Qunying Huang

EDUCATION

Ph.D., Earth Systems and Geoinformation Sciences, George Mason University, 2011
M.S., Cartography and GIScience, Peking University, 2007
B.S., Survey and Mapping Engineering, Central South University, 2004

RESEARCH AREAS

My research primarily focuses on GIScience, Spatial Big Data Analytics, GeoAI, and Remote Sensing. I leverage and synthesize big data streams from both physical (e.g., remote sensing) and social (e.g., social media, mobile phone records) sensing networks for different application domains, such as natural hazards, environmental justice, human mobility, social inequality and segregation. I am also very interested in applying different computing models, including cluster, grid, GPU, citizen computing, and especially cloud computing to address contemporary computing challenges in the GIScience.

CURRENT RESEARCH

Flood mapping, wildfire burnt area mapping, active fire detection, disaster damage assessment, trajectory data anlytics,  human mobility and population dynamics, urban informatics, digital agriculture

Please visit my lab’s website for information on recent research projects.

COURSES TAUGHT

Geog170, Our Digital Globe: An Overview of GIScience and its Technology
Geog574, Spatial Database
Geog576, Spatial Web and Mobile Programming
Geog970, Seminar in Geographic Information Science

PUBLICATIONS

For the most recent publications, please refer to my Google Scholar profile, and CV.

BOOKS

Li Z., Huang Q., Emrich C., 2021. Social Sensing and Big Data Computing for Disaster Management, Routledge/Taylor & Francis, ISBN 978-0-367-61765-3 2. (Edited special issues)

Li Z., Tang W., Huang Q., Shook E., Guan Q., 2020. Big Data Computing for Geospatial Applications, MPDI, ISBN 978-3-03943-244-8. (Edited special issues)

Yang C., Yu M., Huang Q. etc., 2016. Introduction to Programming and GIS Algorithms with Python and ArcGIS, CRC Press/Taylor & Francis, 328p. ISBN: 978-1466510081.

Yang C., Huang Q., Li Z., Xu C., Liu K., 2013. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis, 304p. ISBN: 978-1466593169.

Huang Q., 2012. Adaptive Nested Models and Cloud Computing for Scientific Simulation: A Case Study Using Dust Storm Forecasting, LAP LAMBERT Academic Publishing, 120p. ISBN: 978-3659154775.

SELECTED REFERRED PUBLICATIONS
*Underlined names are student advisees; Corresponding authors are marked with “*”

Wu M., Liu X., Qin Y., Huang Q.*, 2023. Estimating experienced racial-ethnic segregation based on social media data: A case study in Los Angeles-Long Beach-Anaheim. Computers, Environment and Urban Systems (CEUS), 104 (2023): 102008 – 102021. DOI: 10.1016/j.compenvurbsys.2023.102008.

Vongkusolkit J., Peng B., Wu M., Huang Q.*, Andresen C. G., 2023. Near Real-Time Flood Mapping with Weakly Supervised Machine Learning. Remote Sensing, 15(13), 2363. DOI: 10.3390/rs15133263.

Ma Y., Yang Z., Huang Q., Zhang Z*., 2023. Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach. Remote Sensing, 15(18), 4562; DOI: 10.3390/rs15184562

Xu S, Huang Q., Zou Z*., 2023. Spatio-Temporal Transformer Recommender: Next Location Recommendation with Attention Mechanism by Mining the Spatio-Temporal Relationship between Visited Locations. ISPRS International Journal of Geo-Information, 12(2):79.

Wu M., Huang Q.*, Gao S., 2023. Measuring access inequality in a hybrid physical-virtual world: A case study of racial disparity of healthcare access during CoVID-19. In Proceedings of 2023 30th International Conference on Geoinformatics, Jul 19-21, 2023 London, UK, pg.1-10. DOI: 10.1109/Geoinformatics60313.2023.10247690.

Wu M.D., Huang Q.*, Sui T., Wu M., Pixel-wise Wildfire Burn Severity Classification with Bi-temporal Sentinel-2 Data and Deep Learning. In Proceedings of the 6th International Conference on Big Data Technologies (ICBDT 2023), Sep 22-24, 2023, Qingdao, China, pg.1-6.

Liu X., Wu M., Peng B., and Huang Q.*, 2022.  Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data. Scientific Report. DOI : 10.1038/s41598-022-19441-9.

Wu M., and Huang Q.*, 2022. Human movement patterns of different racial-ethnic and economic groups in US top 50 populated cities: What can social media tell us about isolation?. Annals of GIS, 28 (2):161-183.  DOI: 10.1080/19475683.2022.2026471.

