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Dr. Zhenlong Li gave a colloquium talk at UNC Charlotte

Dr. Zhenlong Li  gave a colloquium talk on “Geospatial Big Data Analytics with High Performance Computing”at the Department of Geography & Earth Sciences,  University of North Carolina Charlotte.

Join us on the GIS Day at USC (November 14, 2018)

Join us for a conversation with Distinguished Emeritus Professor David Cowen and campus GIS practitioners on the use of geospatial technologies in teaching and research. To be followed by a Mapathon and GIS poster displays.

When: November 14, 2018;  Where: Hollings Program Room, Thomas Cooper Library, USC

AAG 2019 CFP – “GeoAI and Deep Learning Symposium: Big data and GeoAI for natural hazards”

GeoAI and Deep Learning Symposium: Big data and GeoAI for natural hazards

Recently, we have unfortunately witnessed a series of deadly hurricane events (e.g., Harvey, and Florence) and Northern California wildfires. Such events claim many lives, cause billions of dollars of damage to properties, and severely impact the environment. When a natural hazard occurs, managers and responders need timely and accurate information on damages and resources to make effective response decisions and improve management strategies. This information is referred to as “Situational Awareness” (SA), i.e., an individually as well as socially cognitive state of understanding “the big picture” during critical situations. Fortunately, the popularity and advancement of social and physical sensor networks offer various real-time big data streams for establishing SA. For example, sharing information such as texts, images, and videos through social media platforms enables all citizens to become part of a large sensor network and a homegrown disaster response team. The use of Unmanned Aerial Vehicle (UAVs) images is offering increasing opportunities in disaster-related situations. However, such massive and rapidly changing data streams present new grand challenges to mine actionable data and extract critical validated information for various disaster management activities.
This session explores and captures the innovative machine learning and data mining algorithms, techniques and approaches to generate various useful information and products (e.g., hazard maps), which in turn can assist in disaster management during a natural hazard. Topics of particular interest are, but are not restricted to:
1. Mining and extracting actionable information for rapid emergency response and relief coordination
2. Integrating data mining (machines) and crowdsourcing (human) to support decision-making
3. Topic modeling and event detection
4. Physical infrastructure (e.g., roads, bridges and buildings) feature extraction with deep learning
5. Damage assessment using very high-resolution images or/and social media data
6. Coding/classifying text messages during a natural hazard
7. Identifying or/and matching the needs of people in impacted communities
8. Spatiotemporal mining of social media data to gain geographic situational awareness during a disaster
9. Mining, mapping and visualizing public’s behaviors, opinions or sentiments towards a disaster event
10. Innovative approaches to synthesizing and mining multi-sourced social and physical sensing data for disaster management

 

You will submit your participation/registration fee and abstracts online through AAG’s website (www.aag.org). We would appreciate it if you can send us the abstract at your earliest time, and send your PIN before Saturday, October 20, 2018 which will give us time to register the sessions. Please send your info to the following co-organizers:

Qunying Huang, qhuang46@wisc.edu;

Zhenlong Li, zhenlong@sc.edu;

Xinyue Yexinyue.ye@njit.edu

AAG 2019 CFP – “Symposium on Human Dynamics Research in the Age of Smart/Intelligent Systems: Smart Cities and Urban Computing”
AAG 2019 CFP – “Smart Cities and Urban Computing”
Symposium on Human Dynamics Research in the Age of Smart/Intelligent Systems

American Association of Geographers’ Annual Meeting, April 3-7, 2019, Washington, DC

Aims and Scope:

The dynamics of coupled environmental and human systems, and their complexities and connectivity across space and time poses daunting challenges to effective solutions to a variety of urban development and sustainable issues. However, the advancements in Internet of Things and connected devices, have opened up frontiers for data-driven urban computing and analytics to understand the dynamics of these systems and their interactions in real-time. Analytical advancements have also enabled rigorous analysis of big data available from sensors and citizens to develop effective and timely solutions to challenging urban problems and for policy interventions. This session invites research that sheds light on the opportunities, challenges and solutions of using big data, CyberGIS and spatial data science for urban computing and smart cities. Specifically, theoretical, methodological, and empirical research focusing on social and urban big data with fine spatial, temporal, and thematic resolutions are welcome. Possible topics may include but are not limited to:
  1.     Multi-scale modeling of human mobility
  2.     Urban disaster and emergency
  3.     Indoors GIS and smart buildings
  4.     CyberGIS analytics for urban big data
  5.     Spatial theories of smart cities
  6.     Urban safety, security, and privacy
  7.     Transportation and mobility data analysis
  8.     Urban data fusion
  9.     Urban data mining
You will submit your participation/registration fee and abstracts online through AAG’s website (www.aag.org). We would appreciate it if you can send us the abstract at your earliest time, and send your PIN before Saturday, October 20, 2018 which will give us time to register the sessions.
Please send your info to the following co-organizers:
  • Xinyue Ye, College of Computing, New Jersey Institute of Technology (xye@njit.edu)
  • Bandana Kar, Urban Dynamics Institute, Oak Ridge National Laboratory (karb@ornl.gov)
  • Shaowen Wang, Department of Geography & Geographic Information Science, University of Illinois at Urbana-Champaign (shaowen@illinois.edu)
  • Zhenlong Li, Department of Geography, University of South Carolina (zhenlong@sc.edu)
Dr. Zhenlong Li was invited to give a talk at the Harvard University Center for Geographical Analyses

