Spatial Computing and Data Mining Lab

Publications

  1. Wu M., Huang Q.*, Sui T., Peng B., Yu M., 2024. A Remote Sensing Spectral Index Guided Bitemporal Residual Attention Network for Wildfire Burn Severity Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI: 10.1109/JSTARS.2024.3460531
  2. Sui T., Huang Q.*, Wu M.D., Wu M.L., Zhang Z., 2024. BiAU-Net: Wildfire burnt area mapping using bi-temporal Sentinel-2 imagery and U-Net with attention mechanism. International Journal of Applied Earth Observation and Geoinformation, 132:104034. DOI: 10.1016/j.jag.2024.104034
  3. Yu M., Huang Q., Li Z., 2024. Deep learning for spatiotemporal forecasting in Earth system science: a review. International Journal of Digital Earth, 17(1): 2391952. DOI: 10.1080/17538947.2024.2391952
  4. Yang S, Huang Q.*, Yu M., 2024. Advancements in Remote Sensing for Active Fire Detection: A Review of Datasets and Methods. Science of Total Environment, 943: 173273. DOI: 10.1016/j.scitotenv.2024.173273
  5. Wu M., Huang Q.*, Gao S., Zhang Z., 2023. Mixed Land Use Measurement and Mapping with Street View Images and Spatial Context-Aware Prompts via Zero-shot Multimodal Learning. International Journal of Applied Earth Observation and Geoinformation, 125 (2023): 103591. DOI: org/10.1016/j.jag.2023.103591
  6. Wu M., Liu X., Qin Y., Huang Q.*, 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. Download
  7. 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.
  8. 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
  9. 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.
  10. 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. Download
  11. Wu M., and Huang Q., 2022. IM2City: Image Geo-localization via Multi-modal Learning. In Proceedings of 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022), ACM, Seattle, WA, USA.
  12. 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, pp.1-23.  DOI: 10.1080/19475683.2022.2026471. Download
  13. 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.
  14. Zou B.,Peng B., Huang Q., 2022. 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.
  15. Scheele C., Yu M. and Huang Q., 2021. 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: https://doi.org/10.1080/17538947.2021.1968048.  Download
  16. Peng B., Huang Q., and Rao J., 2021. Spatiotemporal Contrastive Representation Learning for Building Damage Classification. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 11-16, 2021, Brussels, Belgium. Download
  17. 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.
  18. 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, p.01655515211007724. Download
  19. 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.
  20. Liu X., Huang Q., Gao S. and Xia J., 2020. 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: 1016/j.compenvurbsys.2020.101551. Download
  21. 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), p.683.
  22. Huang, Q., Li, J., Zhang, T., 2020. Domain Application of High Performance Computing in Earth Science: An Example of Dust Storm Modeling and Visualization. In Wu W., and Wang S., eds. High Performance Computing for Geospatial Applications, pp. 249-268. Springer, Cham.
  23. Vongkusolkit J., and Huang Q., 2020. Situational awareness extraction: a comprehensive review of social media data classification during natural hazards. Annals of GIS, DOI: 10.1080/19475683.2020.1817146
  24. 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.
  25. 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.
  26. Yu M., Bambacus M., Cervone G., Clarke K., Duffy D., Huang Q., Li J., Li W., Li Z., Liu Q. and Resch B., 2020. Spatiotemporal event detection: a review. International Journal of Digital Earth, 13(10): 1186 – 1211.
  27. Rao J., Gao S., Kang Y., and Huang Q., 2020. LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection. In Proceedings of the 11th International Conference on Geographic Information Science (GIScience 2021), pp. 1-16. (Accepted)
  28. 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. Remote Sensing, 11(21), 2492. DOI: 0.3390/rs11212492.
  29. 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.
  30. Meng Z., Peng B., Huang Q., 2019. 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.
  31. 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, 40–47. DOI:https://doi.org/10.1145/3356471.3365235.
  32. Gao S., Rao, J., Liu, X., Kang, Y., Huang Q., and App J., 2019. Exploring the Effectiveness of Geomasking Techniques for Protecting the Geoprivacy of Twitter Users. Journal of Spatial Information Science, 2019 (19): 105-129.
  33. Li Z., Huang Q., Jiang Y., Hu F., 2019. SOVAS: A Scalable Online Visual Analytic System for Big Climate Data Analysis. International Journal of Geographical Information Science, 34(6): 1188-1209.
