While surface deformations and their impact on buildings have been observed in the city of Lanzhou, it is difficult to find studies of surface deformation and the influential factors in the recent decades. This study was designed to detect the spatial position of these surface deformations and to understand the mechanism behind them. Sentinel-1A TOPS model image data acquired in descending orbits between March 2015 and May 2019 were processed by using Small Baseline Subsets Interferometry (SBAS)-Interferometric Synthetic Aperture Radar (InSAR) technology, and then compared with geology, tectonical aspects of the study area and land cover types in Lanzhou. The results reveal that the land surface deformation is uneven in this city, and seven surface deformation regions were detected in the following areas: the north freight yard, Jiuzhou, Country garden, Donggang, Yanjiaping, Zhongxinping and Liuquan town. The land surface deformation rate in Lanzhou ranges from −82.13 mm/year to 19.31 mm/year. Time-series land surface deformation analysis showed that deformation increased over time in major deformation regions. Surface deformation expansion was significant after June 2017and it continued to expand. The surface deformation of Lanzhou is affected by natural factors (geology and geological faults) and human activities (land cover types/land cover changes). Local geological conditions control the location of the surface deformation process. These findings provide compelling data and theoretical support for disaster prevention and reduction in Lanzhou.

The spatial accessibility of urban parks is an important indicator of the livability level of cities. In this paper, we propose a comprehensive multimodal two-step floating catchment area (CM2SFCA) method which integrates supply capacity, the selection probability of individuals, and variable catchment sizes into the traditional multimodel 2SFCA method. This method is used to measure park accessibility in Wuhan, China. The results show that the spatial distribution of park accessibility under the proposed method is variant. High accessibility areas are clustered near the Third Ring Road with strong supply capacity parks, and low accessibility areas are distributed in the western and southern regions. Compared with the single-model accessibility (bicycling, driving, and public transit) method, we found that the multimodal spatial accessibility, combining the characteristics of three single transportations, can provide a more realistic evaluation. We also explore the spatial relationship between park accessibility and population density by bivariate local Moran’s I statistic and find that the Low Ai-High Pi area is located in the center of the study area, and the Low Ai-Low Pi area is located at the edge of the study area, with a relatively discrete distribution of parks and weak supply capacity. These findings may provide some insights for urban planners to formulate effective policies and strategies to ease the spatial inequity of urban parks.

Content integrity of high-resolution remote sensing (HRRS) images is the premise of its usability. Existing HRRS image integrity authentication methods are mostly binary decision-making processes, which cannot provide a further interpretable information (e.g., tamper localization, tamper type determination). Due to this reason, a robust HRRS images integrity authentication algorithm using perceptual hashing technology considering both global and local features is proposed in this paper. It extracts global features by the efficient recognition ability of Zernike moments to texture information. Meanwhile, Features from Accelerated Segment Test (FAST) key points are applied to local features construction and tamper localization. By applying the concept of multi-feature combination to the integrity authentication of HRRS images, the authentication process is more convincing in comparison to existing algorithms. Furthermore, an interpretable authentication result can be given. The experimental results show that the algorithm proposed in this paper is highly robust to the content retention operation, has a strong sensitivity to the content changing operations, and the result of tampering localization is more precise comparing with existing algorithms.

Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.

Based on the method of the Jenks natural break point, this paper constructs the floor area ratio level of urban habitat environmental quality, and proposes a method of analyzing spatial difference characteristics of the human living environment in urban neighborhoods using the Theil-index model and spatial difference measurement index. The proposed method can be used not only to characterize the spatial difference measurement value of urban neighborhoods, but also to calculate the inner spatial difference in city blocks. The effectiveness of the method is verified by the remote sensing image of the case point and the actual data.

Economic growth has always been one of the hottest topics in economic research. Behind the rapid economic growth, the economic gap between regions is gradually widening, and the internal gap will have an impact on the overall coordination of economic growth. Research on the convergence of economic development and its causes has great strategic significance for narrowing the differences in raising economy among regions. In recent years, the impact of big data on economic analysis has become more and more obvious, and this fact has attracted the attention of the academic community. Big data are a new strategic resource and a tool for assessing economic trends. Adding big data technology to the research on the convergence raise of economics can predict the law of data changes, reduce data errors, optimize research results, and provide a more scientific basis for the coordinated development of regional economies. Based on big data theory and technology, this paper uses a spatial econometric model to empirically analyze the convergence of regional economic growth and its influencing factors. The experimental results show that the research on the convergence mechanism and spatial relationship of economic growth in the context of big data can improve the accuracy of the convergence analysis of economic growth to a certain extent. Through modeling analysis, the accuracy of economic convergence is improved by 4.1%. The utilization of big data in a trend of economic development makes the analysis results more reasonable and has greater reference value.