An evaluation of data completeness of VGI through geometric similarity assessment

Chehreghan, Alireza; Abbaspour, Rahim Ali

doi:10.1080/19479832.2018.1504825

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…Most works found that VGI quality is very heterogeneous, mostly affected by spatial incompleteness or sparseness, thus the importance of evaluating completeness [16,22], with greatest timeliness than official geospatial data in some localities [14]. For example, quality assessment of VGI within the OpenStreetMap (OSM) project, has been carried out in distinct localities and the results outline that urban areas can reach both higher positional accuracy and greater completeness than the authoritative datasets with respect to rural places [23].…”

Section: Methodsmentioning

confidence: 99%

A Semantic Approach for Quality Assurance and Assessment of Volunteered Geographic Information

Bordogna¹

2021

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The paper analyses the characteristics of Volunteer Geographic Information (VGI) and the need to assure and assess its quality for a possible use and re-use. Ontologies and soft ontologies are presented as means to support quality assurance and assessment of VGI by highlighting their limitations. A proposal of a possibilistic approach using fuzzy ontology is finally illustrated that allows to model both imprecision and vagueness of domain knowledge and epistemic uncertainty affecting observations. A case study example is illustrated.

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Section: Methodsmentioning

confidence: 99%

A Semantic Approach for Quality Assurance and Assessment of Volunteered Geographic Information

Bordogna¹

2021

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“…Regional studies on the completeness of buildings in the last years report a coverage of 25% in regions of Germany in 2011 and 57% in regions of Italy in 2017 (Brovelli & Zamboni, 2018; Hecht et al, 2013). In general, the level of completeness of roads and buildings varies strongly from region to region (Barrington‐Leigh & Millard‐Ball, 2017; Brovelli & Zamboni, 2018; Hecht et al, 2013), but was observed to increase over the last years (Chehreghan & Ali Abbaspour, 2018). Hence, the accuracy and completeness of the data sets should be considered and checked before applications in flood risk estimations to avoid underestimations of exposed objects.…”

Section: Exposure Data Setsmentioning

confidence: 99%

“…One limitation of the newly available data sets is the accuracy of the objects (e.g., building footprints or roads) as well as their completeness. The local geometric accuracy of roads can be over 90% compared with reference data sets (Chehreghan & Ali Abbaspour, 2018), while the completeness of roads is over 80% globally (Barrington‐Leigh & Millard‐Ball, 2017). Building footprints showed high similarities to authority data, although with a lower level of detail (Fan et al, 2014).…”

Section: Exposure Data Setsmentioning

confidence: 99%

Toward an adequate level of detail in flood risk assessments

Sieg

Kienzler

Rözer

et al. 2023

J Flood Risk Management

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Flood risk assessments require different disciplines to understand and model the underlying components hazard, exposure, and vulnerability. Many methods and data sets have been refined considerably to cover more details of spatial, temporal, or process information. We compile case studies indicating that refined methods and data have a considerable effect on the overall assessment of flood risk. But are these improvements worth the effort? The adequate level of detail is typically unknown and prioritization of improvements in a specific component is hampered by the lack of an overarching view on flood risk. Consequently, creating the dilemma of potentially being too greedy or too wasteful with the resources available for a risk assessment. A “sweet spot” between those two would use methods and data sets that cover all relevant known processes without using resources inefficiently. We provide three key questions as a qualitative guidance toward this “sweet spot.” For quantitative decision support, more overarching case studies in various contexts are needed to reveal the sensitivity of the overall flood risk to individual components. This could also support the anticipation of unforeseen events like the flood event in Germany and Belgium in 2021 and increase the reliability of flood risk assessments.

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“…Hyperspectral images (HSIs) contain rich spatial and spectral information [1,2] and are widely applied in the fields of precision agriculture [3], urban planning [4], national defense construction [5], and mineral exploitation [6], among other fields. It has been very successful in allowing active users to participate in collecting, updating, and sharing the massive amounts of data that reflect human activities and social attributes [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Mixed Structure with 3D Multi-Shortcut-Link Networks for Hyperspectral Image Classification

et al. 2022

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A hyperspectral image classification method based on a mixed structure with a 3D multi-shortcut-link network (MSLN) was proposed for the features of few labeled samples, excess noise, and heterogeneous homogeneity of features in hyperspectral images. First, the spatial–spectral joint features of hyperspectral cube data were extracted through 3D convolution operation; then, the deep network was constructed and the 3D MSLN mixed structure was used to fuse shallow representational features and deep abstract features, while the hybrid activation function was utilized to ensure the integrity of nonlinear data. Finally, the global self-adaptive average pooling and L-softmax classifier were introduced to implement the terrain classification of hyperspectral images. The mixed structure proposed in this study could extract multi-channel features with a vast receptive field and reduce the continuous decay of shallow features while improving the utilization of representational features and enhancing the expressiveness of the deep network. The use of the dropout mechanism and L-softmax classifier endowed the learned features with a better generalization property and intraclass cohesion and interclass separation properties. Through experimental comparative analysis of six groups of datasets, the results showed that this method, compared with the existing deep-learning-based hyperspectral image classification methods, could satisfactorily address the issues of degeneration of the deep network and “the same object with distinct spectra, and distinct objects with the same spectrum.” It could also effectively improve the terrain classification accuracy of hyperspectral images, as evinced by the overall classification accuracies of all classes of terrain objects in the six groups of datasets: 97.698%, 98.851%, 99.54%, 97.961%, 97.698%, and 99.138%.

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An evaluation of data completeness of VGI through geometric similarity assessment

Cited by 7 publications

References 28 publications

A Semantic Approach for Quality Assurance and Assessment of Volunteered Geographic Information

A Semantic Approach for Quality Assurance and Assessment of Volunteered Geographic Information

Toward an adequate level of detail in flood risk assessments

Mixed Structure with 3D Multi-Shortcut-Link Networks for Hyperspectral Image Classification

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