  34. Liu X., Huang Q., Gao S., 2019. Exploring the Uncertainty of Activity Zone Detection Using Digital Footprints with Multi-Scaled DBSCAN. International Journal of Geographical Information Science, 33(6):1196-223. DOI: 10.1080/13658816.2018.1563301. Download
  35. Vincent K., Roth R. E, Moore S. A., Huang Q., Lally N., Sack C., Nost E., and Rosenfeld H., 2018. Improving Spatial Decision Making Using Interactive Maps: An Empirical Study on Interface and Decision Complexity in the North American Hazardous Waste Trade. Environment and Planning B, 2018: 2399808318764122.
  36. Cao K., Huang Q., 2018. Geo-Sensor for Potential Prediction of Earthquakes: Can Earthquake Be Predicted By Abnormal Animal Phenomena? Annals of GIS, 24(2): 125-138.
  37. Huang Q., Li J., Li Z., 2018. A Hybrid Cloud Platform Based on Multi-sourced Computing and Model Resources for Geosciences. International Journal of Digital Earth. 11(12): 1184 – 1204.
  38. Zhou Z., Xie X., Huang Q., 2018. Enhancing the Impression on Cities: Mining Relations of Attractions with Geo-tagged Photos. In proceedings of the 2018 IEEE Cloud and Big Data Computings, Guangzhou, China, October 7-11, 2018, pg. 1-7.
  39. Gao S., Huang Q., 2018. Exploring the Effectiveness of Geomasking Techniques for Protecting the Geoprivacy of Twitter Users. In proceedings of the Location Privacy & Security Workshop, GIScience 2018, Aug 28-31, 2018, Melbourne, Australia, 1-8.
  40. Ye X., Li W., Huang Q., 2018. A Synthesized Urban Science in the Context of Big Data and Cyberinfrastructure. In Shen Z. and Li M., eds. Big Data Support of Urban Planning and Management: The Experience in China, pp. 435-448. Cham: Springer International Publishing.
  41. Huang Q., Cervone G., and 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.
  42. Huang Q.,2017.  Mining Online Footprints to Predict User’s Next Location. International Journal of Geographic Information Science, 31(3): 523-541. Download
  43. Wong D., Huang Q., 2017. “Vote with Their Feet”: Delineating the Sphere of Influence Using Social Media Data. International Journal of Geo-Information, 2017, 6 (11), 325. Doi: 10.3390/ijgi6110325.
  44. 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.
  45. 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.
  46. 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.
  47. Li R., Feng W., Wu H., Huang Q., 2017. A Replication Strategy for a Distributed High-Speed Caching System Based on Spatiotemporal Access Patterns of Geospatial Data. Computers, Environment and Urban Systems, 61: 163-171.
  48. Liu X., Huang Q., Li Z., Wu M., 2017. The Impact of MTUP: Explore Online Trajectories for Human Mobility Studies. In Proceedings of the 1st Workshop on Prediction of Human Mobility, ACM SIGSPATIAL 2017, Nov 7-10, Redondo Beach, CA, USA, pg.1-9.
  49. 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. Download
  50. 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, 30 (11): 2230-2252.
  51. Ye X., Huang Q., Li W., 2016. Integrating Big Social Data, Computing and Modeling for Spatial Social Science. Cartography and Geographic Information Science, 43(5): 377-378.
  52. 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.
  53. Wang C., Pavlowsky R.T., Huang Q., 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.
  54. 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.
  55. 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.
  56. Huang Q., Li Z., Li J., Chang C., 2016. Mining Frequent Trajectory Patterns from Online Footprints. In Proceedings of the 7th International ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS) 2016, ACM SIGSPATIAL 2016, Oct 31-Nov 03, Burlingame, CA, USA, 1-8.
  57. Huang Q., Cervone G., 2016. Usage of Social Media and Cloud Computing during Natural Hazards. In Vance T., Merati N., Yang C., and Yuan M., eds. Cloud Computing for Ocean and Atmospheric Sciences. Academic Press.
  58. 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., and Yuan M., eds. Cloud Computing for Ocean and Atmospheric Sciences. Academic Press.
  59. 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. Download
  60. 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.
  61. Xiao Y., Huang Q., Wu K, 2015. Understanding Social Media Data for Disaster Management. Natural Hazards, 79(3):1663-1679.
  62. 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), ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA, pg.1-6.
  63. Chang C., Ye Z., Huang Q., Wang C. 2015. An Integrative Method for Mapping Urban Land Use Change Using Geo-sensor Data. In Proceedings of the 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.
  64. Hultquist C., Simpson M., Cervone, 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, ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.
