A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints

[1]  X. Zhang,et al.  Deriving map images of generalised mountain roads with generative adversarial networks , 2022, Int. J. Geogr. Inf. Sci..

[2]  Xiongfeng Yan,et al.  Pattern Recognition and Segmentation of Administrative Boundaries Using a One-Dimensional Convolutional Neural Network and Grid Shape Context Descriptor , 2022, ISPRS Int. J. Geo Inf..

[3]  Wenhao Yu,et al.  Data‐driven polyline simplification using a stacked autoencoder‐based deep neural network , 2022, Trans. GIS.

[4]  G. Touya,et al.  Constraint-Based Evaluation of Map Images Generalized by Deep Learning , 2022, Journal of Geovisualization and Spatial Analysis.

[5]  T. Ai,et al.  Detecting interchanges in road networks using a graph convolutional network approach , 2022, Int. J. Geogr. Inf. Sci..

[6]  Fang Wu,et al.  Polyline simplification based on the artificial neural network with constraints of generalization knowledge , 2022, Cartography and Geographic Information Science.

[7]  Q. Guo,et al.  An improved integrated generalization method for contours and Rivers using importance sequence of all points from these two elements , 2021, Geocarto International.

[8]  Zhiwei Wei,et al.  A Progressive and Combined Building Simplification Approach with Local Structure Classification and Backtracking Strategy , 2021, ISPRS Int. J. Geo Inf..

[9]  Xianyong Gong,et al.  An Automated Approach to Coastline Simplification for Maritime Structures with Collapse Operation , 2021 .

[10]  Xianyong Gong,et al.  Segmentation and sampling method for complex polyline generalization based on a generative adversarial network , 2021, Geocarto International.

[11]  Tinghua Ai,et al.  A hybrid approach to building simplification with an evaluator from a backpropagation neural network , 2021, Int. J. Geogr. Inf. Sci..

[12]  Xinchang Zhang,et al.  Contour line simplification method based on the two‐level Bellman–Ford algorithm , 2020, Trans. GIS.

[13]  Chengming Li,et al.  A complex junction recognition method based on GoogLeNet model , 2020, Trans. GIS.

[14]  Xiaohua Tong,et al.  Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps , 2020, Int. J. Geogr. Inf. Sci..

[15]  Tianyuan Xiao,et al.  A multi-scale representation model of polyline based on head/tail breaks , 2020, Int. J. Geogr. Inf. Sci..

[16]  Lawrence V. Stanislawski,et al.  Simplification of polylines by segment collapse: minimizing areal displacement while preserving area , 2019, International Journal of Cartography.

[17]  Chengming Li,et al.  A simplification of urban buildings to preserve geometric properties using superpixel segmentation , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Monika Sester,et al.  Learning Cartographic Building Generalization with Deep Convolutional Neural Networks , 2019, ISPRS Int. J. Geo Inf..

[19]  Guillaume Touya,et al.  Is deep learning the new agent for map generalization? , 2019, International Journal of Cartography.

[20]  Song Gao,et al.  Transferring multiscale map styles using generative adversarial networks , 2019, International Journal of Cartography.

[21]  Min Yang,et al.  A graph convolutional neural network for classification of building patterns using spatial vector data , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[22]  Hui Yang,et al.  A coastline generalization method that considers buffer consistency , 2018, PloS one.

[23]  Xiang Zhang,et al.  Template Matching and Simplification Method for Building Features Based on Shape Cognition , 2017, ISPRS Int. J. Geo Inf..

[24]  Timofey E. Samsonov,et al.  Shape-adaptive geometric simplification of heterogeneous line datasets , 2017, Int. J. Geogr. Inf. Sci..

[25]  Jingzhong Li,et al.  Envelope generation and simplification of polylines using Delaunay triangulation , 2017, Int. J. Geogr. Inf. Sci..

[26]  StefanakisEmmanuel,et al.  mR-V: Line Simplification through Mnemonic Rasterization , 2016 .

[27]  Tinghua Ai,et al.  Fourier-based multi-scale representation and progressive transmission of cartographic curves on the internet , 2016 .

[28]  Bettina Speckmann,et al.  Area-Preserving Simplification and Schematization of Polygonal Subdivisions , 2016, ACM Trans. Spatial Algorithms Syst..

[29]  Tinghua Ai,et al.  Area‐preservation Simplification of Polygonal Boundaries by the Use of the Structured Total Least Squares Method with Constraints , 2015, Trans. GIS.

[30]  T. Ai,et al.  A Simplification of Ria Coastline with Geomorphologic Characteristics Preserved , 2014 .

[31]  Hongwei Lin,et al.  Progressive and iterative approximation for least squares B-spline curve and surface fitting , 2014, Comput. Aided Des..

[32]  Yong Wang,et al.  Building simplification using backpropagation neural networks: a combination of cartographers' expertise and raster-based local perception , 2013 .

[33]  Pierre Vandergheynst,et al.  Vertex-Frequency Analysis on Graphs , 2013, ArXiv.

[34]  Paulo Raposo Scale-specific automated line simplification by vertex clustering on a hexagonal tessellation , 2013 .

[35]  Kiyun Yu,et al.  Hybrid line simplification for cartographic generalization , 2011, Pattern Recognit. Lett..

[36]  Alexander Wolff,et al.  Optimal and topologically safe simplification of building footprints , 2010, GIS '10.

[37]  R. Gribonval,et al.  Wavelets on graphs via spectral graph theory , 2009, 0912.3848.

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

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

[40]  Sébastien Mustière,et al.  Cartographic generalization of roads in a local and adaptive approach: A knowledge acquistion problem , 2005, Int. J. Geogr. Inf. Sci..

[41]  Monika Sester,et al.  Optimization approaches for generalization and data abstraction , 2005, Int. J. Geogr. Inf. Sci..

[42]  Eric Saux,et al.  B-spline Functions and Wavelets for Cartographic Line Generalization , 2003 .

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

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

[45]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

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

[47]  Esther M. Arkin,et al.  An efficiently computable metric for comparing polygonal shapes , 1991, SODA '90.

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

[49]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[50]  S. Openshaw,et al.  A Natural Principle for the Objective Generalization of Digital Maps , 1993 .

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