Wu M., and Huang Q.*, 2022. IM2City: image geo-localization via multi-modal learning. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, ACM, 1–4 Nov, 2022, Seattle, WA, USA, pp. 50-61.

Cai T., Gan H., Peng B., Huang Q., and Zou Q., 2022. Real-time Classification of Disaster Images from Social Media with a Self-supervised Learning Framework. In Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 17 – 22 July, 2022, Kuala Lumpur, Malaysia, 1-4.

Zou B.,Peng B., Huang Q.*, Flood Depth Assessment with Location-Based Social Networks Data and Google Street View — a Case Study with Buildings as Reference Objects. In Proceedings of the 2022 IGARSS, 17 – 22 July, 2022, Kuala Lumpur, Malaysia, pg. 1-4.

Peng B., Huang Q.*, Vongkusolkit J., Gao S., Wright D., Fang Z. and Qiang Y., 2021. Urban Flood Mapping with Bi-temporal Multispectral Imagery via a Self-supervised Learning Framework. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 2001-2016. DOI: 10.1109/JSTARS.2020.3047677.

Scheele C., Yu M., and Huang Q.*, Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features. International Journal of Digital Earth, pp.1-23. DOI: 10.1080/17538947.2021.1968048

Liu X., Huang Q.*, Gao S. and Xia J., 2021. Activity knowledge discovery: Detecting collective and individual activities with digital footprints and open source geographic data. Computers, Environment and Urban Systems, 85, p.101551. DOI: 10.1016/j.compenvurbsys.2020.101551.

Zou Z*., Gan H., Huang Q., Cai T. and Cao, K., 2021. Disaster Image Classification by Fusing Multimodal Social Media Data. ISPRS International Journal of Geo-Information, 10(10), p.636.

Zhou S., Kan P., Huang Q*. and Silbernagel J., 2021. A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura. Journal of Information Science, 1-15. DOI: 10.1177/01655515211007724

Peng B., Huang Q.*, Rao J., 2021. Spatiotemporal Contrastive Representation Learning for Building Damage Classification. In Proceedings of the 2022 IGARSS, July 11-16, 2021, Brussels, Belgium, pg. 1-4.

Rao J., Gao S.*, Li M. and Huang Q., 2021. A privacy‐preserving framework for location recommendation using decentralized collaborative machine learning. Transactions in GIS, 25(3): 1153-1175.

Qin Y*, Zhang X, Zhao Z, Li Z, Yang C, Huang Q., 2021. Coupling Relationship Analysis of Gold Content Using Gaofen-5 (GF-5) Satellite Hyperspectral Remote Sensing Data: A Potential Method in Chahuazhai Gold Mining Area, Qiubei County, SW China. Remote Sensing, 4(1):109.

Vongkusolkit J., and Huang Q.*, 2020. Situational awareness extraction: a comprehensive review of social media data classification during natural hazards. Annals of GIS, 27(1):5-28.DOI: 10.1080/19475683.2020.1817146

Shen B., Xu X., Li J*., Plaza A., and Huang Q., 2020. Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation. ISPRS International Journal of Geo-Information, 9(11), 683.

Li Z*., Tang W., Huang Q., Shook E. and Guan Q., 2020. Introduction to Big Data Computing for Geospatial Applications. International Journal of Geo-Information, 9(8): 487.

Rao J., Gao S.*, Kang Y., and Huang Q., LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection. In Proceedings of the 11^th International Conference on Geographic Information Science (GIScience 2021), pp. 1-16.

Peng B., Meng Z., Huang Q.*, Wang C., 2019. Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery. International Journal of Remote Sensing, 11(21), 2492. DOI: 0.3390/rs11212492.2.

Yu M., Huang Q., Scheele C., Han Q., Yang C., 2019. Deep Learning for Real-Time Social Media Text Classification for Situation Awareness – Using Hurricanes Sandy and Harvey as Case Studies. International Journal of Digital Earth, 12(11): 1230-1247.DOI: 10.1080/17538947.2019.1574316.

Meng Z., Peng B., Huang Q.*, Flood Depth Estimation from Open Images. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities, ACM SIGSPATIAL 2019, Nov 5-8, Chicago, IL, USA, pg. 1-6.

Peng B., Liu X., Meng Z., and Huang Q.*, 2019. Urban Flood Mapping with Residual Patch Similarity Learning. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI 2019), Nov 5-8, Chicago, IL, USA, p.40–47. DOI:https://doi.org/10.1145/3356471.3365235.

Huang Q.*, Cervone G., Zhang G., 2017. A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data. Computers, Environment and Urban Systems, 66: 23-37.

