Satellite Observation Data Integration System,\" International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC 2015), 2015","S. Zheng and J. Zheng, \"Assessing the completeness and positional accuracy of OpenStreetMap in China,\" Thematic Cartography for the Society, Springer International Publishing, 2014, pp. 171-189","http://wiki.openstreetmap.org/wiki/Zoom_levels [accessed 15.06.2017]","https://github.com/gravitystorm/openstreetmap- carto/blob/master/roads.mss [accessed 15.06.2017]"]}

Often, same polygon objects are presented in Geoinformational Systems by distinct geometries with random positional discrepancies. It makes difficult to detect correspondences between data layers containing same object or parts of objects. The suggested method allows the user to improve the accuracy of one polygon layer by another more accurate polygon dataset by defining correspondences between polygons and parts of polygon boundaries. Two main techniques are applied: triangulation and segmentation. The triangulation is used to define correspondences between whole polygons by comparing triples of polygons. The segmentation approach is applied for the remaining polygons. Existing approaches do not work well in the case of partial equality of polygon boundaries. The main idea of the segmentation algorithm in this paper is based on defining correspondent segments of polygon boundaries and further replacing polygon boundary segments of the non-accurate layer with segments of the accurate data set; segments without pairs are rectified using ground control points. The resulting data contain parts of the accurate data set polygon boundaries, whereas the remaining elements are rectified according to the replaced boundary segments. From a review implemented by specialists it might be concluded that the results are satisfactory. The developed method could be applied to various types of polygonal datasets with similar scale., {"references":["A. Noskov and Y. Doytsher, \"A Segmentation-based Approach for Improving the Accuracy of Polygon Data,\" GEOProcessing 2015, 2009, Portugal, pp. 69-74","S. Filin and Y. Doytsher, \"The detection of corresponding objects in a linear-based map conflation,\" Surveying and land information systems, vol. 60(2), 2000, pp. 117-127","V. Walter and D. Fritsch, \"Matching spatial data sets: a statistical approach,\" International Journal of Geographical Information Science (IJGIS), vol. 13 (5), 1999, pp. 445–473","X. Shu and X. Wu. \"A novel contour descriptor for 2D shape matching and its application to image retrieval\", Image and vision Computing, vol. 29.4, 2011, pp. 286-294","S. Belongie, J. Malik, and J. Puzicha, \"Shape matching and object recognition using shape contexts,\" IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(4), 2002, pp. 509-522","E. Safra, Y. Kanza, Y. Sagiv, C. Beeri, and Y. Doytsher, \"Ad- hoc matching of vectorial road networks,\" International Journal of Geographical Information Science, iFirst, 2012, pp. 1–40, ISSN: 1365-8816, ISSN: 1362-3087","B. Schmitzer and S. Christoph, \"Object segmentation by shape matching with Wasserstein modes,\" Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer Berlin Heidelberg, 2013","T. Ma and J. Longin, \"From partial shape matching through local deformation to robust global shape similarity for object detection,\" Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, 2011, pp. 1441-1448","X. Chen, \"Spatial relation between uncertain sets,\" International archives of Photogrammetry and remote sensing, vol. 31(B3), Vienna, 1996, pp. 105-110","A. Bronstein, R. Kimmel, M. Mahmoudi, and G. Sapiro, \"A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching,\" International Journal of Computer Vision, vol. 89(2-3), 2010, pp. 266-286","P. Tahmasebi, A. Hezarkhani, and M. Sahimi, \"Linking Objects of Different Spatial Data Sets by Integration and Aggregation,\" vol. 2(4), 1998, pp. 335-358","E. Rahm and P. Bernstein, \"A survey of approaches to automatic schema matching,\" The International Journal on Very Large Data Bases (VLDB), vol. 10(4), 2001, pp. 334– 350","V. Kashyap and A. Sheth, \"Semantic and schematic similarities between database