A feature-based approach to conflation of geospatial sources

Samal, Ashok; Seth, Sharad; Cueto, Kevin

doi:10.1080/13658810410001658076

Cited by 138 publications

(98 citation statements)

References 14 publications

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“…2 for scales 1:5000 and 1:10,000. The general approach is similar to the one used by Samal et al (2004) to test pairs of polygons for shape similarity.…”

Section: Quality Assessmentmentioning

confidence: 99%

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Freire

Santos

Navarro

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

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a b s t r a c tMany municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination, counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore, the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone. The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1:1000, 1:5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1:10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1:10,000, while no buildings were compliant with the specification for scale 1:1000. Ó

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“…2 for scales 1:5000 and 1:10,000. The general approach is similar to the one used by Samal et al (2004) to test pairs of polygons for shape similarity.…”

Section: Quality Assessmentmentioning

confidence: 99%

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Freire

Santos

Navarro

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

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“…A lot of advanced matching methods for road networks have been proposed based on geometric, structure and sematic criterions (Cobb et al 1998, Xiong and Sperling 2004, Samal et al 2004, Volz 2006, Mustiè re and Devogele 2007, Zhang 2009). On the other hand, many studies integrated information theory or approaches of graph matching to update the matching criterions iteratively for global optimization.…”

Section: Introductionmentioning

confidence: 99%

Automated Matching Crowdsourcing Road Networks Using Probabilistic Relaxation

Zhang

Wang

Luan

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Geospatial data matching is an important prerequisite for data integration, change detection, and data updating. Presently, crowdsourcing geospatial data is drawing great attention with its significant potential for geospatial data updating and Location Based Services, etc. To explore the availabilities of crowdsourcing geospatial data, the paper proposes a heuristic probabilistic relaxation road matching method, named PRRM. It starts with an initial probabilistic matrix according to geometric dissimilarities and then integrates the relative compatibility coefficient of neighbouring candidate pairs to update the previous matrix. Finally, the initial 1:1 matching pairs are selected based on probabilities calculated and refined based on the structure similarity of the selected matching pairs, then a matching growing process is implemented to find M: N matching pairs. Two experiments between OpenStreetMap and professional data show that our method achieves good performance in matching crowdsourcing and professional data with non-rigid deviations and inconsistent structures. Moreover, the proposed method is independent on matching direction and could handle 1: 0 (Null), 1: M and M: N matching. * zyfwhu@whu.edu.cn

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“…The most challenging task in data conflation is feature matching, which relies on a similarity measurement to decide the correspondence between two features. Various similarity metrics have been developed, based on geometry, attribute and topology [22,24,25]. After a similarity measurement is defined, most methods use a greedy strategy to find matched pairs of geographic features in a sequential manner.…”

Section: Literaturementioning

confidence: 99%

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Goodchild

et al. 2013

IJGI

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Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.

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A feature-based approach to conflation of geospatial sources

Cited by 138 publications

References 14 publications

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Automated Matching Crowdsourcing Road Networks Using Probabilistic Relaxation

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

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