To enhance the accuracy of land use classification in mining areas, the Object-based Convolutional Neural Network (OCNN) method has been widely used. However, existing researches tend to neglect the importance of decision-level fusion, focusing only on feature-level fusion. This study proposes a new classification framework with Multi-level Fusion of Object-based analysis and CNN (MFOCNN) to achieves high-accuracy land use classification in mining areas. First, Simple Linear Iterative Cluster (SLIC) is employed to generate image objects, which serve as the basic unit for classification. Second, an improved DenseNet is proposed to extract deep features from image patches, which represent image objects, and provide the classification result. Third, handcrafted features including spectral, textural and geometric of the image objects are extracted and fused with the deep features to obtain the classification result with random forest classifier. Finally, the Dempster-Shafer (DS) evidence theory is applied to fuse the two previously described classification results at the decision-level to obtain the final result. Experiments conducted in the mining areas of Erdos using Gaofen-6 images demonstrate that the proposed MFOCNN achieves the best visual performance and accuracy among all tested methods. The MFOCNN, with its feature-level fusion and decision-level fusion, significantly improves the accuracy of land use classification in mining areas. The results suggest that the proposed MFOCNN is a promising method for achieving high-accuracy land use classification in mining areas.

Dynamic monitoring and statistics of mining land uses are crucial important for the rational development of mineral resources and regional ecological protection. Therefore, realizing efficient and accurate identification of different land uses in mining areas is one problem to be solved urgently at present. This study presents an automatic land use classification method in mining areas by combining object-based image analysis and improved DenseNet network, named ODenseNet. First, the remote sensing image is segmented into image objects based on SLIC (Simple Linear Iterative Clustering) algorithm. Second, image features are extracted for each image object, and an improved DenseNet is proposed to extract the deep features for image objects. Finally, the two types of features are concatenated and classified using a random forest classifier. The study area is located in the mining area of Ordos City, China. Gaofen-6 (GF -6) high resolution satellite image is employed in the experiment. The results reveal that the classification accuracy of the proposed method reaches 93.81 %, and the accuracy of this method is better than DenseNet and VGGNet.

Understanding how urban residents process road network information and conduct wayfinding is important for both individual travel and intelligent transportation. However, most existing research is limited to the heterogeneity of individuals' expression and perception abilities, and the results based on small samples are weakly representative. This paper proposes a quantitative and population-based evaluation method of wayfinding performance on city-scale road networks based on massive trajectory data. It can accurately compute and visualize the magnitude and spatial distribution differences of drivers' wayfinding performance levels, which is not achieved by conventional methods based on small samples. In addition, a systematic index set of road network features are constructed for correlation analysis. This is an improvement on the current research, which focuses on the influence of single factors. Finally, taking 20,000 taxi drivers in Beijing as a case study, experimental results show the following: (1) Taxi drivers' wayfinding performances show a spatial pattern of a high level on arterial road networks and a low level on secondary networks, and they are spatially autocorrelated. (2) The correlation factors of taxi drivers' wayfinding performances mainly include anchor point, road grade, road importance, road complexity, origin-destination length, and complexity, and each factor has a different influence. (3) The path complexity has a higher correlation with the wayfinding performance level than with the path distance. (4) There is a critical point in the taxi drivers' wayfinding performances in terms of path distance. When the critical value is exceeded, it is difficult for a driver to find a good route based on personal cognition. This research can provide theoretical and technical support for intelligent driving and wayfinding research.

Seeking passengers is a kind of behavior of taxi drivers with clear purposes. They always need to make decisions on where to seek the next passenger after finishing a trip. Experienced drivers are capable to capture passenger source within a short time to reduce no-load time. Most of the existing literatures focus on simulating or analyzing movement patterns of taxi drivers. This research proposes a method of analyzing spatiotemporal characteristics of taxi drivers' cognition to passenger source. Using a seven-day taxi trajectory data set collected in Beijing, an index CLPS is introduced to evaluate taxi drivers' cognitive level to passenger source. Based on this, spatiotemporal distribution of top drivers' cognition to passenger source is explored. The results of the research show that top drivers' cognition to passenger source has obvious spatiotemporal distribution features. This research is expected to provide new ways for understanding human spatial cognition.

Air passenger flow can show the spatial connection among cities. Based on the ticket data of major airports in Beijing-Tianjin-Hebei region, this paper systematically explored the temporal and spatial distribution characteristics of passenger flow in this region, drew the OD map among this region and other cities, and analyzed regional differences and connection characteristics among cities. Experimental results show that the passenger flow of airports in the Beijing-Tianjin-Hebei region is uneven in time and space. In terms of space, the regional distribution of air passenger flow is extremely unbalanced. The air transportation status in the east is extremely prominent, while the western part is relatively strong, and the central and northeast parts are relatively weak. In terms of time, the major airports in the Beijing-Tianjin-Hebei region have obvious peak and low peak periods, and the distribution of passenger flow direction in different time periods is also slightly different. The research results provide a basis for the analysis of population flow and economic relationship among urban agglomerations, and provide reasonable suggestions for traffic planning and passenger travel.