Huang Q.*, Li J., Li Z., 2017. A Hybrid Cloud Platform Based on Multi-sourced Computing and Model Resources for Geosciences. International Journal of Digital Earth. DOI: http://dx.doi.org/10.1080/17538947.2017.1385652.

Zhang G, Zhu AX, Huang Q., 2017. A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data. International Journal of Geographical Information Science, 31(10): 2068-2097.

Li Z.*, Huang Q., Carbone G.J., Hu F., 2017. A high performance query analytical framework for supporting data-intensive climate studies. Computers, Environment and Urban Systems, 62: 210-221.

Yang C.*, Huang Q., Li Z., Liu K., Hu F., 2017. Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10: 13-53.

Huang Q.*, 2016. Mining Online Footprints to predict user’s next location. International Journal of Geographic Information Science, 31(3): 523-541.

Huang Q.*, Wong D., 2016. Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? International Journal of Geographic Information Science, 30(9): 1873-1898.

Huang Q.*, 2016. Mining Online Footprints to predict user’s next location. International Journal of Geographic Information Science, 31(3): 523-541.

Zhang G., Huang Q., Zhu A.X., Keel J.H., 2016. Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function. International Journal of Geographical Information Science. doi: 10.1080/2F13658816.2016.1170836

Cervone G., Sava E., Huang Q., Schnebele E., Harrison J., Waters N., 2016. Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study. International Journal of Remote Sensing, 37(1): 100-124.

Wang C., Pavlowsky R.T., Huang Q., C. Chang, 2016. Channel bar area extraction for a mining-contaminated river using high-spatial multispectral remote sensing imagery. GIScience and Remote Sensing, 53(3): 283-302. doi: 10.1080/15481603.2016.1148229.

Zhang T., Li J., Liu Q., Huang Q., 2016. A cloud-enabled remote visualization tool for time-varying climate data analytics. Environmental Modeling & Software, 75: 513-518.

Gui Z, Yu M, Yang C, Jiang Y, Chen S, Xia J. Huang Q., Liu K., Li Z., Hassan M., Jin B., 2016. Developing Subdomain Allocation Algorithms Based on Spatial and Communicational Constraints to Accelerate Dust Storm Simulation. PLoS ONE, 11(4): e0152250. doi:10.1371/journal.pone.0152250.

Huang Q.*, Cervone, G., 2016. Usage of Social Media and Cloud Computing during Natural Hazards, in.In: Vance T., Merati N., Yang C., Yuan M., eds. Cloud Computing for Ocean and Atmospheric Sciences. Academic Press.

Li J., Liu K., Huang Q., 2016. Utilizing cloud computing to support scalable atmospheric modeling: A case study of cloud-enabled ModelE. In: Vance T., Merati N., Yang C., Yuan M., eds. Cloud Computing for Ocean and Atmospheric Sciences. Academic Press.

Huang Q.*, Wong D., 2015. Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty: An Example Using Twitter Data. Annals of the Association of American Geographers, 105(6): 1179-1197.

Huang Q.*, Xiao Y., 2015. Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery. International Journal of Geo-Information, 4(3): 1549-1568. doi:10.3390/ijgi4031549.

Huang Q.*, Cervone G, Jing D., Chang C., 2015. DisasterMapper: A CyberGIS framework for disaster management using social media data. In Proceedings of the 4th International ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data (BigSpatial) 2015, ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.

Xiao Y., Huang Q., Wu K, 2015. Understanding social media data for disaster management. Natural Hazards. doi:10.1007/s11069-015-1918-0.

Chang C., Ye. Z., Huang Q., and Wang C. 2015. “An Integrative Method for Mapping Urban Land Use Change Using Geo-sensor Data.” UrbanGIS’15: Proceedings of 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.

Hultquist C., Simpson M., Cervone G, Huang Q., 2015. Using Nightlight Remote Sensing Imagery and Twitter Data to Study Power Outages. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management 2015 (EM-GIS 2015), ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.

Huang Q.*, Cao G., Wang C., 2014. From Where Do Tweets Originate? – A GIS Approach for User Location Inference. In Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN ’14), ACM SIGSPATIAL 2014, Nov 6-9, Dallas, TX.

Huang Q.*, C. Xu, 2014. A Data-Driven Framework for Archiving and Exploring Social Media Data, Annals of GIS, 20(4): 265-277

Li R., Feng W., Wu H., Huang Q., 2014. A replication strategy for a distributed high-speed caching system based on spatiotemporal access patterns of geospatial data. Computers, Environment and Urban Systems. DOI: 10.1016/j.compenvurbsys.2014.02.009

Li Z., Yang C., Huang Q., Liu K., Sun M., Xia J., et al., 2014. Building Model as a Service to Support Geosciences. Computers, Environment and Urban Systems. DOI: 10.1016/j.compenvurbsys.2014.06.004.