  65. Yang C., Sun M., Liu K., Huang Q., Li Z., Gui Z., … & Lostritto P., 2015. Contemporary Computing Technologies for Processing Big Spatiotemporal Data. In Kwan M.P, Richardson D., Wang D., and Zhou C., eds. Space-Time Integration in Geography and GIScience, pp. 327-351. Springer Netherlands.
  66. Huang Q., Xu C., 2014. A Data-Driven Framework for Archiving and Exploring Social Media Data, Annals of GIS, 20(4): 265-277.
  67. 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.
  68. 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: 1016/j.compenvurbsys.2014.06.004.
  69. 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.
  70. Gui Z., Yang C., Xia J., Huang Q. et al., 2014. A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services. PLoS ONE, 9(8): e105297. doi: 1371/journal.pone.0105297.
  71. 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, pg. 1-8.
  72. 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., and Yang C., eds. Modern Accelerating Technologies for GIScience. Springer.
  73. Li J., Jiang Y., Yang C., Huang Q., 2014. Utilizing GPU to Support Scientific Visualization in Geosciences. In Shi X., Kindratenko V., and Yang C., eds. Modern Accelerating Technologies for GIScience. Springer.
  74. Yang C., Liu K., Nebert, Li Z., Li W., Wu H., Li J., Sun M., Miao L., Huang Q., Xu Y., Fay D., 2014. GEOSS Clearinghouse – Integrating Geospatial Resources to Support the Global Earth Observation System of Systems. In Karimi H.A., eds. Big Data: Techniques and Technologies in Geoinformatics. CRC Press.
  75. 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.
  76. 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.
  77. 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.
  78. 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, 59 (2013): 78-89.
  79. Li Z., Yang C., Sun M., Li J., Xu C., Huang Q., Liu K., 2013. A High Performance Web-Based System for Analyzing and Visualizing Spatiotemporal Data for Climate Studies. In Proceedings of International Symposium on Web and Wireless Geographical Information Systems, pp. 190-198. Springer, Berlin Heidelberg.
  80. Huang Q., Xia J., Yu M., Benedict K., Bambacus M., 2013. Cloud-Enable Dust Storm Forecasting. In Yang C., Huang Q. eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  81. Huang Q., Xia J., Sun M., Liu K., Li J., Gui Z., Xu C., Yang C., 2013. How to Test the Readiness of Open Source Solutions. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  82. Huang Q., Li Z., Liu K., Xia J., Jiang Y., Xu C., Yang C., 2013. Handling of Data, Computing, Concurrent and Spatiotemporal Intensities. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  83. Yang C., Huang Q., 2013. Cloud Computing Concepts, Characteristics and Architecture. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  84. Li Z., Huang Q., Gui Z., 2013. Enabling Technologies. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  85. Liu K., Huang Q., Xia J., Li Z., Lostritto P., 2013. How to Use Cloud Computing. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  86. Liu K., Huang Q., Xia J., 2013. Cloud-enabling Geoscience Applications. In: Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  87. Gui Z., Xia J., Zhou N., Huang Q., 2013. How to choose cloud Computing: Towards a Cloud Computing Cost Model. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  88. Liu K., Nebert D., Huang Q., Xia J., Li. Z., 2013. Cloud-Enable GEOSS Clearinghouse. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  89. Li J., Li Z., Huang Q., Sun M., Liu K., 2013. Cloud-Enabling Climate@Home. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  90. Xu C., Xia J., Huang Q., Yu M., Bambacus M., 2013. Cloud services. In: Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  91. Yang C., Sun M., Xia J., Li J., Liu K., Huang Q., Gui Z., 2013. How to Test the Readiness of Cloud Services. In: Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  92. Xu C., Gui Z., Li J., Liu K., Huang Q., Bambacus M., 2013. Open Source Cloud Computing Solutions. In: Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  93. Nebert D., Huang Q., GeoCloud Initiative. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  94. Yang C., Huang Q., Gui Z., Li Z., Xu C., Jiang Y., Li J., 2013. Cloud Computing Research for Geosciences and Applications. In Yang C., Huang Q., eds. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis.
  95. 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, 4(4): 1069-1085.
  96. Huang Q., Xia J., Yang C., Hassan, Chen S., 2012. An Experimental Study of Open-Source Cloud Platforms for Dust Storm Forecasting. In Proceedings of the ACM SIGSPATIAL 2012, Nov 6-9, Redondo Beach, CA, pp.534-537.
