Pengfei Chen

• Professor (Assistant) at Sun Yat-sen University

Moulins play a pivotal role in delivering surface meltwater and significantly impacting the mass balance of the Greenland ice sheet (GrIS). Unlike crevasses, moulins are difficult to detect from satellite remote sensing imagery due to their significantly small size. Recently, unmanned aerial vehicle (UAV)-based remote sensing has become a prevalent...

The application of crowdsourced data holds transformative potential in reshaping decision-making processes. However, effectively harnessing the power of crowdsourced data within the complex landscape of urban tourism governance, especially in China marked by rapid growth and dynamic shifts in the tourism market, remains hindered by institutional co...

Global land cover (GLC) data are an indispensable resource for understanding the relationship between human activities and the natural environment. Estimating their classification accuracy is significant for studying environmental change and sustainable development. With the rapid emergence of various GLC products, the lack of high-quality referenc...

Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal location...

The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to worldwide health systems in quick response to epidemics. The assessment of personal exposure to COVID-19 in enclosed spaces is critical to identifying potential infectees and preventing outbreaks. However, traditional contact tracing methods rely heavily on a manua...

Individuals’ indoor trajectories can be reconstructed with sensing data for indoor navigation. However, the movement uncertainty of such reconstructed indoor trajectories has seldom been modelled before, which seriously affects the reliability of indoor trajectory analytics. Previous methods for movement uncertainty modelling mainly focus on outdoo...

In the last decade, land cover products are produced at a global scale and updated with an unprecedent speed with the development of earth observation and mapping techniques. However, assessing large-scale land cover datasets is always a challenging task because of lack of ground truth and high dependency on manual inspection. To promote the effici...

Urban green space (UGS) is an important component of urban resources which contributes to human physical and mental health. Studies on the accessibility of UGS under the two-step floating catchment (2SFCA) framework have recently received much attention. However, the effects of people’s actual mobility patterns have not been fully considered in cur...

The increasing amount of geotagged social media data provides a possible resource for location prediction. However, existing location prediction methods rarely incorporate temporal changes in mobility patterns, which could lead to unreliable predictions. In particular, human mobility patterns have changed greatly in the COVID-19 era. We propose a n...

Timely precise metro ridership forecasting is helpful to reveal real-time traffic demand, which is a crucial but challenging task in modern traffic management. Given the complex spatial correlation and temporal variation of riding behaviour in a metro system, deep learning algorithms have been widely applied owing to their superior performance in c...

Research has shown that the growing holiday travel demand in modern society has a significant influence on daily travel patterns. However, few studies have focused on the distinctness of travel patterns during a holiday season and as a specified case, travel behavior studies of the Chinese Spring Festival (CSF) at the city level are even rarer. Thi...

Modelling movement uncertainty is of profound significance in promoting effective trajectory analysis and mining. However, classic uncertainty models are limited by rigid assumptions on moving speed and distance, which ignores the stochastic nature of individual’s travel behaviour. This study introduces a novel method using adaptive ellipses to rep...

Volunteered geographic information can be used to predict regional desirability. A common challenge regarding previous works is that intuitive empirical models, which are inaccurate and bring in perceptual bias, are traditionally used to predict regional desirability. This results from the fact that the hidden interactions between user online check...

Extracting buildings from remotely sensed data is a fundamental task in many geospatial applications. However, this task is resistant to automation due to variability in building shapes and the environmental complexity surrounding buildings. To solve this problem, this paper introduces a novel automatic building extraction method that integrates Li...

Since manually labeling aerial images for pixel-level classification is expensive and time-consuming, developing strategies for land cover mapping without reference labels is essential and meaningful. As an efficient solution for this issue, domain adaptation has been widely utilized in numerous semantic labeling-based applications. However, curren...

Understanding the integration process of urban agglomeration is essential for sustainable regional development and urban planning. However, few studies have analyzed the spatial integration patterns of metropolitan regions according to the impacts of landscape ecology along rail transit corridors. This study performed a comprehensive inter-city gra...

An increasing number of social media users are becoming used to disseminate activities through geotagged posts. The massive available geotagged posts enable collections of users’ footprints over time and offer effective opportunities for mobility prediction. Using geotagged posts for spatio-temporal prediction of future location, however, is challe...

We propose a reference-free method for identifying potential classification errors and quantitatively evaluating the reliability of vector-based land cover data. First, by integrating the results of multiple-resolution segmentation on a data-driven scale, land cover data are divided into segments with increased homogeneity for feature extraction. T...

Mapping scale is an essential issue in land use and land cover (LULC) data production, which always involves the minimum mapping unit (MMU) that stipulated in the product specification. Since the application of MMUs will inevitably cause some inappropriate classification problems, a technique is needed to evaluate the impact on the data outputs. In...

Geospatial data is a carrier of information that represents the geography of the real world. Measuring the information contents of geospatial data is always a hot topic in spatial-information science. As the main type of geospatial data, spatial vector data models provide an effective framework for encoding spatial relationships and manipulating sp...

Reliability is one of the most important theoretical problems in geographical conditions monitoring. In this paper, it presented that the research on the reliability of geographical conditions monitoring was based on the theory of spatial data reliability. First, it reviewed and analyzed the theory of spatial data reliability. The scope of reliabil...

The quality aspects of spatial data are very important in the decision-making process. However, the quality inspection of spatial data is still dependent on manual checking, and there is an urgent need to develop an automatic or semi-automatic generic system for spatial data quality inspection. In this paper, we present a general framework that aut...