Xia J., Yang C., Liu K., Gui Z., Li Z., Huang Q., & Li, R. (2014). Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives. International Journal of Digital Earth, 8(6): 451-475.

Gui Z., Yang C., Huang Q. et al., 2014. A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services, PLOS ONE , 9(8): e105297. doi: 10.1371/journal.pone.0105297.

Huang Q., Li Z., Liu K., Xia J., Xu C., Jiang Y., Yu M., Yang C.*, 2014. Accelerating Geocomputation with Cloud Computing. In: Shi X., Kindratenko, V., Yang C., eds. Modern Accelerating Technologies for GIScience. Springer.

Huang Q., Yang C.*, Liu K., Xia J., Xu C., Li J., Gui Z., Sun M., Li Z., 2013. Evaluating Open Source Cloud Computing Solutions for Geosciences, Computers & Geosciences, 59(9): 41-52.

Huang Q., Yang C.*, Benedict K., Chen, S., Rezgui A., Xie J., 2013. Utilize Cloud Computing to Support Dust Storm Forecasting, International Journal of Digital Earth, 6(4): 338-355.

Huang Q., Yang C.*, Benedict K., Rezgui A., Xie J., Xia J., Chen, S., 2013. Using Adaptively Coupled Models and High-performance Computing for Enabling the Computability of Dust Storm Forecasting, International Journal of Geographic Information Science, 27(4): 765-784.

Huang Q., Yang C.*, 2011. Optimizing Grid Configuration to Support Geospatial Processing – An Example with DEM Interpolation, Computer & Geosciences, 37(2): 165-176.

Li J., Jiang Y., Yang C.*, Huang Q., Rice M., 2013. Visualizing 3D/4D environmental data using many-core graphics processing units (GPUs) and multi-core central processing units (CPUs), Computers & Geosciences. DOI: j.cageo.2013.04.029.

Huang Q., Xia J., Yang C.*, Hassan M., Chen S., 2012. An Experimental Study of Open-Source Cloud Platforms for Dust Storm Forecasting, Proceedings of the ACM SIGSPATIAL 2012, Nov 6-9, Redondo Beach, CA, pp.534-537.

Sun M., Li J., Yang C.*, Schmidt G.A., Bambacus M., Cahalan R., Huang Q., Xu C., Noble E.U., Li Z., 2012. A Web-based Geovisual Analytical Tool for Spatiotemporal Climate Data, Future Internet. DOI: 10.3390/fi4041069.

Yang C.*, Wu H., Huang Q., Li Z., Li J., 2011. Spatial Computing for Supporting Physical Sciences, Proceedings of National Academy of Sciences, 108(14):5498-5503.

Yang C.*, Goodchild M., Huang Q., Nebert D., Raskin R., Bambacus M., Xu Y., Fay D., 2011. Spatial Cloud Computing – How Can Geospatial Sciences Use and Help to Shape Cloud Computing, International Journal of Digital Earth, 4(4): 305-329.

Xie J., Yang C.*, Zhou B., Huang Q., 2010. High Performance Computing for the Simulation of Dust Storms. Computers, Environment, and Urban Systems, 34(4): 278-290.

Huang Q., Yang C.*, Nebert D., Liu K., Wu H., 2010. Cloud Computing for Geosciences: Deployment of GEOSS Clearinghouse on Amazon’s EC2, Proceedings of the ACM.

AWARDS AND HONORS

2020: Vilas Associates Award;
2020: Microsoft AI for Earth Award
2016: Madison Teaching and Learning Excellence (MTLE) Faculty Fellow;
2015: Best paper award for or the 1st International Symposium on Spatiotemporal Computing (ISSC);
2014: Next Generation of Hazards & Disasters Researchers, National Science Foundation;
2014: CyberGIS Fellow, NCSA;
2012: Outstanding Graduate Student, GMU;
2011: Summer Student Best Paper Travel Award, UCGIS;
2010: AAG CISG Student Best Paper Award

GRADUATE STUDENTS AND POSTDOCS

Current: Bo Peng (Ph.D), Chenxiao (Atlas) Guo (Ph.D), Xinyi Liu (Ph.D), Jirapa (Jam) Vongkusolkit (M.S), Yuhan Qin (B.S);
Graduated: Chris Scheele (M.S), Duanyang Jing (M.S) , Zidong Zhang (M.S), Meiliu Wu (M.S), Zonglin Meng (B.S);