The availability of very high spatial resolution (VHR) remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC) changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees) classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km² to ~3.60 km² during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake’s shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

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.

A policy of integrating China’s intangible cultural heritage into its tourism industry has been proposed by the Ministry of Culture and Tourism. Intangible cultural heritage assets offer unique opportunities in tourism development, yet during the integration process, residents’ perspectives are often overlooked. From the perspective of social exchange, this study examines the relationships among residents’ perceptions of, and their behaviors toward, local intangible cultural heritage and tourism. Survey data were collected from residents (N = 440) in Dali Bai Autonomous Prefecture, China. This study showed that residents’ positive perceptions of tourism impacts have a significant effect on residents’ supportive behaviors. Residents’ place attachment indirectly influences their behaviors. The results suggest that intergroup contact strengthens the relationship between residents’ perceptions of the positive impacts of tourism and behavior but weakens the relationship between place attachment and behavior. The findings provide a theoretical basis for exploring intangible cultural heritage tourism from the perspectives of residents, as well as offering insights for local authorities to use to reduce the negative impacts of tourism on residents and balance tourism development with residents’ normal life.

Immovable cultural heritages are nonrenewable and irreplaceable excellent cultural resources. With the help of the GIS spatial analysis method, Geodetector and location entropy, this paper explores the spatial and temporal distribution characteristics and influencing factors of 1070 nationally and provincially designated immovable cultural heritage sites in Hubei, China. The physical geographical factors, historical and humanistic factors, and their explanatory power indexes are constructed, which provide a scientific quantitative analysis of influencing factors for the spatial distribution of cultural heritages. The results show that the kernel density distribution of immovable cultural heritages in Hubei Province is agglomeration, and Wuhan is the dominant nuclear. The number of cultural heritages in different historical periods is unevenly distributed. The average center of cultural heritages in each period was located in Jingshan. The spatial differentiation of these cultural heritages is affected by multiple factors. Among natural factors, topographic and geomorphic factors such as elevation and slope have a more significant effect, whereas among social and economic factors, the cultural atmosphere and the number of permanent residents have the most significant explanatory power. These findings may help local governments formulate more effective measures for the protection of immovable cultural heritages.

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.