Dr. Zhenlong Li gave a presentation “Social Sensing and Big Data Computing for Disaster Management: What can social media tell us about Hurricane evacuation?” at the New Generation GIS workshop. The workshop was organized by the NSF Spatiotemporal Innovation Center (STC) and hosted by Harvard University Center for Geographical Analyses on Oct 11, 2018.

More about the workshop: https://cga-download.hmdc.harvard.edu/publish_web/website_files/PDF_MISC/NGIS-newsletter-20181031.pdf

 

AAG 2019 CFP – “Symposium on Frontiers in Geospatial Data Science: Big Data Computing for Geospatial Applications”
Symposium on Frontiers in Geospatial Data Science at AAG 2019: Big Data Computing for Geospatial Applications

 

Aims and Scope:
Earth observation systems and model simulations are generating massive volumes of disparate, dynamic, and geographically distributed geospatial data with increasingly finer spatiotemporal resolutions. Meanwhile, the propagation of smart devices and social media also provide extensive geo-information about daily life activities. Efficiently analyzing those geospatial big data streams enables us to investigate unknown and complex patterns and develop new decision-support systems, thus provides unprecedented values for business, sciences, and engineering.
However, handling the “Vs” (volume, variety, velocity, veracity, and value) of big data is a challenging task. This is especially true for geospatial big data since the massive datasets often need to be analyzed in the context of dynamic space and time. This section aims to capture the latest efforts on utilizing, adapting, and developing new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges for supporting geospatial applications in different domains such as climate change, disaster management, human dynamics, public health, and environment and engineering.
Potential topics include (but are not limited to) the following:
  • Geo-cyberinfrastructure integrating spatiotemporal principles and advanced computational technologies (e.g., high-performance computing, cloud computing, and deep learning/AI).
  • New computing and programming frameworks and architecture or parallel computing algorithms for geospatial applications.
  • New geospatial data management strategies and data storage models coupled with high-performance computing for efficient data query, retrieval, and processing (e.g. new spatiotemporal indexing mechanisms).
  • New computing methods considering spatiotemporal collocation (locations and relationships) of users, data, and computing resources.
  • Geospatial big data processing, mining and visualization methods using high-performance computing and artificial intelligence.
  • Integrating scientific workflows in cloud computing and/or high performance computing environment.
  • Any other research, development, education, and visions related to geospatial big data computing.
To present a paper in the session, you will first need to register and submit your abstract online (www.aag.org/annualmeetings/), and then email your presenter identification number (PIN), paper title, and abstract to one of the organizers listed below  by October 20, 2018.
Organizers:
  • Zhenlong Li, Department of Geography, University of South Carolina, US. zhenlong@sc.edu
  • Qunying Huang, Department of Geography, University of Wisconsin-Madison, Madison, US. qhuang46@wisc.edu
  • Wenwu Tang, Department of Geography and Earth Sciences, University of North Carolina at Charlotte,US. wenwutang@uncc.edu
  • Eric Shook, Department of Geography, Environment, and Society, University of Minnesota, US. eshook@umn.edu
  • Qingfeng Guan, School of Information Engineering, China University of Geosciences, China. guanqf@cug.edu.cn
Paper Accepted by ACM SIGSPATIAL

Our paper Measuring Inter-City Network Using Digital Footprints from Twitter Users has been accepted by the ACM SIGSPATIAL Workshop on Prediction of Human Mobility (PredictGIS), which will be held in Seattle, WA, November, 2018.

In this paper, a new method that uses Twitter users’ movement as measurement to measure hierarchical city connectivity and build directional information flow. This method integrates human movements in the physical world and the digital movements in the virtual world. Using human mobility as measurement, flows go beyond administrative boundaries and connect counties which are not physically neighbors to each other.

