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Journal ArticleDOI

Use of Artificial Neural Networks for Selective Omission in Updating Road Networks

28 Feb 2014-Cartographic Journal (Taylor & Francis)-Vol. 51, Iss: 1, pp 38-51
TL;DR: In this article, the use of a back propagation neural network (BPNN) and a self-organizing map (SOM) for selective omission in a road network was investigated.
Abstract: An important problem faced by national mapping agencies is frequent map updates. An ideal solution is only updating the large-scale map with other smaller scale maps undergoing automatic updates. This process may involve a series of operators, among which selective omission has received much attention. This study focuses on selective omission in a road network, and the use of an artificial neural network (i.e. a back propagation neural network, BPNN). The use of another type of artificial neural network (i.e. a self-organizing map, SOM) is investigated as a comparison. The use of both neural networks for selective omission is tested on a real-life road network. The use of a BPNN for practical application road updating is also tested. The results of selective omission are evaluated by overall accuracy. It is found that (1) the use of a BPNN can adaptively determine which and how many roads are to be retained at a specific scale, with an overall accuracy above 80%; (2) it may be hard to determine wh...
Citations
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Journal ArticleDOI
TL;DR: The significant challenges currently facing ISPRS and its communities are examined, such as providing high-quality information, enabling advanced geospatial computing, and supporting collaborative problem solving.
Abstract: With the increased availability of very high-resolution satellite imagery, terrain based imaging and participatory sensing, inexpensive platforms, and advanced information and communication technologies, the application of imagery is now ubiquitous, playing an important role in many aspects of life and work today. As a leading organisation in this field, the International Society for Photogrammetry and Remote Sensing (ISPRS) has been devoted to effectively and efficiently obtaining and utilising information from imagery since its foundation in the year 1910. This paper examines the significant challenges currently facing ISPRS and its communities, such as providing high-quality information, enabling advanced geospatial computing, and supporting collaborative problem solving. The state-of-the-art in ISPRS related research and development is reviewed and the trends and topics for future work are identified. By providing an overarching scientific vision and research agenda, we hope to call on and mobilise all ISPRS scientists, practitioners and other stakeholders to continue improving our understanding and capacity on information from imagery and to deliver advanced geospatial knowledge that enables humankind to better deal with the challenges ahead, posed for example by global change, ubiquitous sensing, and a demand for real-time information generation.

92 citations

Journal ArticleDOI
TL;DR: Although elimination is a direct part of the proposed process, generalization operations, such as simplification and aggregation of polygons, must still be performed for buildings classified as retained and aggregated, and these algorithms can be used for building classification and can serve as preparatory steps for building generalization.
Abstract: A critical problem in mapping data is the frequent updating of large data sets. To solve this problem, the updating of small-scale data based on large-scale data is very effective. Various map generalization techniques, such as simplification, displacement, typification, elimination, and aggregation, must therefore be applied. In this study, we focused on the elimination and aggregation of the building layer, for which each building in a large scale was classified as “0-eliminated,” “1-retained,” or “2-aggregated.” Machine-learning classification algorithms were then used for classifying the buildings. The data of 1:1000 scale and 1:25,000 scale digital maps obtained from the National Geographic Information Institute were used. We applied to these data various machine-learning classification algorithms, including naive Bayes (NB), decision tree (DT), k-nearest neighbor (k-NN), and support vector machine (SVM). The overall accuracies of each algorithm were satisfactory: DT, 88.96%; k-NN, 88.27%; SVM, 87.57%; and NB, 79.50%. Although elimination is a direct part of the proposed process, generalization operations, such as simplification and aggregation of polygons, must still be performed for buildings classified as retained and aggregated. Thus, these algorithms can be used for building classification and can serve as preparatory steps for building generalization.

22 citations

Journal ArticleDOI
TL;DR: A machine‐learning‐based approach is proposed, in which the road networks are first converted from lines to polygons, and various geometric descriptors are used to train a random forest classifier and identify the candidates.

19 citations


Cites methods from "Use of Artificial Neural Networks f..."

  • ...Among these, some have introduced intelligent algorithms, including a case study approach (Guo et al., 2014), a method that used the genetic algorithm (Wang & Deng, 2005), and another that used a neural network (Balboa & López, 2008; Zhou & Li, 2014)....

