Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe

Ullah, Tahira; Lautenbach, Sven; Herfort, Benjamin; Reinmuth, Marcel; Schorlemmer, Danijel

doi:10.3390/ijgi12040143

Cited by 13 publications

(3 citation statements)

References 52 publications

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“…If the data were not correctly transformed, the similarity values could be much lower (for example, due to the displacement and rotation of objects). Based on the results of the case study, we also agree with the results of previous works [22,40,44,82,83] that it is important to verify the completeness of the OSM data. Even if the data were not complete in some areas, it can be used as an additional data source in integration with other data.…”

Section: Case Studysupporting

confidence: 89%

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

Ďuračiová

2023

IJGI

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The mutual identification of spatial objects is a fundamental issue when updating geographic data with other data sets. Representations of spatial objects in different sources may not have the same identifiers, which would unambiguously assign them to each other. Intersections of spatial objects can be used for this purpose, but this does not allow for the detection of possible changes and their quantification. The aim of this paper is to propose a simple, applicable procedure for calculating the shape similarity measure, which should be able to efficiently identify different representations of spatial objects in two data sources, even though they may be changed or generalised. The main result is the aggregated index of shape similarity and instructions for its calculation and implementation. The shape similarity index is based on the calculation of the set similarity, the distance of the boundaries, and the differences in the area, perimeter, and number of the vertices of areal spatial objects. In the case study, the footprints of the building complexes in Dúbravka (part of the city of Bratislava, the capital of Slovakia) are compared using data from OpenStreetMap and INSPIRE (Infrastructure for Spatial Information in Europe) Buildings. A contribution to the quality check of the OpenStreetMap data is then a secondary result. The proposed method can be effectively used in the semi-automatic integration of heterogeneous data sources, updating the data source with other spatial data, or in their quality control.

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Section: Case Studysupporting

confidence: 89%

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

Ďuračiová

2023

IJGI

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“…The second dataset, named EUBUCCO (Milojevic-Dupont et al, 2023) As mentioned in Section 1, the quality of OSM buildings (that are also reused by EUBUCCO and DBSM) usually depends on the degree of urbanisation. Additional studies further highlight the variance of open building coverage between urban and rural areas (Gonzales, 2016;Ullah et al, 2023). For these reasons, we disaggregate the analysis described in Section 3 across urbanisation levels, using the EU Nomenclature of Territorial Units for Statistics (NUTS) as the reference framework to allow comparisons between regions with different urbanisation degrees.…”

Section: Datasetsmentioning

confidence: 99%

Pan-European open building footprints: analysis and comparison in selected countries

Minghini,

Thabit Gonzalez,

Gabrielli

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper presents a comprehensive analysis of four non-governmental open building datasets available at the European Union (EU) level, namely OpenStreetMap (OSM), EUBUCCO, Digital Building Stock Model (DBSM) and Microsoft’s Global ML Building Footprints (MS). The objective is to perform a geometrical comparison and identify similarities and differences between them, across five EU countries (Belgium, Denmark, Greece, Malta and Sweden) and various degrees of urbanisation from rural to urban. This is done in a two-step process: first, by comparing the total number and the total areas of building polygons for each dataset and country; second, by intersecting the building polygons and calculating the fraction of the area of each dataset represented by the intersection. Results highlight the influence of urbanisation on the dataset coverage (with increasing completeness when moving from rural to urban areas) and the varying degrees of overlap between the datasets based on a number of factors, including: the amount and up-to-dateness of the input sources used to produce the dataset; the presence of an active OSM community (for OSM and the datasets based on OSM); and the accuracy of Machine Learning algorithms for MS. Based on these findings, we provide insights into the strengths and limitations of each dataset and some recommendations on their use.

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“…On the downside, studies show that the distribution of OSM data is strongly uneven at a global level and that the data for many regions are still incomplete or of poor quality, especially with respect to building footprints [90,91]. Efforts have been undertaken to close gaps with respect to a more extensive attribution based on mobile apps, such as StreetComplete [92] or MapSwipe [93], the systematic data contribution for underrepresented regions within mapping events ('mapathons'), especially in the humanitarian To strategically address data scarcity in developing countries, Google released a building dataset in 2021 [99] containing 1.8 billion building footprints retrieved from satellite imagery within Africa, South Asia, South-East Asia, Latin America and the Caribbean. Each building comes with a confidence estimate to communicate the expected data quality to potential users.…”

Section: Openly Available Building Footprintsmentioning

confidence: 99%

Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities

Braun,

Warth,

Bachofer

et al. 2023

Land

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In the face of growing 21st-century urban challenges, this study emphasizes the role of remote sensing data in objectively defining urban structure types (USTs) based on morphology. While numerous UST delineation approaches exist, few are universally applicable due to data constraints or impractical class schemes. This article attempts to tackle this challenge by summarizing important approaches dealing with the computation of USTs and to condense their contributions to the field of research within a single comprehensive framework. Hereby, this framework not only serves as a conjunctive reference for currently existing implementations, but is also independent regarding the input data, spatial scale, or targeted purpose of the mapping. It consists of four major steps: (1) the collection of suitable data sources to describe the building morphology as a key input, (2) the definition of a spatial mapping unit, (3) the parameterization of the mapping units, and (4) the final classification of the mapping units into urban structure types. We outline how these tasks can lead to a UST classification which fits the users’ needs based on their available input data. At the same time, the framework can serve as a protocol for future studies where USTs are mapped, or new approaches are presented. This article closes with an application example for three different cities to underline the flexibility and applicability of the proposed framework while maintaining maximized objectivity and comparability. We recommend this framework as a guideline for the use-specific mapping of USTs and hope to contribute to past and future research on this topic by fostering the implementation of this concept for the spatial analysis and a better understanding of complex urban environments.

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Assessing Completeness of OpenStreetMap Building Footprints Using MapSwipe

Cited by 13 publications

References 52 publications

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

An Aggregated Shape Similarity Index: A Case Study of Comparing the Footprints of OpenStreetMap and INSPIRE Buildings

Pan-European open building footprints: analysis and comparison in selected countries

Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities

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