Air pollution is one of the key environmental problems associated with urbanization and land use. Taking Wuhan city, Central China, as a case example, we explore the quantitative relationship between land use (built-up land, water bodies, and vegetation) and air quality (SO₂, NO₂, and PM₁₀) based on nine ground-level monitoring sites from a long-term spatio-temporal perspective in 2007–2014. Five buffers with radiuses from 0.5 to 4 km are created at each site in geographical information system (GIS) and areas of land use categories within different buffers at each site are calculated. Socio-economic development, energy use, traffic emission, industrial emission, and meteorological condition are taken into consideration to control the influences of those factors on air quality. Results of bivariate correlation analysis between land use variables and annual average concentrations of air pollutants indicate that land use categories have discriminatory effects on different air pollutants, whether for the direction of correlation, the magnitude of correlation or the spatial scale effect of correlation. Stepwise linear regressions are used to quantitatively model their relationships and the results reveal that land use significantly influence air quality. Built-up land with one standard deviation growth will cause 2% increases in NO₂ concentration while vegetation will cause 5% decreases. The increases of water bodies with one standard deviation are associated with 3%–6% decreases of SO₂ or PM₁₀ concentration, which is comparable to the mitigation effect of meteorology factor such as precipitation. Land use strategies should be paid much more attention while making air pollution reduction policies.

Lampposts, traffic lights, traffic signs, utility poles and so forth are important road furniture in urban areas. The fast and accurate localization and extraction of this type of furniture is urgent for the construction and updating of infrastructure databases in cities. This paper proposes a pipeline for mobile laser scanning data processing to locate and extract road poles. The proposed method is based on the vertical continuity with isolation feature of the pole part and the overall roughness feature of the attachment part of road poles. The isolation feature of the pole part is analysed by constructing two concentric cylinders from bottom to top and there should be no or a limited number of, points between these two cylinders. After splitting up the pole part and the attachment part of a road pole, the roughness of the candidate attachment points is computed and the attachment is obtained by performing region growing method based on roughness values. By applying the proposed pipeline to different situations in two datasets, the proposed method proves to be efficient not only in simple scenes but also in cluttered scenes.

With the rapid development of 3D Digital City, the focus of research has shifted from 3D city modeling and geo-database construction to 3D geo-database service and maintenance. The frequent modifications on geometry, texture, attribute, and topology present a great challenge to the 3D geo-database updating. This article proposes an event-driven spatiotemporal database model (ESDM) that combines the historical and present 3D city models with the semantic classification and state expression, triggered by changing events predefined. In addition, a corresponding dynamic updating method based on adaptive matching algorithm is presented to perform the dynamic updating operation for the complex 3D city models automatically, according to the compound matching of semantics, attributes, and spatial locations. Finally, the validity and feasibility of the proposed ESDM and its updating method are demonstrated through a 3D geo-database with more than 1.5 million 3D city models.