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QPredict: using low quality volunteered geospatial data to evaluate high quality authority data: a case study on building footprint data

QPredict: using low quality volunteered geospatial data to evaluate high quality authority data: a case study on building footprint data
QPredict: using low quality volunteered geospatial data to evaluate high quality authority data: a case study on building footprint data
High-quality, typically administrative, geospatial data should adhere to established measurement and representation practices and be protected from malicious attacks. However, this kind of geospatial data may only be infrequently updated due to its often prolonged production process compared to a data source of volunteered geographic information such as OpenStreetMap. Existing approaches typically try to quality-assure geospatial data by comparing it to another reference data set of perceived higher quality - often another administrative dataset facing a similar update cycle. In contrast, this article tries to determine whether actual changes present in volunteered geographic information data such as OpenStreetMap, which also need to be applied in an administrative dataset (i.e., consists of actual changes in the real world), can be identified automatically. To that end, we present QPredict, a machine learning approach observing changes in volunteered geospatial data such as OpenStreetMap to predict issues with a target (administrative) data set. The algorithm is trained by exploiting geospatial object characteristics, intrinsic and extrinsic quality metrics and their respective changes over time. We evaluate the effectiveness of our approach on two data sets representing two mid-size cities in Germany and discuss our findings in terms of their applicability in use cases.
Map change prediction, machine learning, spatial data quality
2374-0361
Homburg, Timo
f002f61c-7748-48a9-b1d6-0d6c327e07e1
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
Boochs, Frank
264ffa2c-57bd-4dc0-9ddc-206f2754dcec
Homburg, Timo
f002f61c-7748-48a9-b1d6-0d6c327e07e1
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
Boochs, Frank
264ffa2c-57bd-4dc0-9ddc-206f2754dcec

Homburg, Timo, Staab, Steffen and Boochs, Frank (2025) QPredict: using low quality volunteered geospatial data to evaluate high quality authority data: a case study on building footprint data. ACM Transactions on Spatial Algorithms and Systems, 11 (1), [4]. (doi:10.1145/3715910).

Record type: Article

Abstract

High-quality, typically administrative, geospatial data should adhere to established measurement and representation practices and be protected from malicious attacks. However, this kind of geospatial data may only be infrequently updated due to its often prolonged production process compared to a data source of volunteered geographic information such as OpenStreetMap. Existing approaches typically try to quality-assure geospatial data by comparing it to another reference data set of perceived higher quality - often another administrative dataset facing a similar update cycle. In contrast, this article tries to determine whether actual changes present in volunteered geographic information data such as OpenStreetMap, which also need to be applied in an administrative dataset (i.e., consists of actual changes in the real world), can be identified automatically. To that end, we present QPredict, a machine learning approach observing changes in volunteered geospatial data such as OpenStreetMap to predict issues with a target (administrative) data set. The algorithm is trained by exploiting geospatial object characteristics, intrinsic and extrinsic quality metrics and their respective changes over time. We evaluate the effectiveness of our approach on two data sets representing two mid-size cities in Germany and discuss our findings in terms of their applicability in use cases.

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Accepted/In Press date: 16 January 2025
e-pub ahead of print date: 30 January 2025
Published date: 25 February 2025
Keywords: Map change prediction, machine learning, spatial data quality

Identifiers

Local EPrints ID: 498594
URI: http://eprints.soton.ac.uk/id/eprint/498594
ISSN: 2374-0361
PURE UUID: 049e4170-e08d-4a44-bb55-3f26b939c50f
ORCID for Steffen Staab: ORCID iD orcid.org/0000-0002-0780-4154

Catalogue record

Date deposited: 21 Feb 2025 17:45
Last modified: 30 Aug 2025 01:50

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Contributors

Author: Timo Homburg
Author: Steffen Staab ORCID iD
Author: Frank Boochs

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