objects: a context-based approach,\" The International Journal on Very Large Data Bases (VLDB), vol. 5(4), 1996, pp. 276–304","P. Shvaiko and J. Euzenat, \"A survey of schema-based matching approaches,\" Journal on Data Semantics IV, Springer Berlin Heidelberg, 2005, pp. 146-171","B. Amann, C. Beeri., I. Fundulaki, and M. Scholl., \"Ontology-based integration of XML Web resources,\" 1st International Semantic Web Conference (ISWC), Sardinia, Italy, June 9-12 2002, pp. 117–131","L. Gravano, P. G. Ipeirotis, H. V. Jagadish, N. Koudas, S. Muthukrishnan, and D. Srivastava, \"Approximate String Joins in a Database (Almost) for Free,\" Proceedings of the 27th International Conference on Very Large Data Bases, Italy, 2001","L. Gravano, P. G. Ipeirotis, N. Koudas, and D. Srivastava, \"Text joins in an RDBMS for web data integration,\" Proceedings of the 12th international conference on World Wide Web. ACM, 2003","C. Parent and S. Spaccapietra, \"Database integration: the key to data interoperability,\" Advances in Object-Oriented Data Modeling, M. P. Papazoglou, S. Spaccapietra, Z. Tari (Eds.), The MIT Press, 2000","G. Wiederhold, \"Mediation to deal with heterogeneous data sources,\" Interoperating Geographic Information System, 1999, pp. 1–16","A. Saalfeld, \"Conflation-automated map compilation,\" International Journal of Geographical Information Science (IJGIS), vol. 2 (3), 1988, pp. 217–228","M. Sester, K. Anders, and V. Walter, \"Linking Objects of Different Spatial Data Sets by Integration and Aggregation,\" GeoInformatica, vol. 2(4), 1998, pp. 335-358","C. Steger, M. Ulrich, and C. Wiedemann, \"Machine vision algorithms and applications\", Weinheim: wiley-VCH, 2008, pp. 1-2","G. Bradski and A. Kaehler, \"Learning OpenCV: Computer vision with the OpenCV library,\" O'Reilly Media, Inc, 2008","J. Howse, \"OpenCV Computer Vision with Python,\" Packt Publishing Ltd, 2013, ISBN: 978-1-78216-392-3","J. Shewchuk, \"Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator,\" Applied computational geometry towards geometric engineering, Springer Berlin Heidelberg, 1996, pp. 203-222","F. Aurenhammer, \"Voronoi diagrams - a survey of a fundamental geometric data structure,\" ACM Computing Surveys (CSUR), vol. 23(3), 1991, pp. 345-405","M. Landa, \"GRASS GIS 7.0: Interoperability improvements,\" GIS Ostrava, Jan. 2013, pp. 21-23","R. Blazek, M. Neteler, and R. Micarelli, \"The new GRASS 5.1 vector architecture,\" Open source GIS-GRASS users conference, University of Trento, Italy, 2002","J. Herring, \"OpenGIS Implementation Standard for Geographic information-Simple feature access-Part 1: Common architecture,\" OGC Document 4, no. 21, 2011","I. Wilson, J.M. Ware, and J.A. Ware, \"A genetic algorithm approach to cartographic map generalisation\" Computers in Industry, vol. 52(3), 2003, pp. 291-304","B. Gaster, L. Howes, D. Kaeli, P. Mistry, and D. Schaa, \"Heterogeneous Computing with OpenCL: Revised OpenCL1,\" Newnes, 2012"]}

The developed method allows the user to integrate polygonal or linear datasets. Most existing approaches do not work well in the case of partial equality of polygons. The suggested method consists of two phases: searching for counterpart boundaries or polylines by triangulation, and rectifying objects without correspondent polylines by a transformation and a shortest path algorithm. At the first phase, middle points of polygon boundaries are used to implement the triangulation. In order to define correspondent boundaries, the polylines of the two datasets which are connected by triangles are compared based on the lengths of lines and the distances between the nodes. At the second phase, vertices of the polylines without counterparts are shifted with respect to the lengths of the shortest distances to the nodes of the polylines with counterpart. The method is effective for pairs of datasets with different degrees of accuracy. Less accurate datasets use precise elements of other datasets for integration and improvement of their accuracy. The resulting data are well integrated with a more accurate map. A review implemented by specialists enables us to say that the results are