Derby Laboratory for Visualization and Spatial Analysis (LVSA)


The inter-College University research laboratory in the College of Engineering and Technology epitomizes Derby University’s interdisciplinary culture. Digital Earth and Big Earth Data are interdisciplinary and complex areas of study by integrating technology-centred and human-centred approaches from the perspective of information system science. The Lab for Visualization and Spatial Analytics (LVSA) conducts research into:

  • Fundamental research questions, both in theoretical and practical aspects, related to processing (the creation, capture, storage and conversion of Big Earth Data from implicit to explicit), mapping, codifying, extracting, visualization and utilising various forms of Big Earth data in firms and organisations;
  • Developing innovative methods, models and tools for the spatial data analytics, innovation and learning capability to help UK enterprises sustain their core Big Earth data-based competencies and competitiveness advantage in the data economy.

Head of Lab

  • Professor Yong Xue – Professor in Computation

Research Interests: Middleware development for Geocomputation on Spatial Information Grid, Aerosol, Digital Earth, High Throughput Computation and Telegeoprocessing


Members List (in Alphabetical Order)

  • Dr Ovidiu Bagdasar – Lecturer in Mathematics

Research Interests: Optimisation, Mathematical Modelling, Recurrent Sequences and Their Applications, Counting Problems and Combinatorics


  • Ms Haixia Bi – Postdoctoral Research Fellow

Research Interests: Big Earth Data, Artificial Intelligence, Remote Sensing

  • Ms Roisin Hunt – Lecturer in Game Modelling and Animation

Research Interests: 3D Modelling, Animation & Computer Graphics, CAD/CAM


  • Mr Wayne Rippin – Senior Lecturer in Computer Science

Research Interests: Software System Development; Computer Programming Language


  • Mr Richard J Self – Senior Lecturer in Governance of Advanced and Emerging Technologies

Research Interests: Governance impact of Emerging Information Technologies, such as AI, Neural Networks, Machine Learning, Blockchain, Visualisation of Data Veracity Analysis


  • Mr Dave Voorhis – Senior Lecturer in Computer Science

Research Interests: Database Systems, Database Languages, Data Analysis, Programming languages


  • Dr Amanda Whitbrook – Senior Lecturer in Artificial Intelligence

Research Interests: Artificial Intelligence


  • Dr Chris Windmill – Senior Lecturer in Computer Game Programming

Research Interests: Internet technology, Computer Programming Language


  • Dr Xiaojun Zhai – Lecturer in Network Engineering

Research Interests: Digital forensics, Information security, and IT security management


Grants and Projects

LVSA has extensive research experience following their involvement in a large number of funded projects by the EU, ESA, Teaching Company Schemes, Knowledge Transfer Partnership and private companies. The Laboratory’s goals are:

  1. To design and develop computer software for the analysis and graphic display of spatial data;
  2. To distribute the resulting software to governmental agencies, educational organizations and interested professionals; and
  3. To conduct research concerning the definition and analysis of spatial data and process.

While Grid computing has been a prominent technique to tackle computational issues in Big Data, little work has been done on making Grid computing adapted to remote sensing applications. The aim of one of research projects is to develop a collaborative environment “Service-Oriented Remote Sensing Information Service Grid Platform (SORSIS)”. The platform consists of a web portal, a quantitative remote sensing model base and a dynamic quantitative remote sensing processing workflow engine. It will provide one-stop-service for remote sensing applications based on “Grid processing on demand”. In this platform, we intended to demonstrate the usage of Grid computing for quantitative remote sensing retrieval applications. The novel technologies developed for SORSIS are:

  • Based on the high-throughput computing grid, the SORSIS enables a workflow management system for data placement. A novel architecture for the remote sensing Grid workflow is designed. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modelling, reconstruction and implementation of large-scale complex applications of remote sensing science.
  • The accompanying unified data-and-computation-schedule algorithm helps load balancing between and within workflow steps. A workload estimation and task partition algorithm was developed, and it executes a generic remote sensing algorithm in parallel over partitioned datasets, which is embedded in a middleware framework for the SORSIS.