  97. Li J., Jiang Y., Yang C., Huang Q., 2012. Visualizing 3D/4D Environmental Big Data Using Many-core Compute Unified Device Architecture (CUDA) and Multi-core Central Processing Unit (CPUs). In Proceedings of the MAT4GIS workshop, the 7th GIScience International Conference, Sep 18-21, 2012, Columbus, Ohio, USA.
  98. Huang Q., Yang C., 2011. Optimizing Grid Configuration to Support Geospatial Processing – An Example with DEM Interpolation. Computer & Geosciences, and 37(2): 165-176.
  99. 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.
  100. 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.
  101. Yang C., Wu H., Huang Q., Li Z., Li J., Li W., Miao L., Sun M., 2011. WebGIS Performance Issues and Solutions. In Li S., Dragicevic S., and Veenendaal B., eds. Advances in Web-based GIS, Mapping Services and Applications, 121-138. Taylor and Francis.
  102. 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.
  103. Huang Q., Yang C., Nebert D., Liu K., Wu, 2010. Cloud Computing for Geosciences: Deployment of GEOSS Clearinghouse on Amazon’s EC2. In Proceedings of the International Workshop on High Performance and Distributed Geographic Information Systems, ACM SIGSPATIAL 2010, San Jose, CA.
  104. Huang Q., Yang C., Li W., Wu H., Xie J., and Cao Y., 2010. Geoinformation Computing Platforms. In Yang C., Wong D., Miao Q., and Yang R., eds. Advanced GeoInformation Science, pp.79-126. CRC Press.
  1. Wu M., Huang Q.*, Sui T., Peng B., Yu M., 2024. A Remote Sensing Spectral Index Guided Bitemporal Residual Attention Network for Wildfire Burn Severity Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI: 10.1109/JSTARS.2024.3460531
  2. Sui T., Huang Q.*, Wu M.D., Wu M.L., Zhang Z., 2024. BiAU-Net: Wildfire burnt area mapping using bi-temporal Sentinel-2 imagery and U-Net with attention mechanism. International Journal of Applied Earth Observation and Geoinformation, 132:104034. DOI: 10.1016/j.jag.2024.104034
  3. Yu M., Huang Q., Li Z., 2024. Deep learning for spatiotemporal forecasting in Earth system science: a review. International Journal of Digital Earth, 17(1): 2391952. DOI: 10.1080/17538947.2024.2391952
  4. Yang S, Huang Q.*, Yu M., 2024. Advancements in Remote Sensing for Active Fire Detection: A Review of Datasets and Methods. Science of Total Environment, 943: 173273. DOI: 10.1016/j.scitotenv.2024.173273
  5. 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.
  6. Zou B.,Peng B., Huang Q., 2022. 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.
  7. Peng B., Huang Q., and Rao J., 2021. Spatiotemporal Contrastive Representation Learning for Building Damage Classification. 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.
  8. 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. doi: 10.1109/JSTARS.2020.3047677.
  9. 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. Remote Sensing, 11(21), 2492. DOI: 0.3390/rs11212492.
  10. Meng Z., Peng B., Huang Q., 2019. 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.
  11. 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, 40–47. DOI:https://doi.org/10.1145/3356471.3365235.
  12. 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.
  13. Hultquist C., Simpson M., Cervone, 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, ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.
  1. 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.
  2. 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. Download
  3. 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, p.01655515211007724.
  4. 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.
  5. Vongkusolkit J., and Huang Q., 2020. Situational awareness extraction: a comprehensive review of social media data classification during natural hazards. Annals of GIS, DOI: 10.1080/19475683.2020.1817146
  6. 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.
  7. Cao K., Huang Q., 2018. Geo-Sensor for Potential Prediction of Earthquakes: Can Earthquake Be Predicted By Abnormal Animal Phenomena? Annals of GIS, 24(2): 125-138.
  8. Huang Q., Cervone G., and 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.
  9. 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.
  10. 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), ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA, pg.1-6.
  11. Xiao Y., Huang Q., Wu K, 2015. Understanding Social Media Data for Disaster Management. Natural Hazards, 79(3):1663-1679.
  1. Wu M., Huang Q.*, Gao S., Zhang Z., 2023. Mixed Land Use Measurement and Mapping with Street View Images and Spatial Context-Aware Prompts via Zero-shot Multimodal Learning. International Journal of Applied Earth Observation and Geoinformation, 125 (2023): 103591. DOI: org/10.1016/j.jag.2023.103591
  2. Wu M., Liu X., Qin Y., Huang Q.*, 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. DOI: 10.1016/j.compenvurbsys.2023.102008.
