Modelling the spatial evolution of map objects by map agents

Ying, Shen; Wen, Wan; Yuan, Weiqi; Duan, Xinqiao

doi:10.1080/10106049.2015.1054440

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In some sense, this process is a complicated decision‐making task. Ying, Wen, Wan, and Duan () have used map agents to model the changes made to objects to enable suitable updating behaviors. Zhou and Li () 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, larger‐scale dataset.…”

Section: Introductionmentioning

confidence: 99%

A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales

Yang

Yan

et al. 2018

Transactions in GIS

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The availability of geospatial data has increased significantly over recent decades. As a result, the question of how to update spatial data across different scales has become an attractive topic. One promising strategy is to use an updated larger-scale dataset as a reference for detecting and updating changed objects represented in a tobe-updated smaller-scale dataset. For such an update method, an understanding of the different types of changes that can occur is crucial. Using polygonal building data as an example, 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. Then, we apply map algebra theory to establish a cartographic model for updating polygonal building data.Supported by concepts of map algebra, an update procedure involving change detection, filtering, and fusion is implemented through a series of set operations. In addition to traditional polygon overlay functions, the constrained Delaunay triangulation model and knowledge of map generalization procedures are employed to construct set operations.The proposed method has been validated through tests using realworld data. The experimental results show that our method is effective for updating 1:10k map data using 1:2k map data.

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Section: Introductionmentioning

confidence: 99%

A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales

Yang

Yan

et al. 2018

Transactions in GIS

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“…However, comprehensive updating that uses a large-scale data generalization method to produce small-scale data requires a great amount of work, and ensuring the consistency of multi-scale data is difficult. Multi-scale feature-cascade updating is a popular method for quickly updating spatial data in academic research [3][4][5]. This method uses incremental information from large-scale data to update small-scale data, and updates only the changed features.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Residential Areas Matching Method Using Relevance Vector Machine and Active Learning

Zhang

Luo

et al. 2017

IJGI

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Multi-scale object matching is the key technology for upgrading feature cascade and integrating multi-source spatial data. Considering the distinctiveness of data at different scales, the present study selects residential areas in a multi-scale database as research objects and focuses on characteristic similarities. This study adopts the method of merging with no simplification, clarifies all the matching pairs that lack one-to-one relationships and places them into one-to-one matching pairs, and conducts similarity measurements on five characteristics (i.e., position, area, shape, orientation, and surroundings). The relevance vector machine (RVM) algorithm is introduced, and the method of RVM-based spatial entity matching is designed, thus avoiding the needs of weighing feature similarity and selecting matching thresholds. Moreover, the study utilizes the active learning approach to select the most effective sample for classification, which reduces the manual work of labeling samples. By means of 1:5000 and 1:25,000 residential areas matching experiments, it is shown that the RVM method could achieve high matching precision, which can be used to accurately recognize 1:1, 1:m, and m:n matching relations, thus improving automation and the intelligence level of geographical spatial data management.

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The use of cartographic control points in the harmonization and revision of MRDBs

Chrobak

Lupa

Szombara

et al. 2017

Geocarto International

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Modelling the spatial evolution of map objects by map agents

Cited by 3 publications

References 13 publications

A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales

A map‐algebra‐based method for automatic change detection and spatial data updating across multiple scales

A Multi-Scale Residential Areas Matching Method Using Relevance Vector Machine and Active Learning

The use of cartographic control points in the harmonization and revision of MRDBs

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