Call for Submissions: 2019 AAG Robert Raskin Student Competition
AAG CISG 2019 Robert Raskin Student Competition
We are pleased to announce the “2019 Robert Raskin Student Competition“, which will take place during the AAG Annual Meeting in Washington, DC April 3 – April 7, 2019. This competition is sponsored by the AAG Cyberinfrastructure Specialty Group (CISG). The competition aims to: 

  1. Promote research in topics related to cyberinfrastructure (CI), such as high performance/distributed computing, spatial data management, processing, mapping, visualization, analysis and mining, spatial web and mobile services.
  2. Encourage spatial thinking and the development of geospatial CI in colleges and universities.
  3. Inspire curiosity about geographic patterns, geo-computing, and CI for students and the broader public.

 

IMPORTANT DATES:

10/20/2018: Abstract (~500 words) due to Dr. Jing Li (Jing.Li145@du.edu) or Dr. Zhenlong Li ( zhenlong@ sc.edu)

10/ 25/ 2018: Invitation notification

03/01/2019:  Extended abstract submission deadline

04/05/2019 – 04/09/2019: Presenting and announcing winners

 

The prizes of $500 for the 1st place, $200 for the 2nd place and $100 for the 3rd place will be determined immediately following the special presentation session. The results of the competition will be published in the AAG Newsletter and the GISSSG newsletter, as well as on the specialty group’s web page. The awards committee reserves the right to not offer such prizes if the papers are not of sufficiently high quality.

Competition Rules

  1. Each participant needs to submit an abstract through the AAG conference submission system. Finalists should submit an extended abstract (2,500 words limit). Other supplementary materials, such as a URL to a live website, can also be included in the submission.
  2. Competition participants must be enrolled full-time at a community college or a university. All submissions must be the original work of the entrant.
  3. Recommended application areas include, but are not limited to: crime, public health issues, transportation, climate, urban planning, land use/cover change, and disasters of all kinds.
  4. Each entry can be submitted by one student or one student group (no more than FIVE people in each group). Maximum of one entry per student. Entries must be designed and implemented by the student(s).
  5. Students who participated in previous years’ competition can attend again, but entries must be different from earlier submissions.
  6. As the judge panels, the CISG board members will select up to SIX entries. Judges will consider a variety of criteria, including but not limited to topics, techniques, design and writing. Students may be asked to supply documentation proving their full-time student status.
  7. The finalists will be invited to give an oral presentation at the AAG meeting. The finalists must register for the 2019 AAG meeting at their own expense. They are encouraged to attend the conference and must give a presentation in the CISG Student Competition Panel Session during the AAG meeting. Additional travel support may be available and will be announced through our competition websites.

Questions or submission:
Please contact Jing Li (Jing.Li145@du.edu) or Zhenlong Li (Zhenlong@sc.edu) for questions or submission details.

Paper accepted by International Journal of Digital Earth

Our paper entitled “A visual-textual fused approach to automated tagging of flood-related tweets during a flood event” has been accepted by the International Journal of Digital Earth.

Taking the Houston Flood in 2017 as a study case, this paper presents an automated flood tweets extraction approach by mining both visual and textual information a tweet contains. A CNN architecture was designed to classify the visual content of flood pictures during the Houston Flood. A sensitivity test was then applied to extract flood sensitive keywords that were further used to refine the CNN classified results. A duplication test was finally performed to trim the database by removing the duplicated pictures to create the flood tweets pool for the flood event. The results indicated that coupling CNN classification results with flood sensitive words in tweets allows a significant increase in precision while keeps the recall rate in a high level. The elimination of tweets containing duplicated pictures greatly contributes to higher spatio-temporal relevance to the flood.

IJGI Special Issue “Big Data Computing for Geospatial Applications”

Call for papers

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 30 June 2019

More Information:

http://www.mdpi.com/journal/ijgi/special_issues/Big_Data_Geospatial_Applications

Paper accepted by the Cartography and Geographic Information Science

Our paper entitled “A graph-based approach to detect the tourist movement pattern using social media data” has been accepted by Cartography and Geographic Information Science.

