Geometric-semantic coherent building models are demanding in many geoscience applications. Conventional building modeling methods often rely on successive roof plane segmentation and fitting. The subsequent reconstruction procedure is difficult to assure topologic consistency and geometric accuracy. This paper starts with a library of predefined building models or primitives, including pyramid, gable, hip, etc. We propose an optimal model fitting approach that holistically determines all of its parameters from segmented point cloud data. The approach is formulated as an optimization problem that minimizes the point-to-mesh distance between the point cloud and the meshed primitive model. Necessary constraints in the form of inequality equations are introduced to assure correct and reliable solution. For complex roofs consisting of several predefined primitive models, a hierarchical procedure is presented to reconstruct the major roof model and its superstructures sequentially. The CityGML LoD2 model is created from the parameterized primitives. The quality and performance of this approach are evaluated with airborne lidar and photogrammetric point clouds. Based on the experiments with 910 buildings, the primitive fitting accuracy is 7.8cm and the corner uncertainty is 0.36m or 0.78 times the ground point spacing; the building boundary consistency is 89.6%. The study demonstrates a piecewise continuous polyhedral building model can be determined through a holistic parameter optimization process. The resultant building models intrinsically best fit to the input point cloud with topologic integrity. The approach not only qualitatively generates semantic building models, but also exhibits the potential for building reconstruction over large areas.
Millions of geo-tagged photos are becoming available due to the wide spread of photo-sharing websites, which provide valuable information to mine spatial patterns from human activities. In this study, we present a simple and fast density-based spatial clustering algorithm to detect popular scenic spots using geo-tagged photos collected from Flickr. In this algorithm, Gaussian kernel is applied to estimate local density of data points, and a decision graph is used to obtain cluster centers easily. More than 289,000 geo-tagged photos located in five typical cities of China are downloaded as case studies, and data pre-processing such as duplicate removing is performed to improve the quality of clustering result. Finally, popular tourist attractions of each sample city are successfully detected with this algorithm, and our result is useful for recommending some interesting destinations which might not be on the list of tourist website or mobile guide applications. The proposed solution is robust with respect to different distributions of photos, and it is efficient by comparing with other popular clustering approaches.
Background:
Uric acid (UA) has both antioxidative and pro-oxidative properties. The
study aimed to investigate the relationship between serum UA and hemorrhagic transformation (HT)
after intravenous thrombolysis in patients with acute ischemic stroke
Methods:
The patients undergoing intravenous thrombolysis from two hospitals in China were retrospectively analyzed. HT was evaluated using computed tomography images reviewed within 24-
36h after thrombolysis. Symptomatic intracranial hemorrhage (sICH) was defined as HT accompanied by worsening neurological function. Multivariate logistic regression and spline regression
models were performed to explore the relationship between serum UA levels and the risk of HT and
sICH.
Results:
Among 503 included patients, 60 (11.9%) were diagnosed with HT and 22 (4.4%) developed sICH. Patients with HT had significant lower serum UA levels than those without HT (245
[214-325 vs. 312 [256-370] µmol/L, p < 0.001). Multivariable logistic regression analysis indicated
that patients with higher serum UA levels had a lower risk of HT (OR per 10-µmol/L increase 0.96,
95%CI 0.92–0.99, p = 0.015). Furthermore, multiple-adjusted spline regression models showed a Ushaped association between serum UA levels and HT (p < 0.001 for non-linearity). Similar results
were present between serum UA and sICH. Restricted cubic spline models predicted the lowest risk
of HT and sICH when the serum UA levels were 386µmol/L.
Conclusion:
The data show the U-shaped relationship between serum UA levels and the risk of HT
and sICH after intravenous thrombolysis.
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