  3. Wu M., and Huang Q., 2022. IM2City: Image Geo-localization via Multi-modal Learning. In Proceedings of 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022). ACM, Seattle, WA, USA.
  4. 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. Download
  5. 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, pp.1-23.  DOI: 10.1080/19475683.2022.2026471. Download
  6. Liu X., Huang Q., Gao S. and Xia J., 2020. 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: 1016/j.compenvurbsys.2020.101551. Download
  7. 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), p.683.
  8. Liu X., Huang Q., Gao S., 2019. Exploring the Uncertainty of Activity Zone Detection Using Digital Footprints with Multi-Scaled DBSCAN. International Journal of Geographical Information Science, 33(6):1196-223. DOI: 10.1080/13658816.2018.1563301. Download
  9. Huang Q., 2017.  Mining Online Footprints to Predict User’s Next Location. International Journal of Geographic Information Science, 31(3): 523-541.  Download
  10. Wong D., Huang Q., 2017. “Vote with Their Feet”: Delineating the Sphere of Influence Using Social Media Data. International Journal of Geo-Information, 2017, 6 (11), 325. Doi: 10.3390/ijgi6110325.
  11. 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. Download
  12. Huang Q., Li Z., Li J., Chang C., 2016. Mining Frequent Trajectory Patterns from Online Footprints. In Proceedings of the 7th International ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS) 2016, ACM SIGSPATIAL 2016, Oct 31-Nov 03, Burlingame, CA, USA, 1-8.
  13. 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. Download
  14. 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.
  1. 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.
  2. Huang Q., Li J., Li Z., 2018. A Hybrid Cloud Platform Based on Multi-sourced Computing and Model Resources for Geosciences. International Journal of Digital Earth. 11(12): 1184 – 1204.
  3. 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.
  4. 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.
  5. Li R., Feng W., Wu H., Huang Q., 2017. A Replication Strategy for a Distributed High-Speed Caching System Based on Spatiotemporal Access Patterns of Geospatial Data. Computers, Environment and Urban Systems, 61: 163-171.
  6. Ye X., Huang Q., Li W., 2016. Integrating Big Social Data, Computing and Modeling for Spatial Social Science. Cartography and Geographic Information Science, 43(5): 377-378.
  7. 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.
  8. 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.
  9. Huang Q., Xu C., 2014. A Data-Driven Framework for Archiving and Exploring Social Media Data, Annals of GIS, 20(4): 265-277.
  10. 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: 1016/j.compenvurbsys.2014.06.004.
  11. 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.
  12. Gui Z., Yang C., Xia J., Huang Q. et al., 2014. A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services. PLoS ONE, 9(8): e105297. doi: 1371/journal.pone.0105297.
  13. 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.
  14. 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.
  15. 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.
  16. 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, 59 (2013): 78-89.
  17. Li Z., Yang C., Sun M., Li J., Xu C., Huang Q., Liu K., 2013. A High Performance Web-Based System for Analyzing and Visualizing Spatiotemporal Data for Climate Studies. In Proceedings of International Symposium on Web and Wireless Geographical Information Systems, pp. 190-198. Springer, Berlin Heidelberg.
  18. 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, 4(4): 1069-1085.
  19. Huang Q., Xia J., Yang C., Hassan, Chen S., 2012. An Experimental Study of Open-Source Cloud Platforms for Dust Storm Forecasting. In Proceedings of the ACM SIGSPATIAL 2012, Nov 6-9, Redondo Beach, CA, pp.534-537.
  20. Li J., Jiang Y., Yang C., Huang Q., 2012. Visualizing 3D/4D Environmental Big Data Using Many-core Compute Unified Device Architecture (CUDA) and Multi-core Central Processing Unit (CPUs). In Proceedings of the MAT4GIS workshop, the 7th GIScience International Conference, Sep 18-21, 2012, Columbus, Ohio, USA.
  21. Huang Q., Yang C., 2011. Optimizing Grid Configuration to Support Geospatial Processing – An Example with DEM Interpolation. Computer & Geosciences, and 37(2): 165-176.
  22. 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.
  23. 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.
  24. 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.
  25. Huang Q., Yang C., Nebert D., Liu K., Wu, 2010. Cloud Computing for Geosciences: Deployment of GEOSS Clearinghouse on Amazon’s EC2. In Proceedings of the International Workshop on High Performance and Distributed Geographic Information Systems, ACM SIGSPATIAL 2010, San Jose, CA.