An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web

Memduhoğlu, Abdulkadir; Başaraner, Melih

doi:10.1080/15230406.2021.1952108

Cited by 11 publications

(10 citation statements)

References 47 publications

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“…Some previous studies have shown that the average position offset of OSM building data and authoritative building data is 2.5-4.5 m (Brovelli & Zamboni, 2018;Fan et al, 2014;Memduhoglu & Basaraner, 2021;Xu et al, 2017). We also counted the positional offsets of the experimental data.…”

Section: Positional Accuracymentioning

confidence: 99%

A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling

Liu

Xia

et al. 2023

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Volunteered geographic information (VGI) is an emerging phenomenon where anyone can create geographic information and share it with others. Compared with traditional authoritative geospatial data, it has several advantages, such as enriched data, instant updates, and low cost. The object matching method is widely used in VGI quality assessment and data updates. However, VGI matching faces certain challenges, such as the levels of detail that vary from object to object, the uneven distribution of data quality, and the automated matching requirement. To resolve these problems, this article proposes a new matching method that effectively combines the advantages of minimum bounding rectangle combinatorial optimization (MBRCO) and relaxation labeling. The proposed method (1) avoids setting the similarity threshold and weights and does not require training samples. This process is realized based on contextual information and optimization. (2) It overcomes the disadvantage that the MBRCO algorithm cannot distinguish adjacent buildings with similar shapes. Our approach is experimentally validated using two publicly available spatial datasets: OpenStreetMap and AutoNavi map. The experimental studies show that the proposed automatic matching method outperforms all the threshold‐based MBRCO methods and achieves high accuracy with a precision of 97.8% and a recall of 99.2%.

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Section: Positional Accuracymentioning

confidence: 99%

A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling

Liu

Xia

et al. 2023

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“…The primary input to the ANN is the shape of geospatial features. In [26], the authors considered a building footprint integration problem within a spatial-ontology framework. Since this involves conflating polygons, the authors used the overlapping area of building footprints as a similarity measure in their geometry-based matching process.…”

Section: Related Workmentioning

confidence: 99%

Harmonizing Full and Partial Matching in Geospatial Conflation: A Unified Optimization Model

Lei

2022

IJGI

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Spatial data conflation is aimed at matching and merging objects in two datasets into a more comprehensive one. Starting from the “map assignment problem” in the 1980s, optimized conflation models treat feature matching as a natural optimization problem of minimizing certain metrics, such as the total discrepancy. One complication in optimized conflation is that heterogeneous datasets can represent geographic features differently. Features can correspond to target features in the other dataset either on a one-to-one basis (forming full matches) or on a many-to-one basis (forming partial matches). Traditional models consider either full matching or partial matches exclusively. This dichotomy has several issues. Firstly, full matching models are limited and cannot capture any partial match. Secondly, partial matching models treat full matches just as partial matches, and they are more prone to admit false matches. Thirdly, existing conflation models may introduce conflicting directional matches. This paper presents a new model that captures both full and partial matches simultaneously. This allows us to impose structural constraints differently on full/partial matches and enforce the consistency between directional matches. Experimental results show that the new model outperforms conventional optimized conflation models in terms of precision (89.2%), while achieving a similar recall (93.2%).

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“…Outras estratégias também são empregadas frequentemente, como Sue et al ( 2016), que a partir de algoritmos genéticos desenvolveram um operador de deslocamento. No contexto web, a generalização cartográfica também apresenta um aspecto importante devido à prevalência de mapas em ambientes digitais que permitem operações de zoom e visão panorâmica em várias extensões e escalas (GRÖBE; BURGHARDT, 2021), envolvendo aspectos de modelagem ontológica (KULIK; DUCKHAM; GENHOFER, 2005;BILJECKI et al, 2016;MEMDUHOGLU;BASARANER, 2021), e interação com o usuário (GAFFURI, 2011). Figura 3 -Áreas de concentração que buscam generalização cartográfica (WoS).…”

Section: Elaboração: Os Autores (2022)unclassified

Generalização Cartográfica: Desafios e Perspectivas

Freiman

Santos

2022

Rev. Bras. Cartogr.

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Conceitos e procedimentos relacionados ao tema de generalização cartográfica evoluíram nas últimas décadas. Originalmente, este processo era aplicado no contexto analógico para garantir a manutenção da visualização e comunicação cartográfica de forma adequada. No entanto, com o avanço da cartografia digital o paradigma é ampliado e, então, o processo é aplicado desde a concepção do produto cartográfico, ou seja, quando se deseja reduzir uma base de dados para um mínimo necessário, mas mantendo as propriedades espaciais relevantes do conjunto. Computacionalmente, as regras de generalização são expressas por meio de algoritmos computacionais que incluem aspectos de semântica e geometria, assim como exigem, além de formulações objetivas, análises subjetivas de difícil implementação e automatização. Além disso, a inserção de novas tecnologias de aquisição e processamento de dados geoespaciais produzem novas lacunas científicas a serem superadas. Nesse aspecto, este trabalho busca, a partir de uma pesquisa bibliográfica e bibliométrica, identificar como as novas tecnologias ampliam os desafios relacionados à generalização cartografia. Observa-se que, apesar dos operadores de generalização serem considerados satisfatórios, o número de publicações na temática é crescente e apresenta interesse internacional. Ademais, as novas tecnologias como algoritmos de Inteligência Artificial, aquisição de dados geoespaciais de forma incremental e construções de representações 3D inserem complexidades teóricas e práticas que devem ser superadas. Logo, os avanços da generalização cartográfica dependem da implementação de soluções relacionadas à materialização computacional dos operadores de generalização em processos objetivos que permitam a compreensão imediata da relação geométrica e estatística formada por um conjunto de dados geoespaciais.

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An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web

Cited by 11 publications

References 47 publications

A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling

A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling

Harmonizing Full and Partial Matching in Geospatial Conflation: A Unified Optimization Model

Generalização Cartográfica: Desafios e Perspectivas

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