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Instance segmentation has been widely applied in building extraction from remote sensing imagery in recent years, and accurate instance segmentation results are crucial for urban planning, construction and management. However, existing methods for building instance segmentation (BSI) still have room for improvement. To achieve better detection accuracy and superior performance, we introduce a Hybrid Task Cascade (HTC)-based building extraction method, which is more tailored to the characteristics of buildings. As opposed to a cascaded improvement that performs the bounding box and mask branch refinement separately, HTC intertwines them in a joint multilevel process. The experimental results also validate its effectiveness. Our approach achieves better detection accuracy compared to mainstream instance segmentation methods on three different building datasets, yielding outcomes that are more in line with the distinctive characteristics of buildings. Furthermore, we evaluate the effectiveness of each module of the HTC for building extraction and analyze the impact of the detection threshold on the model's detection accuracy. Finally, we investigate the generalization ability of the proposed model. [ABSTRACT FROM AUTHOR]

The interaction between the population and built environment is a constant topic in urban spaces and is the main driving force of urban evolution. Understanding urban population distribution and its relationship with the built environment could provide guidance for urban planning, traffic, and disaster management. Following this line of thought, this study conducted an empirical analysis in Xi'an, a rapidly developing western city in China. Well-permeated mobile phone location data were used to represent the spatiotemporal dynamics of the population, and the built environment was characterized from five perspectives—transportation, location, building, greenery, and land use—using multisource geospatial data. Finally, the dynamic heterogeneous influence of built environment factors on population distribution was examined using multiscale geographically weighted regression (MGWR). Overall, the influencing coefficients exhibited a significant dynamic changing process from a temporal perspective and simultaneously demonstrated spatial nonstationarity. Moreover, the specific findings about the influence of each built environment factor facilitate a deeper insight into dynamic population distribution and its determinants. [ABSTRACT FROM AUTHOR]

Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this paper, an indicator, mobile phone data-based nighttime economy–housing imbalance intensity, is proposed to measure the degree of the nighttime economy–housing imbalance. This indicator can distinguish vitality variations between sleep periods and nighttime activity periods, which are highly related to the nighttime economy–housing imbalance. The spatial pattern of the nighttime economy–housing imbalance was explored, and its association with the built environment was investigated through city-scale geographical regression analysis in Shanghai, China. The results showed that the sub-districts of Shanghai with high-positive-imbalance intensities displayed structures with superimposed rings and striped shapes, and the sub-districts with negative imbalance intensities were distributed around high positive-intensity areas. There were significant linear correlations between imbalance intensity and the built environment. The multiple influences of built environment factors and related mechanisms were explored from a geographical perspective. Our study utilized the social sensing data to provide a more comprehensive understanding of the nighttime economy–housing imbalance. These findings will be useful for fostering the nighttime economy and supporting urban renewal. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pansharpening is the process of fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. In the process of pansharpening, the LRMS image is often directly upsampled by a scale of 4, which may result in the loss of high-frequency details in the fused high-resolution multispectral (HRMS) image. To solve this problem, we put forward a novel progressive cascade deep residual network (PCDRN) with two residual subnetworks for pansharpening. The network adjusts the size of an MS image to the size of a PAN image twice and gradually fuses the LRMS image with the PAN image in a coarse-to-fine manner. To prevent an overly-smooth phenomenon and achieve high-quality fusion results, a multitask loss function is defined to train our network. Furthermore, to eliminate checkerboard artifacts in the fusion results, we employ a resize-convolution approach instead of transposed convolution for upsampling LRMS images. Experimental results on the Pléiades and WorldView-3 datasets prove that PCDRN exhibits superior performance compared to other popular pansharpening methods in terms of quantitative and visual assessments. [ABSTRACT FROM AUTHOR]

For a city to be livable and walkable is the ultimate goal of future cities. However, conflicts among pedestrians, vehicles, and cyclists at traffic intersections are becoming severe in high-density urban transportation areas, especially in China. Correspondingly, the transit time at intersections is becoming prolonged, and pedestrian safety is becoming endangered. Simulating pedestrian movements at complex traffic intersections is necessary to optimize the traffic organization. We propose an unmanned aerial vehicle (UAV)-based method for tracking and simulating pedestrian movements at intersections. Specifically, high-resolution videos acquired by a UAV are used to recognize and position moving targets, including pedestrians, cyclists, and vehicles, using the convolutional neural network. An improved social force-based motion model is proposed, considering the conflicts among pedestrians, cyclists, and vehicles. In addition, maximum likelihood estimation is performed to calibrate an improved social force model. UAV videos of intersections in Shenzhen are analyzed to demonstrate the performance of the presented approach. The results demonstrate that the proposed social force-based motion model can effectively simulate the movement of pedestrians and cyclists at road intersections. The presented approach provides an alternative method to track and simulate pedestrian movements, thus benefitting the organization of pedestrian flow and traffic signals controlling the intersections. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Over the past few decades, the coastal city of Shenzhen has been transformed from a small fishing village to a mega city as China's first Special Economic Zone. The rapid economic development was matched by a sharp increase in the demand for usable land and coastal reclamation has been undertaken to create new land from the sea. However, it has been reported that subsidence occurred in land reclamation area and around subway tunnel area. Subsidence and the additional threat of coastal inundation from sea-level rise highlight the necessity of displacement monitoring in Shenzhen. The time Series InSAR technique is capable of detecting sub-centimeter displacement of the Earth's surface over large areas. This study uses Envisat, COSMO-SkyMed, and Sentinel-1 datasets to determine the surface movements in Shenzhen from 2004 to 2010 and from 2013 to 2017. Subsidence observed can be attributable to both land reclamation and subway construction. Seasonal displacements are likely to be associated with precipitation. The influence of ocean tidal level changes on seasonal displacement is not strongly evident from the results and requires further investigations. In general, InSAR has proven its ability to provide accurate measurements of ground stability for the city of Shenzhen. [ABSTRACT FROM AUTHOR]