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Mapping population distribution at fine spatial scales is significant and fundamental for resource utilization, assessment of city disaster, environmental regulation, and urbanization. Multisource data produced by remote and social sensing have been widely used to disaggregate census information to map population distributions at fine resolution. However, it is challenging to achieve accurate high-spatial-resolution population mapping by combining multisource data and considering geographic spatial heterogeneity. The existing approaches do not consider global and local spatial information simultaneously, resulting in low accuracy. This article proposes a multimodel fusion neural network for estimating fine-resolution population estimates from multisource data. Our approach takes into account the local spatial information and global information of each geographic unit. Specifically, a first-order space matrix of a geographic unit is used to characterize its local spatial information. We propose a multimodel neural network, which combines a convolutional neural network and a multilayer perceptron (MLP) model to estimate a fine-resolution population mapping. Using Shenzhen, China, as the experimental setting, a population distribution map was generated at a 100-m spatial resolution. The model was quantitatively validated by showing that it captured the relationship between the estimated population and the census population at the township level ($R^2=0.77$) more accurately than the WorldPop dataset ($R^2=0.51$) and the MLP-based model ($R^2=0.63$). Qualitatively, the proposed model can identify differences in population density in densely populated areas and some remote population clusters more accurately than the WorldPop population dataset.

Urban land use and land cover (LULC) change is the result of urban population economic activities and national policy. Determining the spatial pattern of land cover types in cities is of particular significance for regional sustainable development. To achieve a better understand the spatiotemporal patterns of land use types in Shenzhen, the fractal dimension of spatial distributions is adopted as an index of the complex evolution of urban land-use. In addition, a long-term sequences LULC datasets are collected to do analysis, which covers the period 1988–2015 by employing Landsat TM/ETM+/OLI of 1988, 1993, 1999, 2001, 2005, 2008, 2011, 2013 and 2015. Last but not least, a granularity analysis is adopted to study the structural changes of each land cover. After analysis, it can be observed that a significant self-similarity law exists in the Shenzhen city. From 1988 to 2015, the fractal dimension of grassland, waterbody and bare land exhibits a bi-fractals dimension. However, grassland and bare land structure show a bi-fractals trend which increases every year, and the water-body bi-fractals trend is weakening. The development of urban land in this region experiences a process of a multiscale differential development with a hierarchical spatial system. These findings will provide some scientific references for the regional planning decisions on evolution of urban land use of Shenzhen city.

This research is based on Australian Data Cube Data Organization and Management Framework. Through data cleaning, data segmentation and data index, remote sensing observation data collected in Wuhan from 2000 to 2017, are organized into a data cube with time series as the Z-axis. Then, extraction of MODIS-NDVI data about 414 tile images. The mean value and standard deviation value of the tile matrix were calculated to detect the areas with frequent changes in vegetation coverage in Wuhan during the 18 consecutive years. The vegetation coverage curve was extracted using the time series as the z-axis to further explore the specific time nodes and change process of vegetation coverage. The experiment results show that: (1)During the period from 2000 to 2010, the area covered by vegetation in Wuhan decreased dramatically when the city expanded; (2)After 2010, due to the followup of green work in the later stages of urban development, the area of green space in Wuhan was restored; (3)In recent years, with the large-scale transportation projects carried out in Wuhan, to a certain extent, it has affected the existing green areas.