This paper introduces a graph-based approach to detect the tourist movement patterns from massive and noisy social media(Twitter) data, and an object-based model is designed to represent the tourist’s spatiotemporal movement trajectory. To build the tourist graph, we first utilize the DBSCAN-based method to cluster the tourist trajectories to identify the vertices in the graph and then connect the vertices by using the tourist trajectories to generate the edges of the graph. Once the tourist graph is constructed, a set of graph-based network analysis methods is introduced to detect the tourist movement patterns.
New York City is used as the study area to demonstrate and evaluate the proposed approach. Based on the results of the case study, we reveal the tourist movement patterns by detecting the popular attractions, centric attraction, popular point-to-point routes, popular tour routes from the tourist graph. These results demonstrate that the proposed methodologies provide a feasible and effective way to build a graph-based network model for tourists from big social media data to analyse their movement patterns.

Yuqin won the 2018 Department of Geography Teaching Assistant Award

Yuqin Jiang received the 2018 Department of Geography Teaching Assistant Award for her TA work in Fall 2017 semester.

Yuqin worked for Dr. Cuizhen (Susan) Wang as lab instructor for GEOG 345 Aerial Photo Interpretation and worked for Dr. Michael Hodgson as lab instructor for GEOG 363 Introduction to GIS and GEOG 564 GIS-based Modeling.

Paper accepted by IEEE Transactions on Geoscience and Remote Sensing

Paper authored by Xiao Huang, Cuizhen (Susan) Wang, and Zhenlong Li entitled Reconstructing Flood Inundation Probability by Enhancing Near Real-Time Imagery with Real-Time Gauges and Tweets has been accepted for publication by the IEEE Transactions on Geoscience and Remote Sensing (Impact Factor: 4.942).

Zhenlong Li co-organized a series of Big Data sessions in AAG 2018 Annual Meeting

Dr. Zhenlong Li co-organized a series of sessions at AAG 2018 Annual Meeting (with Drs. Qunying Huang, Xinyue Ye, Shaowen Wang, Wenwu Tang, and Eric Shook). These sessions aimed to capture and discuss the latest advancements of big data computing, artificial intelligence and deep learning for supporting natural disaster management, geospatial applications, smart cities. Details can be found below:

Yuqin Jiang won 3rd place in the 2018 AAG Robert Raskin Competition

Yuqin Jiang won 3rd place in the 2018 AAG Robert Raskin Student Paper Competition Award among a group of highly competitive students for her paper titled “Social Network, Activity Space, Sentiment, and Evacuation: What Can Social Media Tell Us?”

Zhenlong Li is elected as the Vice Chair of the AAG Cyberinfrastructure Specialty Group

Dr. Zhenlong Li is elected as the Vice Chair of the Cyberinfrastructure Specialty Group(CISG) of the Association of American Geographers(AAG) for  year of 2018-2019. He will assume the Chair role for the second year.

The AAG CISG strives to enhance geographic research and scholarship on matters relating to cyberinfrastructure. For more information about CISG, visit: http://gis.cas.sc.edu/cisg

Huan Ning wins the USGIF/NVIDIA GPU Essay Challenge

Organized by the US Geospatial Intelligence Foundation and open to the 14 GEOINT accredited universities, the challenge asked applicants to describe which problem they would choose to solve if given access to to an NVIDIA GPU-powered supercomputer. Ning’s winning essay, submitted with USC Geography professors Zhenlong Li and Cuizhen “Susan” Wang, was titled “Tagging the Earth with High Resolution Imagery and Deep Learning.”

In announcing the winners, Dr. Camelia Kantor, USGIF’s Director of Academic Programs, noted that the winning entries “stood out because they made a strong case for using the GPUs for the benefit of their students.”

CFP: Special Issue on “Social Sensing and Big Data Computing for Disaster Management” in International Journal of Digital Earth(IJDE)

Open Call for Submissions

Special Issue on “Social Sensing and Big Data Computing for Disaster Management” in International Journal of Digital Earth(IJDE)

http://explore.tandfonline.com/cfp/est/ijde/si-5

Guest Editors

Zhenlong Li, Department of Geography, University of South Carolina, SC 29208, USA zhenlong@sc.edu

Qunying Huang, Department of Geography, University of Wisconsin-Madison, WI 53706, USA qhuang46@wisc.edu

Christopher Emrich, School of Public Administration, University of Central Florida, FL 32816, USA  christopher.emrich@ucf.edu

Submission Deadline: March 1st, 2018

Aims and Scope

Rapid onset disasters, often difficult to prepare for and respond to, make disaster management a challenging task worldwide. Disaster and emergency management effectiveness depends heavily on making good decisions in near-real time under extreme duress.  These key, often life-saving, decisions are possible only with real-time data sources and the ability to timely collect, process, synthesize, and analyze these multi-sourced data. Traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or immediately following damaging events. For example, stream gauges are only useful for flood mapping while the stations are functioning properly and before they are overtopped by floodwaters and rendered inoperable.