satisfactory., {"references":["S. Belongie, J. Malik, and J. Puzicha, \"Shape matching and object recognition using shape contexts,\" IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(4), 2002, pp. 509—522","J. Bennett, \"OpenStreetMap - Be your own cartographer,\" ISBN: 978-1-84719-750-4, Packt Publishing, 2011","A. Bronstein, R. Kimmel, M. Mahmoudi, and G. Sapiro, \"A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching,\" International Journal of Computer Vision, vol. 89(2-3), 2010, pp. 266-286","X. Chen, \"Spatial relation between uncertain sets,\" International archives of Photogrammetry and remote sensing, vol. 31(B3), Vienna, 1996, pp. 105-110","B. Schmitzer and C. Schnorr, \"Object segmentation by shape matching with Wasserstein modes,\" Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer Berlin Heidelberg, 2013","S. Filin and Y. Doytsher, \"The detection of corresponding objects in a linear-based map conflation,\" Surveying and land information systems, vol. 60(2), 2000, pp. 117-127","A. Noskov and Y. Doytsher, \"A Segmentation-based Approach for Improving the Accuracy of Polygon Data,\" GEOProcessing 2015, Portugal, 2015, pp. 69-74","A. Noskov and Y. Doytsher, \"Triangulation and Segmentation-based Approach for Improving the Accuracy of Polygon Data,\" International Journal on Advances in Software, vol. 9 (1-2), 2016, accepted, in progress","C. Parent and S. Spaccapietra, \"Database integration: the key to data interoperability,\" Advances in Object-Oriented Data Modeling, M. P. Papazoglou, S. Spaccapietra, Z. Tari (Eds.), The MIT Press, 2000","E. Rahm and P. Bernstein, \"A survey of approaches to automatic schema matching,\" The International Journal on Very Large Data Bases (VLDB), vol. 10(4), 2001, pp. 334– 350","A. Saalfeld, \"Conflation-automated map compilation,\" International Journal of Geographical Information Science (IJGIS), vol. 2 (3), 1988, pp. 217–228","X. Shu and X. Wu. \"A novel contour descriptor for 2D shape matching and its application to image retrieval\", Image and vision Computing, vol. 29.4, 2011, pp. 286-294","P. Shvaiko and J. Euzenat, \"A survey of schema-based matching approaches,\" Journal on Data Semantics IV, Springer Berlin Heidelberg, 2005, pp. 146-171","G. Wiederhold, \"Mediation to deal with heterogeneous data sources,\" Interoperating Geographic Information System, 1999, pp. 1–16","S. Zheng and J. Zheng, \"Assessing the completeness and positional accuracy of OpenStreetMap in China,\" Thematic Cartography for the Society, Springer International Publishing, 2014, pp. 171-189","M. Landa, \"GRASS GIS 7.0: Interoperability improvements,\" GIS Ostrava, Jan. 2013, pp.21-23","T. Ma and J. Longin, \"From partial shape matching through local deformation to robust global shape similarity for object detection,\" Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, 2011, pp. 1441-1448"]}

The suggested method enables us to improve the accuracy of city planning data by matching it with exact cadastral data. The existing approaches do not work well in the case of partial equality of polygon boundaries. The main idea of the presented algorithm in this paper is based on defining correspondent segments of polygon boundaries and further replacing polygon boundary segments of the non-accurate layer by segments of the accurate data set, segments without pairs are rectified using ground control points. The resulting data contain parts of the accurate data set polygon boundaries, whereas the remaining elements are rectified according to the replaced boundary segments. A review implemented by specialists enables us to say, that the results are satisfactory., {"references":["F. Aurenhammer, \"Voronoi diagrams—a survey of a fundamental geometric data structure,\" ACM Computing Surveys (CSUR), vol. 23(3), 1991, pp. 345-405","S. Belongie, J. Malik, and J. Puzicha, \"Shape matching and object recognition using shape contexts,\" IEEE Trans. on [1] [22] Pattern Analysis and Machine Intelligence, 24(4), 2002, pp. 509—522","G. Bradski and A. Kaehler, \"Learning OpenCV: Computer vision with the OpenCV library,\" O'Reilly Media, Inc, 2008","A. Bronstein, R. Kimmel, M. Mahmoudi, and