Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.

Portraying urban functional zones provides useful insights into understanding complex urban systems and establishing rational urban planning. Although several studies have confirmed the efficacy of remote sensing imagery in urban studies, coupling remote sensing and new human sensing data like mobile phone positioning data to identify urban functional zones has still not been investigated. In this study, a new framework integrating remote sensing imagery and mobile phone positioning data was developed to analyze urban functional zones with landscape and human activity metrics. Landscapes metrics were calculated based on land cover from remote sensing images. Human activities were extracted from massive mobile phone positioning data. By integrating them, urban functional zones (urban center, sub-center, suburbs, urban buffer, transit region and ecological area) were identified by a hierarchical clustering. Finally, gradient analysis in three typical transects was conducted to investigate the pattern of landscapes and human activities. Taking Shenzhen, China, as an example, the conducted experiment shows that the pattern of landscapes and human activities in the urban functional zones in Shenzhen does not totally conform to the classical urban theories. It demonstrates that the fusion of remote sensing imagery and human sensing data can characterize the complex urban spatial structure in Shenzhen well. Urban functional zones have the potential to act as bridges between the urban structure, human activity and urban planning policy, providing scientific support for rational urban planning and sustainable urban development policymaking.

People’s daily travels are structured and can be expressed as networks. Few studies explore how people organize their daily travels and which behavioral principles result in the choices of specific network types. In this study, we first reconstruct location networks and activity networks for numerous individuals from high-resolution mobile phone positioning data and define frequent networks as motifs. The results suggest that 99.9% of people’s travels can be characterized by a limited set of location-based motifs and activity-based motifs. The results further reveal that the least effort principle governs the preferred motif choices through quantifying the rank-frequency properties. The scaling properties of distance characteristically impact motifs, and their scaling differences by node numbers and motif types coincide with the popularities of motifs, verifying the self-adaptions in motif choices; that is, although individuals travel with unique propensities, they always tend to choose the motif with the lowest consumption that satisfies their demand.

Understanding complex urban systems necessitates untangling the relationships between diverse urban elements such as population, infrastructure, and socioeconomic activities. Scaling laws are basic but effective rules for evaluating a city’s internal growth logic and assessing its efficiency by investigating whether urban indicators scale with population. To date, only limited research has empirically explored the scaling relations between variables of urban mobility in mega-cities at an intra-urban scale of a few meters. Using multiple urban-sensed and human-sensed data, this study proposes a thorough framework for quantifying the scaling laws in a city. To begin, urban mobility networks are built by aggregating population flows using large-scale mobile phone tracking data. To demonstrate the spatiotemporal variability of urban mobility, various network-based mobility measures are proposed. Following that, three different features of urban mobility laws are exposed, explaining spatial agglomeration, spatial hierarchical structures, and the temporal growth process. The scaling correlations between urban indicators pertaining to socioeconomic features and infrastructure and a mobility-population measure are then quantified using multi-sourced urban-sensed data. Applying this framework to the case study of Shenzhen, China revealed (a) spatial travel heterogeneity, hierarchical spatial structures, and mobility growth, and (b) not only a robust sub-linear relationship between infrastructure volume and population, but also a sub-linear relationship for socioeconomic activity. The identified scaling laws, both in terms of mobility measures and urban indicators, provide a multi-faceted portrait of the spatio-temporal variations of urban settings, allowing us to better understand intra-urban developments and, consequently, provide critical policy evaluations and suggestions for improving intra-urban efficiency in the future.