Fortunately, sharing information such as texts, images, and videos through social media platforms enables all citizens to become part of a large sensor network and a homegrown disaster response team. Compared to traditional physical sensors, such a citizen-sensor network (social sensing) is low cost, more comprehensive, and always broadcasting situational awareness information.  For example, with social sensing, massive amounts of micro-level disaster information (e.g. site specific damage) can be captured in real-time through social media platforms (e.g. Twitter, Facebook) and voluntarily reported via dedicated crowdsourcing applications (volunteered geographic information, VGI), enabling rapid assessment of evolving disaster situations.

On the other hand, data collected with social sensing is often massive, heterogeneous, noisy, unreliable, and comes in continuous streams.  This is inherent “Big Data”, for example, millions of microblog posts from different social media platforms can be generated in a short time right after an impactful disaster. Hence, Big Data computing methods and technologies such as cloud computing, distributed geo-information processing, spatial statistics/modeling, data mining, spatial database, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion.

Along these lines, this special issue on “Social Sensing and Big Data Computing for Disaster Management” by the International Journal of Digital Earth aims to capture recent advancements in leveraging social sensing and big data computing for supporting disaster management in one or more disaster phases (mitigation, preparedness, response, and recovery). Specifically, we solicit original unpublished research articles that can shed light on the opportunities, challenges, and solutions of leveraging social sensing and big data computing for supporting disaster management.

Potential topics include (but are not limited to) the following:

  1. Innovative approaches to synthesize multi-sourced social sensing data and/or traditional data (e.g. remote sensing) for disaster management
  2. Analyzing and visualizing human movement patterns before, during, and after disaster events
  3. Disaster event detection, early warning, and impact/damage assessment with social sensing
  4. Mining and extracting actionable information for rapid emergency response and relief coordination
  5. Geovisual analytics of social sensing data during a disaster
  6. Integrating data mining (machines) and crowdsourcing (human) to support decision-making
  7. New tools and solutions for real-time big social sensing data collecting, processing, analyzing, and visualizing
  8. Exploring public perception, sentiments, and understanding towards disaster events
  9. Novel social engagement approaches to effectively link the public in an organized way toward contributing to emergency response, recovery
  10. Data quality, reliability, and privacy issues of social sensing for disaster management
  11. Leveraging big social sensing data to enhance social resilience

Important Dates

November 15, 2017, 800-word abstract submission to guest editors

December 1, 2017, full paper submission invited

March 1, 2018, full paper submission online

May 1, 2018, revision/rejection notification

August 1, 2018, paper acceptance notification

About the journal

The International Journal of Digital Earth is an international peer-reviewed academic journal (SCI-E with a 2016 impact factor 2.292) focusing on the theories, technologies, applications, and societal implications of Digital Earth and those visionary concepts that will enable a modeled virtual world.

Submission Guidelines

Submissions must follow the instructions to authors outlined on the Taylor & Francis web page for the International Journal of Digital Earth found: here. Word templates are available on the web site and papers are typically 5000‐8000 words in length.

Papers should be submitted online at the International Journal of Digital Earth’s Manuscript Central Site: here. New users should first create an account. Once a user is logged onto the site submissions should be made via the Author Centre. Please indicate the paper is submitted to Special Issue on “Social Sensing and Big Data Computing for Disaster Management” in the cover letter.

Each paper will receive comments from at least three reviewers. The special issue will include a maximum of 8 papers.

We look forward to your contributions. Please do not hesitate to contact the Guest Editors in case of questions.

 

Dr. Zhenlong Li’s research is highlighted in Charleston City Paper

Dr. Zhenlong Li’s research on using social media for flood mapping is highlighted in Charleston City Paper. Full story can be found here:

 

https://www.charlestoncitypaper.com/charleston/avoiding-fake-news-during-extreme-storms-while-leveraging-twitter-to-report-the-weather/Content?oid=11026999

Dr. Zhenlong Li and Yuqin Jiang’s research featured on local ABC broadcast

Dr. Zhenlong Li and his student Yuqin Jiang’s research on the use of Twitter data to track solar-eclipse viewing in South Carolina was featured on the local ABC broadcast affiliate.

Zhenlong Li’s research was highlighted on WSPA COLUMBIA, S.C.

“Researchers also looked at the use of social media during and after the floods. Zhenlong Li studied the use of Twitter for quickly mapping the flood based on what people were tweeting and from where. He found a high correspondence between the number of tweets from a specific location and the level of flooding.

https://www.wsav.com/news/usc-researchers-who-studied-sc-flood-share-findings/amp/