G. Sapiro, \"A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching,\" International Journal of Computer Vision, vol. 89(2-3), 2010, pp. 266-286","X. Chen, \"Spatial relation between uncertain sets,\" International archives of Photogrammetry and remote sensing, vol. 31(B3), Vienna, 1996, pp. 105-110","Schmitzer, Bernhard, and S. Christoph, \"Object segmentation by shape matching with Wasserstein modes,\" Energy Minimization Methods in Computer Vision and Pattern Recognition, Springer Berlin Heidelberg, 2013","S. Filin and Y. Doytsher, \"The detection of corresponding objects in a linear-based map conflation,\" Surveying and land information systems, vol. 60(2), 2000, pp. 117-127","B. Gaster, L. Howes, D. Kaeli, P. Mistry, and D. Schaa, \"Heterogeneous Computing with OpenCL: Revised OpenCL1,\" Newnes, 2012","J. Howse, \"OpenCV Computer Vision with Python,\" Packt Publishing Ltd, 2013","C. Parent and S. Spaccapietra, \"Database integration: the key to data interoperability,\" Advances in Object-Oriented Data Modeling, M. P. Papazoglou, S. Spaccapietra, Z. Tari (Eds.), The MIT Press, 2000","E. Rahm and P. Bernstein, \"A survey of approaches to automatic schema matching,\" The International Journal on Very Large Data Bases (VLDB), vol. 10(4), 2001, pp. 334– 350","A. Saalfeld, \"Conflation-automated map compilation,\" International Journal of Geographical Information Science (IJGIS), vol. 2 (3), 1988, pp. 217–228","E. Safra, , Y. Kanza, Y. Sagiv, C. Beeri, and Y. Doytsher, \"Ad-hoc matching of vectorial road networks,\" International Journal of Geographical Information Science, iFirst, 2012, pp. 1–40, ISSN: 1365-8816, ISSN: 1362-3087","J. Shewchuk, \"Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator,\" Applied computational geometry towards geometric engineering, Springer Berlin Heidelberg,1996, pp. 203-222","X. Shu and X. Wu. \"A novel contour descriptor for 2D shape matching and its application to image retrieval\", Image and vision Computing, vol. 29.4, 2011, pp. 286-294","P. Shvaiko and J. Euzenat, \"A survey of schema-based matching approaches,\" Journal on Data Semantics IV, Springer Berlin Heidelberg, 2005, pp. 146-171","C. Steger, M. Ulrich, and C. Wiedemann, Machine vision algorithms and applications, Weinheim: wiley-VCH, 2008, pp. 1-2","V. Walter and D. Fritsch, \"Matching spatial data sets: a statistical approach,\" International Journal of Geographical Information Science (IJGIS), vol. 13 (5), 1999, pp. 445–473","G. Wiederhold, \"Mediation to deal with heterogeneous data sources,\" Interoperating Geographic Information System, 1999, pp. 1–16","I. Wilson, J.M. Ware, and J.A. Ware, \"A genetic algorithm approach to cartographic map generalisation\" Computers in Industry, vol. 52(3), 2003, pp. 291-304","M. Landa, \"GRASS GIS 7.0: Interoperability improvements,\" GIS Ostrava, Jan. 2013, pp.21-23","T. Ma and J. Longin, \"From partial shape matching through local deformation to robust global shape similarity for object detection,\" Computer Vision and Pattern Recognition (CVPR), IEEE Conference on. IEEE, 2011, pp. 1441-1448"]}

The suggested method for 3D generalization of groups of buildings in urban environments is based on the rasterization of the 2D footprints of 3D buildings. The rasterization is processed within quarters, which are automatically defined by using Digital Elevation Model (DEM), water objects and roads. Quarters were organized into a hierarchical model according to the gaps between the quarters and the stages of the clustering process. Each degree of generalization corresponds to some level of hierarchy. The 3D urban perspective is computed based on separate levels of generalization of each quarter as a function of its distance from a pre-defined view point. The developed approach enables to compile a 3D scene of urban environment based on the generalized buildings' layer. The buildings' layer consists of objects with different degree of generalization level which is growing gradually from the view point. The two main distinctions of the approach from others are: (1) the generalization is implemented with respect to the geospatial properties of urban environments and the relations between the objects; (2) the approach is simple and universal which enables to simplify the whole area of a city and can be applied to different types of cities., {"references":["Noskov A., Doytsher Y., \"Urban Perspectives: A Raster-Based Approach to 3D Generalization of Groups of Buildings,\" GEOProcessing 2013, pp. 67-72, France, 2013","Ankerst M., Breunig M., Kriegel H. 