Dr. Xing joined the school of Engineering, Newcastle University, as the lecturer in geospatial analysis in 2018. He has obtained PhD in Geographic Information Science from McGill University’s Department of Geography in 2018. His PhD research in urban land use change has been recognized by the Rathlyn GIS Award. Before joining Newcastle University, he conducted smart city research at Laval University, Canada, with the support of MITACS fellowship. His research has been published in top Geographic Information Science/ Remote Sensing journals, and presented at various conferences.

Dr. Xing actively employs his research to solve real-world problems. In the ESRI APP challenge 2015, he led his team to a second place finish by developing an online data fusion platform to integrate multiple urban facilities, services, and demographic datasets. To alleviate urban air pollution and traffic jams, he also developed a remote sensing-based parking space finding APP that made it to the semi-final in McGill University’s Dobson Cup (start-up) competition in 2017.

Programme Committee

GISRUK 2019

AAG 2019: Symposium on Frontiers in Geospatial Data Science: Geospatial Artificial Intelligence: Machine Learning and Deep Learning

Looking for Prospective Graduate Students/ Visiting Scholars

I am always looking for enthusiastic and self-motivated PhD/Msc students with strong background in geographic information science or computer science to work on smart city and GeoAI projects. Please feel free to contact me with your CV, a brief research statement, and transcripts. 

I am also happy to discuss and provide help in fellowship application. 

If you are from UK/EU, please take a look a our CDT.

Chinese students, please apply for our CSC scholarship. 

Call for Papers

Special Issue of Remote Sensing: Deep Learning Approaches for Urban Sensing Data Analytics (Closing Date December 31st, 2019)

Dr. Xing’s research shifts cities smarter. His current research focuses on geospatial deep learning algorithms and geospatial cyber-infrastructure for urban data analytics. He investigates deep learning algorithms to achieve better decision-making for smart cities. Convolutional neural network and recurrent neural network have been combined to explore various sensing datasets.  In geospatial cyber-infrastructure research, he continuously explores high performance computing techniques such as cloud computing and real-time computing frameworks.

Research Interests:

• Smart Cities
• Machine Learning 
• Big Data
• CyberGIS
• Land Use/Cover Change Detection
• Remote Sensing 
• Volunteered Geographic Information
• Spatio-Temporal Modelling
• Urban Science
• Climate Change

Previous projects:

Team member of Quebec City, Smart Cities Challenge, Canada.

Research Assistant, Geothink Canada.

Research Assistant, Data Rescue: Archives and Weather, McGill University, Canada.

• Chen L, Weng T, Xing J, Pan Z, Yuan Z, Xing X, Zhang P. A New Deep Learning Network for Automatic Bridge Detection from SAR Images Based on Balanced and Attention Mechanism. Remote Sensing 2020, 12(3), 441.
• Chen L, Tan S, Pan Z, Xing J, Yuan Z, Xing X, Zhang P. A New Framework for Automatic Airports Extraction from SAR Images Using Multi-level Dual Attention Mechanism. Remote Sensing 2020, 12(3), 560.
• Chen L, Cui X, Li Z, Yuan Z, Xing J, Xing X, Jia Z. A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration. Sensors 2019, 19(11), 2479.
• Zhang P, Chen L, Li Z, Xing J, Xing X, Yuan Z. Automatic Extraction of Water and Shadow from SAR Images Based on a Multi-Resolution Dense Encoder and Decoder Network. Sensors 2019, 19(16), 3576.
• Xing J, Sieber RE, Roche S. Rethinking Spatial Tessellation in an Era of the Smart City. Annals of the American Association of Geographers 2020, 110(2), 399-407.
• Xing J, Sieber RE, Caelli T. A scale-invariant change detection method for land use/cover change research. ISPRS Journal of Photogrammetry and Remote Sensing 2018, 141, 252-246.
• Xing J, Sieber RE. Propagation of Uncertainty for Volunteered Geographic Information in Machine Learning. In: 10th International Conference on Geographic Information Science. 2018, Melbourne, Australia.
• Xing J, Sieber RE. A land use/land cover change geospatial cyberinfrastructure to integrate big data and temporal topology. International Journal of Geographical Information Science 2016, 30(3), 573-593.
• Xing J, Sieber RE. Geospatial CyberInfrastructure in Land Use/Cover Change Research. In: NSF Workshop on Geospatial Data Science in the Era of Big Data and CyberGIS. 2016, University of Illinois.
• Xing J, Sieber RE. Scale Verification in GyberGIS: A Case Study in Road Change Detection. In: The Third International Conference on CyberGIS and Geospatial Data Science. 2016.
• Xing J, Sieber RE. Sampling based image splitting in large scale distributed computing of earth observation data. In: 2014 IEEE Geoscience and Remote Sensing Symposium. 2014, Quebec, Canada: IEEE.
• Xing J, Sieber RE, Kalacska M. The challenges of image segmentation in big remotely sensed imagery data. Annals of GIS 2014, 20(4), 233-244.