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Journal ArticleDOI
TL;DR: This study examines the various possible changes from different perspectives, such as the reasons for their occurrence, the forms in which they manifest, and their effects on output, and applies map algebra theory to establish a cartographic model for updating polygonal building data.

13 citations


Cites background from "Use of Artificial Neural Networks f..."

  • ...Zhou and Li (2014) have adopted artificial neural networks to acquire quantity and qualification knowledge from existing road networks at different scales and then used this knowledge to determine which and how many roads should be updated in an older, smaller-scale dataset based on a newer,…...

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References
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Journal ArticleDOI
TL;DR: In this article, three distinct intuitive notions of centrality are uncovered and existing measures are refined to embody these conceptions, and the implications of these measures for the experimental study of small groups are examined.

14,757 citations


"Use of Artificial Neural Networks f..." refers background or methods in this paper

  • ...…the road length, the more importance the road is viewed as possessing; 2. topological properties: they have been widely used to determine the important nodes or links within a network, such as a social network (Freeman, 1979) and a road network (Jiang and Claramunt, 2004; Crucitti et al., 2006)....

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  • ...topological properties: they have been widely used to determine the important nodes or links within a network, such as a social network (Freeman, 1979) and a road network (Jiang and Claramunt, 2004; Crucitti et al....

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Book
01 Jan 1995
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Abstract: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a manner which is accessible without prior expert knowledge.

12,920 citations

Journal ArticleDOI
TL;DR: A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation ANNs theory and design, and a generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation is described.

2,622 citations


"Use of Artificial Neural Networks f..." refers background in this paper

  • ...All the weights are readjusted to reduce the error (see equations (7)–(12), Basheer and Hajmeer, 2000; Fischer and Leung, 2001)....

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Book
14 Apr 1993
TL;DR: This paper presents a meta-modelling framework for evaluating the performance of Neural Networks using the NEURAL Program, which automates the very labor-intensive and therefore time-heavy and expensive process of unsupervised training.
Abstract: Foundations. Classification. Autoassociation. Time Series Prediction. Function Approximation. Multilayer Feedforward Networks. Eluding Local Minimai: Simulated Annealing. Eluding Local Minima II: Genetic Optimisation. Regression and Neural Networks. Designing Feedforward Network Architectures. Interpreting Weights: How Does This Thing Work? Probalistic Neural Networks. Functional Link Networks. Hybrid Networks. Designing the Training Set. Preparing Input Data. Fuzzy Data and Processing. Unsupervised Training. Evaluating Performance of Neural Networks. Hybrid Networks. Designing the Training Set. Preparing Input Data. Fuzzy Data and Processing. Unsupervised Training. Evaluating Performance of Neural Networks. Confidence Measures. Optimizing the Decision Threshold. Using the NEURAL Program. Appendix. Bibliography. Index.

1,671 citations


"Use of Artificial Neural Networks f..." refers background in this paper

  • ...A more detailed explanation can be found in Masters (1993), Webos (1994) and Kohonen (2001)....

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Journal ArticleDOI
TL;DR: The authors show that the absence of any clue of assortativity differentiates urban street networks from other non-geographic systems and that most of the considered networks have a broad degree distribution typical of scale-free networks and exhibit small-world properties as well.
Abstract: The application of the network approach to the urban case poses several questions in terms of how to deal with metric distances, what kind of graph representation to use, what kind of measures to investigate, how to deepen the correlation between measures of the structure of the network and measures of the dynamics on the network, what are the possible contributions from the GIS community. In this paper, the author considers six cases of urban street networks characterized by different patterns and historical roots. The authors propose a representation of the street networks based firstly on a primal graph, where intersections are turned into nodes and streets into edges. In a second step, a dual graph, where streets are nodes and intersections are edges, is constructed by means of a generalization model named Intersection Continuity Negotiation, which allows to acknowledge the continuity of streets over a plurality of edges. Finally, the authors address a comparative study of some structural properties of the dual graphs, seeking significant similarities among clusters of cases. A wide set of network analysis techniques are implemented over the dual graph: in particular the authors show that the absence of any clue of assortativity differentiates urban street networks from other non-geographic systems and that most of the considered networks have a broad degree distribution typical of scale-free networks and exhibit small-world properties as well.

726 citations


"Use of Artificial Neural Networks f..." refers background in this paper

  • ...The latter can be represented as a dual graph in which individual roads are taken as nodes and road intersections are taken as links (Porta et al., 2006)....

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