Xingzhe Xie

Bio: Xingzhe Xie is an academic researcher from Ghent University. The author has contributed to research in topics: Dynamic time warping & RANSAC. The author has an hindex of 7, co-authored 20 publications receiving 267 citations. Previous affiliations of Xingzhe Xie include Southwest University of Science and Technology.

A review of urban air pollution monitoring and exposure assessment methods

Abstract: The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data.

Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm

Abstract: Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users’ turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score.

Inferring Directed Road Networks from GPS Traces by Track Alignment

Abstract: This paper proposes a method to infer road networks from GPS traces These networks include intersections between roads, the connectivity between the intersections and the possible traffic directions between directly-connected intersections These intersections are localized by detecting and clustering turning points, which are locations where the moving direction changes on GPS traces We infer the structure of road networks by segmenting all of the GPS traces to identify these intersections We can then form both a connectivity matrix of the intersections and a small representative GPS track for each road segment The road segment between each pair of directly-connected intersections is represented using a series of geographical locations, which are averaged from all of the tracks on this road segment by aligning them using the dynamic time warping (DTW) algorithm Our contribution is two-fold First, we detect potential intersections by clustering the turning points on the GPS traces Second, we infer the geometry of the road segments between intersections by aligning GPS tracks point by point using a “stretch and then compress” strategy based on the DTW algorithm This approach not only allows road estimation by averaging the aligned tracks, but also a deeper statistical analysis based on the individual track’s time alignment, for example the variance of speed along a road segment

Human Mobility Monitoring in Very Low Resolution Visual Sensor Network

Abstract: This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 × 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics.

Road network inference from GPS traces using DTW algorithm

Abstract: This paper proposes a method for inferring the road network from Global Position System (GPS) traces, which is composed of intersections and the roads between each pair of directly-connected intersections. Random Sampling (RANSAC) algorithm is used to cluster the turning points, where the users change their moving directions, into intersections. All of the GPS traces are segmented by the intersections, resulting in connectivity matrix of the intersections and small GPS tracks for each pair of directly-connected intersections. At last, the road between each two directly-connected intersections is extracted through aligning and averaging all of the tracks using Dynamic Time Warping (DTW) algorithm. The main novelty of the uthors methods is aligning the tracks point by point for each road using a "stretching and compression" strategy, which not only allows road estimation by averaging the aligned tracks, but also a deeper statistical analysis using their time alignment, such as analyzing the users' speed stability at a specific location. The experimental results show that the authors algorithm outperforms other methods by producing clean road network without spurious edges.

Change detection techniques for remote sensing applications: a survey

Abstract: Change detection captures the spatial changes from multi temporal satellite images due to manmade or natural phenomenon. It is of great importance in remote sensing, monitoring environmental changes and land use –land cover change detection. Remote sensing satellites acquire satellite images at varying resolutions and use these for change detection. This paper briefly analyses various change detection methods and the challenges and issues faced as part of change detection. Over the years, a wide range of methods have been developed for analyzing remote sensing data and newer methods are still being developed. Timely and accurate change detection of Earth’s surface features provides the basis for evaluating the relationships and interactions between human and natural phenomena for the better management of resources. In general, change detection applies multi-temporal datasets to quantitatively analyse the temporal effects of the phenomenon. As such, this study attempts to provide a comprehensive review of the fundamental processes required for change detection. The study also gives a brief account of the main techniques of change detection and discusses the need for development of enhanced change detection methods.

Unprecedented smoke-related health burden associated with the 2019-20 bushfires in eastern Australia.

Abstract: Weather conditions conducive to extreme bushfires are becoming more frequent as a consequence of climate change.1 Such fires have substantial social, ecological, and economic effects, including the effects on public health associated with smoke, such as premature mortality and exacerbation of cardiorespiratory conditions.2,3 During the final quarter of 2019 and the first of 2020, bushfires burned in many forested regions of Australia, and smoke affected large numbers of people in New South Wales, Queensland, the Australian Capital Territory and Victoria. The scale and duration of these bushfires was unprecedented in Australia. We undertook a preliminary evaluation of the health burden attributable to air pollution generated by bushfires during this period.

The rise of low-cost sensing for managing air pollution in cities

Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

CHI '02 Extended Abstracts on Human Factors in Computing Systems

Abstract: Interactive technologies have changed - and continue to change - our world. We are living in an era of transformation driven by the Internet, hand-held computing, digital photography, interactive entertainment, and wireless communication technologies. But such transformations are nothing new. Adoption of many technologies has transformed our work, play, communication, and thought. It has also transformed our research and practice in the HCI community.CHI 2002 explores transforming technologies, looking back to the past and forward to the future. First, by reflecting on past (and current) technologies, we seek a better understanding of questions like: Why do some technologies succeed where others fail? How have economic and work conditions and leisure been changed? What roles have technologists, entrepreneurs, legislators, lawyers, and citizens played? We examine these issues especially as they relate to HCI, a young field that will celebrate its 20th anniversary at CHI 2002.Second, we look forward, examining emerging technologies and involving us all in shaping their progress. Profound HCI issues are raised as computer and communications technologies progress from portable to wearable to implantable, as the power and speed of technology increases and the cost decreases, and as the standard desktop graphical interface is augmented by mobile devices, tangible and mixed reality interfaces. In response, we ask: What role should HCI professionals play in the development and deployment of such profoundly transformative devices and the socio-technical systems surrounding them? How can we ensure usability and a regard for personal privacy? What is the role of the legal and political system can they "keep up" with technology, or do sufficiently compelling technologies simply push them aside.Third, in addition to looking outward, CHI 2002 has looked inward to the practices of our community. We used some new technologies to nudge the CHI conference in the direction of greater interactivity, turning attendees into participants. CHIplace.org, our interactive online forum, enables people to exchange ideas, offer suggestions, and preview conference content. As part of CHIs increasing emphasis on issues of interest to designers and usability practitioners, CHI 2002 features the Practitioners Special Track. Professional and student designers present portfolios of their work, and usability professionals reflect on the experiences they have gained with usability methods in practice. CHI 2002 also is proud to feature a two-day forum, the CHI 2002|AIGA Experience Design Forum. Held in collaboration with the Experience Design Group of AIGA, the American Institute of Graphic Arts, this forum serves as an in-depth, interdisciplinary exploration of issues in design and human-computer interaction.

Unprecedented health costs of smoke-related PM 2.5 from the 2019–20 Australian megafires

Abstract: In flammable landscapes around the globe, longer fire seasons with larger, more severely burnt areas are causing social and economic impacts that are unsustainable. The Australian 2019–20 fire season is emblematic of this trend, burning over 8 million ha of predominately Eucalyptus forests over a six-month period. We calculated the wildfire-smoke-related health burden and costs in Australia for the most recent 20 fire seasons and found that the 2019–20 season was a major anomaly in the recent record, with smoke-related health costs of AU$1.95 billion. These were driven largely by an estimated 429 smoke-related premature deaths in addition to 3,230 hospital admissions for cardiovascular and respiratory disorders and 1,523 emergency attendances for asthma. The total cost was well above the next highest estimate of AU$566 million in 2002–03 and more than nine times the median annual wildfire associated costs for the previous 19 years of AU$211 million. There are substantial economic costs attributable to wildfire smoke and the potential for dramatic increases in this burden as the frequency and intensity of wildfires increase with a hotter climate. Worldwide, longer fire seasons are causing unsustainable impacts. This study finds that the 2019–20 Australia fire season caused health-related costs of AU$1.95 billion, nine times the median for the previous 19 years.