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The suggested method for 3D generalization of groups of buildings is based on rasterization of 2D footprints of the 3D buildings. The rasterization is processed within quarters, which are automatically defined by using Digital Elevation Model (DEM), water objects and roads. The 3D urban perspective is computed based on separate levels of generalization of each quarter as a function of its distance from a pre-defined view point., {"references":["J. Döllner and H. Buchholz, \"Continuous Level-of-detail Modeling of Buildings in 3D City Models,\" in GIS'05 Proceedings of the 13th Annual ACM International Workshop on Geographic Information Systems, Bremen, Germany, 2005, pp. 173-181.","A. Forberg, \"Generalization of 3D Building Data Based on a Scale- Space Approach,\" ISPRS Journal of Photogrammetry & Remote Sensing, vol. 62, 2007, pp. 104-111","T. Glander and J. 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In the last decade or so, there has been a very clear transition in many countries throughout the world from a graphical cadastre and/or relatively non-accurate digital cadastre toward an accurate coordinate based legal cadastre. Aiming at defining accurately the turning points position of the cadastral sub-division based on current data without the need to re-measure the cadastral entities, motivates the development of new algorithms and approaches suitable to performing the task. Implementation on a nationwide level requires to first develop advanced mathematical algorithms and methods to process separate parcellations (cadastral blocks or mutation plans), and then additional algorithms and methods to combine the numerous separate parcellations into a cadastral continuity maintaining rigid topological compatibility. Practical experience, especially from the Israeli viewpoint, indicates that implementation of advanced computational techniques for processing separate cadastral blocks, is only a partial solution of the problem. An optimal joining of the separate cadastral blocks into a homogeneous seamless cadastral space constitutes a complex task due to discrepancies between the adjoining parcellations. These discrepancies, significant in terms of their magnitude and characteristics, are mainly caused by the cadastral parcellation process based on separate cadastral measuring projects on the one hand, and limited accuracy of the measuring techniques in previous decades (mainly in the first half of the 20th century) on the other hand. The paper introduces a new algorithm based on the existing mathematical model, customary in photogrammetric mapping, aimed at connecting the adjoining photographs into blocks based on Block Adjustment by Independent Models. The proposed adjustment method (named the "Cadastral Triangulation") is executed based on the classic Adjustment of Indirect Observations combined with the Chained Similarity Transformation. This adjustment process which is carried out by a global transformation mechanism, enables obtaining both optimal transformation parameters of all the separate parcellations, as well as optimal coordinates of the cadastral boundary turning points. The initial results of the proposed method indicate its effectiveness in connecting the adjoining cadastral blocks, effectiveness expressed by a significant decrease of systematic and random errors compared to their pre-adjusted situation. Additionally, the proposed method enables bringing the adjusted cadastral boundary turning points maximally close to their theoretical true (and unknown) locations and, in any case, much closer than locations computed by currently practiced methods. Therefore, the proposed method may effectively be used as a primary computational algorithm for implementing a nationwide coordinate based legal cadastre.

