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- Höhle, Michael, 1975-, et al.
(författare)
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Generation and Assessment of Urban Land Cover Maps Using High-Resolution Multispectral Aerial Cameras
- 2013
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Ingår i: International Journal on Advances in Software. - 1942-2628. ; 6:3-4, s. 272-282
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Tidskriftsartikel (refereegranskat)abstract
- New aerial cameras and new advanced geoprocessingtools improve the generation of urban land covermaps. Elevations can be derived from stereo pairs with highdensity, positional accuracy, and efficiency. The combinationof multispectral high-resolution imagery and high-densityelevations enable a unique method for the automaticgeneration of urban land cover maps. In the present paper,imagery of a new medium-format aerial camera and advancedgeoprocessing software are applied to derive normalizeddigital surface models and vegetation maps. These twointermediate products then become input to a tree structuredclassifier, which automatically derives land cover maps in 2Dor 3D. We investigate the thematic accuracy of the producedland cover map by a class-wise stratified design and provide amethod for deriving necessary sample sizes. Correspondingsurvey adjusted accuracy measures and their associatedconfidence intervals are used to adequately reflect uncertaintyin the assessment based on the chosen sample size. Proof ofconcept for the method is given for an urban area inSwitzerland. Here, the produced land cover map with sixclasses (building, wall and carport, road and parking lot, hedgeand bush, grass) has an overall accuracy of 86% (95%confidence interval: 83-88%) and a kappa coefficient of 0.82(95% confidence interval: 0.78-0.85). The classification ofbuildings is correct with 99% and of road and parking lot with95%. To possibly improve the classification further,classification tree learning based on recursive partitioning isinvestigated. We conclude that the open source software “R”provides all the tools needed for performing statistical prudentclassification and accuracy evaluations of urban land covermaps.
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| 2. |
- Fenske, Nora, et al.
(författare)
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Boosting Structured Additive Quantile Regression for Longitudinal Childhood Obesity Data
- 2013
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Ingår i: The International Journal of Biostatistics. - : Walter de Gruyter GmbH. - 1557-4679 .- 2194-573X. ; 9:1, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.
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