Sinuosity pattern recognition of road features for segmentation purposes in cartographic generalization

In line generalization, results depend very much on the characteristics of the line in question. For this reason it would be useful to obtain an automatic segmentation and enrichment of lines in order to apply to each section the best algorithm and the most appropriate parameter. In this paper, we present a line segmentation methodology based on a sinuosity pattern recognition measured by means of the effective-area as derived from the Visvalingam-Whyatt algorithm. Sections are determined by applying the Douglas-Peucker algorithm to a shape signature of the line: an effective-area/length space representation. An experiment is carried out with a set of 24 road features from a 1:25000 scale map with a recommendation of the value of some parameters and a procedure for the automated search of that defined as natural number of sections. This procedure is based in the search of zones of stability in a graph of the number of sections when applying Douglas-Peucker to the shape signature. The results are positively assessed by an independent group of experts.

[1]  Olli Jaakkola,et al.  Multi-scale Categorical Data Bases with Automatic Generalization Transformations Based on Map Algebra , 1998 .

[2]  Anne Ruas,et al.  Evaluation in the Map Generalisation Process , 2007 .

[3]  Jean-Claude Müller,et al.  Line Generalization Based on Analysis of Shape Characteristics , 1998 .

[4]  Mahes Visvalingam,et al.  The Douglas‐Peucker Algorithm for Line Simplification: Re‐evaluation through Visualization , 1990, Comput. Graph. Forum.

[5]  Dirk Burghardt,et al.  Controlled Line Smoothing by Snakes , 2005, GeoInformatica.

[6]  Satyandra K. Gupta,et al.  A Survey of Shape Similarity Assessment Algorithms for Product Design and Manufacturing Applications , 2003, J. Comput. Inf. Sci. Eng..

[7]  Francisco Javier Ariza-López,et al.  Generalization-oriented road line segmentation by means of an artificial neural network applied over a moving window , 2008, Pattern Recognit..

[8]  G. Dutton.  Scale, Sinuosity, and Point Selection in Digital Line Generalization , 1999 .

[9]  Haigang Sui,et al.  An Integrated Technique for Automated Generalization of Contour Maps , 2000 .

[10]  Dfl Dorling,et al.  Geographic Information Research: Bridging the Atlantic , 1996 .

[11]  José Luis García Balboa,et al.  Segmentación de elementos lineales mediante filtrado de Douglas-Peucker aplicado a las áreas efectivas de Visvalingam-Whyhatt , 2004 .

[12]  Alessandro Cecconi,et al.  Integration of Cartographic Generalization and Multi-Scale Databases for Enhanced Web Mapping , 2003 .

[13]  Emmanuel Fritsch,et al.  The Importance of Geometric Modeling in Linear Feature Generalization , 1995 .

[14]  Robert B McMaster,et al.  Generalization in Digital Cartography , 2008 .

[15]  Anne Ruas,et al.  Experiments with Learning Techniques for Spatial Model Enrichment and Line Generalization , 1998, GeoInformatica.

[16]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[17]  Dana H. Ballard,et al.  Strip trees: a hierarchical representation for curves , 1981, CACM.

[18]  L. Sarjakoski.  Conceptual Models of Generalisation and Multiple Representation , 2007 .

[19]  William Mackaness,et al.  Computational Processes for Map Generalization , 1999 .

[20]  Yvan Bédard,et al.  A Data Warehouse Strategy for on-Demand Multiscale Mapping , 2007 .

[21]  Maheswari Visvalingam,et al.  A computer science perspective on the bendsimplification algorithm , 1999 .

[22]  Peter J. Williamson,et al.  Simplification and generalization of large scale data for roads : a comparison of two filtering algorithms , 1995 .

[23]  J. L. G. Balboa,et al.  Frequency Filtering of Linear Features by Means of Wavelets. A Method and an Example , 2000 .

[24]  Anne Ruas,et al.  Observations and Research Challenges in Map Generalisation and Multiple Representation , 2007 .

[25]  N. Regnauld,et al.  A synoptic View of Generalisation Operators , 2007 .

[26]  K. Thapa.  Automatic line generalization using zero-crossings , 1988 .

[27]  Francisco Javier Hermosilla Cárdenas.  Restitución numérica del Mapa Topográfico nacional 1:25.000 en el Instituto Geográfico Nacional , 1997 .

[28]  José Luis García Balboa.  Automatización de los procesos de segmentación y clasificación de vías de comunicación en generalización cartográfica , 2006 .

[29]  Sven Loncaric,et al.  A survey of shape analysis techniques , 1998, Pattern Recognit..

[30]  Andriani Skopeliti,et al.  On the Parametric Description of the Shape of the Cartographic Line , 1999 .

[31]  Zhilin Li,et al.  Digital Map Generalization at the Age of Enlightenment: a Review of the First Forty Years , 2007 .

[32]  J. D. Whyatt,et al.  Line generalisation by repeated elimination of points , 1993 .

[33]  Howard Veregin,et al.  Line Simplification, Geometric Distortion, and Positional Error , 1999 .

[34]  Christopher B. Jones,et al.  Characterisation and generalisation of cartographic lines using Delaunay triangulation , 2002, Int. J. Geogr. Inf. Sci..

[35]  Zhilin Li.  Algorithmic Foundation of Multi-Scale Spatial Representation , 2006 .

[36]  R. McMaster,et al.  Map Generalization: Making Rules for Knowledge Representation , 1991 .

[37]  Anne Ruas,et al.  Modelling the Overall Process of Generalisation , 2013 .

[38]  Wenzhong Shi,et al.  Performance Evaluation of Line Simplification Algorithms for Vector Generalization , 2006 .

[39]  Francisco Javier Ariza-López,et al.  Generalization-oriented Road Line Classification by Means of an Artificial Neural Network , 2008, GeoInformatica.