A viable approach for tackling the challenges of integration and analysis of geospatial raster data is to pre-process datasets into a common framework and store them into a cloud repository, accessible through a set of well-defined access protocols. This paper describes an initiative called GeoCubes Finland, where the aim is to provide a number of country-wide raster geodatasets in a common schema. In addition to more traditional access methods, a custom Application Programming Interface (API) has been designed for supporting the various tasks related to retrieval, use, visualisation and analysis of the contained raster datasets.

The paper presents the creation of the mathematical models from experimental data, which are nonlinear and multidimensional. Such approach is required in many computer-based simulations for the variety of technical objects. It is well known that the modern mathematical methods are not able to define, at the same time, both: the needed numerical accuracy and the analytical properties. The presented approximation method for the non- linear experimental data is highly accurate mathematical model, which contains interesting analytical properties. The method correctness has been validated analytically and experimentally for 1-dimensional and 2- dimensional objects. The method accuracy is based on the data “piecewise” approximation, i.e. their local fragmentation. However, in contrast to the “piecewise” approximation, the numerical model fragments are combined by multiplicative-additive transformation, and not by fulfilling the coordinate-logical conditions. Therefore, such numerical model has analytical properties, which are used in the mathematical transformations, as multiplicative transformation of the created analytic functions, called “cut out” functions. These functions are locally approximating the data fragments and have analytical properties, which enable them to be added, when the combined analytical model is defined. The method implementation consists of the following successive and relatively independent stages: (1) splitting the data array into fragments, (2) their polynomial approximation, (3) the multiplicative transformation of fragments and (4) their additive combination into only one analytical function. The proposed method is appropriate for the experimental mathematical modeling of complex non-linear objects, in particular, for their use in the physical and technical simulation processes. The authors envisage that this proposed method would be especially useful for the mathematical modeling of the physics occurring in turbulent flows.

Providers of geospatial data are facing the challenge of di-verse user needs when delivering their products to different user groups. Academic researchers represent a user group with quite specific requirements, like good support for anal-ysis and high-performance computing. A national infrastruc-ture providing both geospatial data and powerful geocompu-ting facilities for research use is being developed in Finland. The part of the infrastructure described in this paper focuses on the management, storage and efficient delivery of raster-formatted geospatial data by applying the concept of datacube.

The Cellular Automata based method for Minimizing Flow (CAMF) aims at selecting, from a rasterized database representing a river catchment, a predefined number of cells that should be afforested in order to minimize the sediment yield of the catchment. To this end, CAMF iteratively ranks cells according to sediment yield reduction, taking into account spatial interaction among cells. It was found during tests that the execution time of CAMF is directly proportional to the database size and the number of cells to be selected. This behavior can become a limiting factor for the applicability of CAMF to high resolution databases that cover large geographical areas. This issue motivated the necessity of exploring simplified CAMF variants that reduce its execution time and preserve the accuracy of its results. For this purpose, a simplified variant called on-site CAMF was devised, implemented and tested. On-site CAMF ranks cells based only on local cell information, i.e., the local sediment reduction that afforestation would produce in a cell, and the cell slope. During tests, on-site CAMF produced virtually the same results as the original version of CAMF in only a small fraction of the execution time. This means that, for these particular tests, spatial interaction did not influence CAMF output, possibly due to the number of cells that were selected, which was small with respect to the full geodatabase size. It is expected that spatial interaction becomes a relevant factor when larger sets of cells are selected. ispartof: pages:94-100 ispartof: Proceedings of GEOProcessing 2014: The Sixth International Conference on Advanced Geographic Information Systems, Applications, and Services pages:94-100 ispartof: GEOProcessing 2014: The Sixth International Conference on Advanced Geographic Information Systems, Applications, and Services location:Barcelona, Spain date:23 Mar - 27 Mar 2014 status: published