Inferring semantics from geometry

Corcoran, Padraig; Jilani, Musfira; Mooney, Peter; Bertolotto, Michela

doi:10.1145/2820783.2820822

Cited by 14 publications

(9 citation statements)

References 29 publications

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“…However, they test their model on a small dataset in Sweden and do not validate their model against reference data. Similarly, machine learning has been used to learn the road class in OSM networks in a series of studies [7,18,19]. The authors first develop a representation of the street network, which combines primal and dual graphs, called multi-granular street network representation [18].…”

Section: Detection Of Classification Errors In Osmmentioning

confidence: 99%

Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases

Guth

Hinz

Winter

2021

JOSIS

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OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors often lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classification errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by searching for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are identified that indicate gaps in road networks. These parameters are then combined in a rating system to obtain an error probability to suggest possible misclassifications to a human user. The methodology is applied to an exemplar case for the state of New South Wales in Australia. The results demonstrate that (1) more classification errors are found at gaps than at disconnected parts, and (2) the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. In future work, the methodology can be extended to include available tags in OSM for the rating system. The source code of the implementation is available via GitHub.

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Section: Detection Of Classification Errors In Osmmentioning

confidence: 99%

Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases

Guth

Hinz

Winter

2021

JOSIS

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“…Skoumas et al (2014) proposed a method that adds a popularity score to each road by analyzing blog articles about travel experiences. Corcoran et al (2015) estimate the road type such as a motorway or residential street from the road network data. Galbrun et al (2014) proposed a method that adds a safety score to roads by using kernel density estimation on open data about crime statistics.…”

Section: Related Workmentioning

confidence: 99%

“…7. For summarizing the social media posts for each POI, we applied mean-shift clustering (Comaniciu and Meer 2002) to each geo-tagged post and assigned each cluster to a neighboring POI with our POI database, as in the previous study Crandall et al (2009). Next, we obtained a bag of words for each POI by applying MeCab (Kudo et al 2004), a Japanese morphological analyzer with mecab-ipadic-NEologd, a system dictionary (Toshinori 2015).…”

Section: Data Collectionmentioning

confidence: 99%

Social media knows what road it is: quantifying road characteristics with geo-tagged posts

Nishimura

Nishida

Toda

et al. 2017

Soc. Netw. Anal. Min.

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Determining traveling routes that provide opportunities to satisfy the various requirements of users in urban areas is still an open problem. This is because it is virtually impossible to manually quantify the characteristics of each street or road and there are few web-based or semantic resources of subjective requirements that describe streets and roads directly. Thus, it is difficult to satisfy all the needs and desires of users that may arise, such as for finding boulevards that are ''fashionable'' or ''beautiful.'' The goal of this study is to automatically quantify the characteristics of streets and roads in relation to requirements that can be described using keywords such as ''fashionable.'' To achieve this goal, we propose a twostage method that analyzes social media and road networks. First, in estimating the topic distribution (i.e., the characteristics) of each point of interest (POI), our method uses the latent Dirichlet allocation model to analyze geo-tagged texts while considering which users posted useful information for estimating road characteristics. Next, it uses a Markov random field model to estimate the characteristics of each street or road on the basis of those of the POIs and the road networks associated with the POIs. Experiments on real datasets demonstrate that our method achieves statistically significant improvements over baseline methods in terms of ranking quality in information retrieval for 300 roads in three urban areas in Tokyo when given 25 keywords.

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“…For instance, multiple metrics have been adopted to explain their structural conditions [3], their intense traffic of vehicles [4], and the emergence of collective behavior [5]. In other studies, the authors centered on the geometrical perspective of the network [6], and on the elements positioning [7,8]. Furthermore, there are those who reviewed the role of the city elements [9,10], that addressed the support to the urban planning and design [11,12], and that improved the facility-location analysis and planning of street meshes [13].…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Distance-Based Tool-Set to Track Inconsistent Urban Structures Through Complex-Networks

Spadon

Machado

Eler

et al. 2018

Lecture Notes in Computer Science

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Complex networks can be used for modeling street meshes and urban agglomerates. With such a model, many aspects of a city can be investigated to promote a better quality of life to its citizens. Along these lines, this paper proposes a set of distance-based pattern-discovery algorithmic instruments to improve urban structures modeled as complex networks, detecting nodes that lack access from/to points of interest in a given city. Furthermore, we introduce a greedy algorithm that is able to recommend improvements to the structure of a city by suggesting where points of interest are to be placed. We contribute to a thorough process to deal with complex networks, including mathematical modeling and algorithmic innovation. The set of our contributions introduces a systematic manner to treat a recurrent problem of broad interest in cities.

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Inferring semantics from geometry

Cited by 14 publications

References 29 publications

Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases

Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases

Social media knows what road it is: quantifying road characteristics with geo-tagged posts

A Distance-Based Tool-Set to Track Inconsistent Urban Structures Through Complex-Networks

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