An integrated framework for linear pattern extraction in the building group generalization process

Pilehforooshha, Parastoo; Karimi, Mohammad

doi:10.1080/10106049.2018.1458253

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…Then, the linear patterns are extracted for each of the detected groups. To this end, linear patterns (straight and perpendicular patterns) in each group are extracted using the procedure proposed in Pilehforooshha and Karimi (2019).…”

Section: Results and Evaluationsmentioning

confidence: 99%

“…Some others include pre‐processing steps in building generalization. Examples involve algorithmic methods for building grouping (Pilehforooshha & Karimi, 2020) and rule‐based (Pilehforooshha & Karimi, 2019), agent‐based (Renard & Duchêne, 2014), or machine learning (Yan, Ai, Yang, & Yin, 2019) approaches for building pattern extraction. However, focusing on the overall process or the pre‐processing steps, which has been studied extensively, is beyond the scope of the present study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The generalization of building blocks (we use the term “buildings” in the remainder of the article for the sake of simplicity) has attracted much research attention over recent decades (e.g., Ai, Zhang, Zhou, & Yang, 2015; Basaraner & Selcuk, 2008; Deng, Tang, Liu, & Wu, 2018; Pilehforooshha & Karimi, 2019). In building generalization, applying pre‐processing steps and geometric transformation can lead to spatial conflicts of objects, so that they overlap or the distance between them (between buildings or between buildings and other map objects such as roads) becomes shorter than the minimum identifiable separation distance (Ai et al., 2015; Liu, Guo, Sun, & Ma, 2014; Mackaness, 1994; Sun, Guo, Liu, Lv, & Yang, 2016; Wei, He, Wang, Wang, & Guo, 2018).…”

Section: Introductionmentioning

confidence: 99%

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A new model combining building block displacement and building block area reduction for resolving spatial conflicts

Pilehforooshha

Karimi

Mansourian

2021

Transactions in GIS

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Applying pre‐processing and geometric transformation, for the generalization of building blocks, can lead to spatial conflicts which are mainly resolved by displacement. However, the conflicts may not be resolved, due to the insufficient space for displacement or the existence of other objects that prevent displacement. This article proposes a novel model combining building block displacement and building block area reduction for resolving spatial conflicts. In this model, first the immune genetic algorithm with improved objective function, with the goal of minimizing the total conflicting area, is used for displacement. Second, a building block area reduction model is applied to reduce the area of the unresolved conflicting building blocks. For this, a building segment partitioning model is developed for partitioning the boundaries of conflicting building blocks. Then, the conflicting segments are locally simplified for more adaptation to the initial generalized ones. We generalized two datasets from scale 1:25 to 1:50k and then used the datasets with scale 1:50k to assess the proposed model. The results demonstrate that the proposed model has improved the correctness and completeness by 3.62 and 4.24% for dataset 1 and 5.67 and 7.92% for dataset 2, respectively, compared to one of the recent models for resolving conflicts.

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Section: Results and Evaluationsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new model combining building block displacement and building block area reduction for resolving spatial conflicts

Pilehforooshha

Karimi

Mansourian

2021

Transactions in GIS

Self Cite

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“…The multicriteria method Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) used in this step was proposed by Tzeng and Huang [125] and has been used in several works; it is one of the most widely used multicriteria methods [126], and it stands out for looking for the alternative that is furthest from the negative ideal solution and closer to the positive ideal solution. In this study, the TOPSIS method was structured as a mathematical instrument for measuring the MMSC smartness in the health area [127,128], environment and sustainability [129][130][131][132][133][134][135][136][137][138], technology [139][140][141][142][143][144], and urban spaces and developments [145][146][147][148][149], among many others in the engineering fields [150][151][152][153][154][155][156][157][158][159][160][161][162].…”

Section: Model Methodologymentioning

confidence: 99%

Smart Cities Maturity Model—A Multicriteria Approach

et al. 2023

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The concept of smart cities has gained relevance over the past few years. Public managers have been planning investments to turn their cities into smart cities. Maturity models can help managers to monitor the performance of urban indicators; however, these maturity models are not always capable of meeting their proposed goals. In this sense, this research aims to develop a maturity model that ranks the “smartness” of a city based on social and technological indicators. The Smart Cities Maturity Model (MMSC) variables were extracted from ISO’s 37153:2017, 37120:2018, 37107:2019. The MMSC is structured on a hybrid TOPSIS multicriteria decision-making method. In this paper, we modified TOPSIS and used it to generate a synthetic indicator, called smart index, that designates the level of maturity of a real city. For this change to be possible, we fixed some alternatives and changed the positive ideal and negative ideal solution. The methodology is proven to be very efficient in measuring the smart city maturity level, and it can be easily adapted for the upcoming ISOs.

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“…Liqiang et al [LHDZ13] successfully formulated the aggregation of geometric features based on Gestalt clustering rules. Pilehforooshha and Karimi [PK19] developed a framework for extracting building linear patterns to define aggregation rules based on their similarity.…”

Section: Aggregation As Cartographic Generalizationmentioning

confidence: 99%

Interactive Optimization for Cartographic Aggregation of Building Features

Takahashi,

Kokubun,

Nishimura

et al. 2024

Computer Graphics Forum

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Aggregation, as an operation of cartographic generalization, provides an effective means of abstracting the configuration of building features by combining them according to the scale reduction of the 2D map. Automating this design process effectively helps professional cartographers design both paper and digital maps, but finding the best aggregation result from the numerous combinations of building features has been a challenge. This paper presents a novel approach to assist cartographers in interactively designing the aggregation of building features in scale‐aware map visualization. Our contribution is to provide an appropriate set of candidates for the cartographer to choose from among a limited number of possible combinations of building features. This is achieved by collecting locally optimal solutions that emerge in the course of aggregation operations, formulated as a label cost optimization problem. Users can also explore better aggregation results by interactively adjusting the design parameters to update the set of possible combinations, along with an operator to force the combination of manually selected building features. Each cluster of aggregated building features is tightly enclosed by a concave hull, which is later adaptively simplified to abstract its boundary shapes. Experimental design examples and evaluations by expert cartographers demonstrate the feasibility of the proposed approach to interactive aggregation.

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An integrated framework for linear pattern extraction in the building group generalization process

Cited by 12 publications

References 29 publications

A new model combining building block displacement and building block area reduction for resolving spatial conflicts

A new model combining building block displacement and building block area reduction for resolving spatial conflicts

Smart Cities Maturity Model—A Multicriteria Approach

Interactive Optimization for Cartographic Aggregation of Building Features

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