Urban land use and transportation are closely associated. Previous studies have investigated the spatial interrelationship between street centralities and land use intensities using land cover data, thus neglecting the social functions of urban land. Taking the city of Shenzhen, China, as a case study, we used reclassified points of interest (POI) data to represent commercial, public service, and residential land, and then investigated the varying interrelationships between the street centralities and different types of urban land use intensities. We calculated three global centralities (“closeness”, “betweenness”, and “straightness”) as well as local centralities (1-km, 2-km, 3-km, and 5-km searching radiuses), which were transformed into raster frameworks using kernel density estimation (KDE) for correlation analysis. Global closeness and straightness are high in the urban core area, and roads with high global betweenness outline the skeleton of the street network. The spatial patterns of the local centralities are distinguished from the global centralities, reflecting local location advantages. High intensities of commercial and public service land are concentrated in the urban core, while residential land is relatively scattered. The bivariate correlation analysis implies that commercial and public service land are more dependent on centralities than residential land. Closeness and straightness have stronger abilities in measuring the location advantages than betweenness. The centralities and intensities are more positively correlated on a larger scale (census block). These findings of the spatial patterns and interrelationships of the centralities and intensities have major implications for urban land use and transportation planning.

At present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the coord-dense-global (CDG) model, built on three parts: a coordconv module by putting coordinate information into feature maps aimed at reducing the loss of spatial information and strengthening road boundaries, an improved dense convolutional network (DenseNet) that could make full use of multiple features through own dense blocks, and a global attention module designed to highlight high-level information and improve category classification by using pooling operation to introduce global information. When tested on a complex road dataset from Massachusetts, USA, CDG achieved clearly superior performance to contemporary networks such as DeepLabV3+, U-net, and D-LinkNet. For example, its mean IoU (intersection of the prediction and ground truth regions over their union) and mean F1 score (evaluation metric for the harmonic mean of the precision and recall metrics) were 61.90% and 76.10%, respectively, which were 1.19% and 0.95% higher than the results of D-LinkNet (the winner of a road-extraction contest). In addition, CDG was also superior to the other three models in solving the problem of tree occlusion. Finally, in universality research with the Gaofen-2 satellite dataset, the CDG model also performed well at extracting the road network in the test maps of Hefei and Tianjin, China.

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Urban underground pipelines are known as "urban blood vessels". To detect changes in integrated pipelines and professional pipelines, the matching of same-name spatial objects is critical. Existing algorithms used for vector network matching were analyzed to develop an improved matching algorithm that can adapt to underground pipeline networks. Our algorithm improves the holistic matching of pipeline strokes, and also a partial matching algorithm is provided. In this study, appropriate geometric measures were selected to calculate the geometric similarity between pipeline strokes in their holistic matching. Existing methods for evaluating similarities in spatial scene structures in partial underground pipeline networks were improved. A method of partial matching of strokes was additionally investigated, and it compensates for the deficiencies of holistic stroke matching. Experiments showed that the matching performance was good, and the operation efficiency was high. geometric connection rules [5,12]. This principle was combined with the characteristics of road and water network spatial data to propose the stroke concept [12,13]. From a local indicator, two road segments can be connected on the condition that their deflection angle is less than a specified threshold. The deflection angle, which ranges from 0 • to 180 • represents the degree of deviation formed by two linked road segments [14], as shown in Figure 1. Figure 1a shows the pipeline spatial data organized in the traditional "node-segment" manner, and Figure 1b shows the data processed by stroke connection. There are four strokes: stroke1, stroke2, stroke3, and stroke4. "Good continuation" usually refers to an approximately level angle (180 • ) between segments. The geometric morphology of a stroke is characterized by a smooth and continuous distribution in space, which maintains the integrity of urban underground pipelines. Therefore, the stroke can be used as the basic unit for matching and analyzing urban underground pipelines.

The combination of synthetic aperture radar (SAR) with unmanned aerial vehicles (UAVs) is attractive for acquiring high-resolution images without the restrictions of time and weather. However, because UAVs are lightweight and small in size, the UAV SAR system is very sensitive to turbulence, resulting in serious trajectory deviations and remaining residual motion errors (RMEs), such as residual range cell migration (RCM) and aperture phase errors (APEs). In this paper, a novel motion compensation (MoCo) strategy is developed based on the symmetric triangle linear frequency modulated continuous wave (STLFMCW) signal. The STLFMCW signal model is first presented, which reveals the relevance between the positive-negative frequency modulations and motion errors. The residual RCMs and relative phase errors are estimated directly through the phase differential interferometry of up-ramp and down-ramp chirp signals, instead of the traditional approach of relying on the range-shift gradient of adjacent range profiles. The proposed approach is independent of conventional prominent point processing (PPP) and calculates the range deviation to avoid the accumulation of errors, thereby achieving high precision and efficiency. Experiments based on simulated and measured data sets validate the proposed approach.