Huan Ning

Huan Ning
University of South Carolina | USC · Department of Geography

Master of Science

About

20
Publications
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173
Citations

Publications

Publications (20)
Article
Full-text available
Street view images are now widely used in web map services, providing on-site photos of street scenes for users to explore without physically being in the field. These photos record detailed visual information of the street environment with geospatial control; therefore, they can be used for metric mapping purposes. In this study, we present a meth...
Article
Full-text available
Urban greenway is an emerging form of urban landscape offering multifaceted benefits to public health, economy, and ecology. However, the usage and user experiences of greenways are often challenging to measure because it is costly to survey such large areas. Based on the online postings from Instagram in 2017, this paper used Computer Vision (CV)...
Article
Full-text available
Widespread problems of psychological distress have been observed in many countries following the outbreak of COVID-19, including Australia. What is lacking from current scholarship is a national-scale assessment that tracks the shifts in mental health during the pandemic timeline and across geographic contexts. Drawing on 244 406 geotagged tweets i...
Article
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Auditing and mapping traffic infrastructure is a crucial task in urban management. For example, signalized intersections play an essential role in transportation management; however, effectively identifying these intersections remains unsolved. Traditionally, signalized intersection data are manually collected through field audits or checking stree...
Article
Full-text available
Street view imagery such as Google Street View is widely used in people's daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is un...
Article
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Effective quantification of visitation is important for understanding many impacts of the COVID-19 pandemic on national parks and other protected areas. In this study, we mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices an...
Article
Full-text available
In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model...
Article
Full-text available
Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy i...
Preprint
Full-text available
In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model...
Article
A reliable, punctual, and spatially accurate dataset of sidewalks is vital for identifying where improvements can be made upon urban environment to enhance multi-modal accessibility, social cohesion, and residents' physical activity. This paper develops a synthetically new spatial procedure to extract the sidewalk by integrating the detected result...
Preprint
Full-text available
Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy i...
Article
Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy i...
Article
Full-text available
Land cover data is an inventory of objects on the Earth’s surface, which is often derived from remotely sensed imagery. Deep Convolutional Neural Network (DCNN) is a competitive method in image semantic segmentation. Some scholars argue that the inadequacy of training set is an obstacle when applying DCNNs in remote sensing image segmentation. Whil...
Article
Full-text available
In the Big Data era, Earth observation is becoming a complex process integrating physical and social sectors. This study presents an approach to generating a 100 m population grid in the Contiguous United States (CONUS) by disaggregating the US census records using 125 million of building footprints released by Microsoft in 2018. Land-use data from...
Article
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This article aims to implement a prototype screening system to identify flooding-related photos from social media. These photos, associated with their geographic locations, can provide free, timely, and reliable visual information about flood events to the decision-makers. This screening system, designed for application to social media images, incl...
Article
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The small Unmanned Aerial System (sUAS) is an emerging approach to monitor new buildings. sUAS acquires ultra-high-resolution imagery which provides visual evidence and reduces the necessity of in-situ investigation. It offers greater potential for building change detection when two epochs of images of the place of interest are captured. This study...
Article
Full-text available
In recent years, social media platforms have played a critical role in mitigation for a wide range of disasters. The highly up-to-date social responses and vast spatial coverage from millions of citizen sensors enable a timely and comprehensive disaster investigation. However, automatic retrieval of on-topic social media posts, especially consideri...
Article
Full-text available
In recent years, social media such as Twitter have received much attention as a new data source for rapid flood awareness. The timely response and large coverage provided by citizen sensors significantly compensate the limitations of non-timely remote sensing data and spatially isolated river gauges. However, automatic extraction of flood tweets fr...

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Projects

Projects (5)
Project
Develop new technology and methodology to mine image/video big data to capture the dynamic of urban life and to understand the interaction between residents and urban environments.
Project
Human movement is among the essential forces that drive the spatial spread of COVID-19. During such a global pandemic, monitoring and analyzing human movement patterns or population flows are critical for us to gain a better understanding into current and future infectious risk at the population level. Funded by NSF and NIH, this project utilizes big movement data (e.g., social media, mobile phone data), artificial intelligence (AI), and spatiotemporal analysis to monitor the spatial spread of COVID-19, quantify the effectiveness of the control measures, and predict the current and future infectious risk at various geospatial scales. Results of this study will not only provide enhanced situation awareness for the government at all levels, but also offer valuable contributions to building collective public awareness of the role people play in the evolution of the COVID-19 pandemic.