Current Funded and Completed Projects

  • Aerosol_CCI – the Climate Change Initiative (GMECV) (Co-PI, 2014 – 2018, European Space Agency – ESA)
  • U-Alert (Co-PI, 2016, Natural Environment Research Council – NERC)
  • Monitoring and Assessment of Regional air quality in China using space Observations, Project Of Long-term Sino-European co-Operation (Co-PI, 2014 – 2017, European Commission: 7th Framework Programme for Research – FP7)
  • AMASED: Access methods for analysing sensitive data (JISC funded project jointly with University of Bristol, Content Mine, British Library, F1000 Research, 2015)
  • Non-linear dynamics of the remotely-sensed atmospheric data and modelling; Implications to Climate & Earth system science; Case studies for Athens (Greece) and Beijing (China) (Co-PI, 2012 – 2016, ESA – NRSCC Dragon Cooperation Programme)
  • Air quality Monitoring and Forecasting in China (Co-PI, 2012 – 2016, ESA – NRSCC Dragon Cooperation Programme)

PhD Students and Research Topics

  • Patrick Merritt, “3D Visualization Modelling for Visualization Analytics Platform for Big Earth Data”

Supervision team: Dr Chris Windmill, Professor Yong Xue, Mr. Robert Berry

  • Seyedroohollah Hosseini, “Artificial Intelligent Dynamic Workflow Analysis for Visualization Analytics Platform for Big Earth Data”

Supervision team: Dr Amanda Whitbrook, Professor Yong Xue, Mr. Dave Voorhis

  • Richard Crossley, “How Can Energy Efficiency in High Performance Computing be Optimised Without Reducing Quality of Service?”

Supervision team: Professor Yong Xue, Professor Lu Liu

  • Priyanthi Dassanayake, “The Influence of Simulation in the Design and Development of Industries and Products”

Supervision team: Dr Amanda Whitbrook, Dr. Jose Manuel Andrade, Professor Yong Xue

  • Jack Fisher, “Investigate methods for designing augmented contexts for leaning that support and scaffold architectural understanding using mixed reality environments”

Supervision team: Dr. Amanda Whitbrook, Mr. Dave Voorhis, Professor Peter Larcombe

Selected Publications

  • Jia Liu, Kaijun Ren, Yong Xue, Junqiang Song, Christopher Windmill, and Patrick Merritt, 2018, High Performance Time Series Quantitative Retrieval from Satellite Images on a GPU Cluster. Information Sciences, (submitted) (Impact Factor: 4.832)
  • Yong Xue, Xingwei He, Gerrit de Leeuw, Linlu Mei, Yahui Che, Wayne Rippin, Jie Guang, Yincui Hu, 2017, Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe. Remote Sensing of Environment, 198, Pages 471 – 489. (DOI:10.1016/j.rse.2017.06.036) (Impact Factor: 6.265)
  • Yanqing Xie, Yong Xue, Yahui Che, Jie Guang, Linlu Mei, Dave Voorhis, Cheng Fan, Lu She, and Hui Xu, 2017, Ensemble of ESA/AATSR AOD products based on the likelihood estimate method with uncertainties. IEEE Transactions on Geoscience and Remote Sensing. DOI:10.1109/TGRS.2017.2757910 (In press) (IF: 4.942)
  • Bagdasar, N. Popovici, Extremal properties of generalized convex vector functions, pp23 (2017)
  • Lu She, Linlu Mei, Yong Xue, Yahui Che, Jie Guang, 2017, SAHARA: a Simplified AtmospHeric correction AlgoRithm for Chinese gAofen data: 1. Aerosol algorithm. Remote Sensing, 9(3), 253; (doi:3390/rs9030253) (Impact Factor 3.036)
  • Jia Liu, Longli Liu, Yong Xue, Jing Dong, Yincui Hu, Richard Hill, Jie Guang and Chi Li, 2017, Grid Workflow Validation Using Ontology-Based Tacit Knowledge: A Case Study for Quantitative Remote Sensing Applications, Computers and Geosciences, 98, Pages 46–54. ( (IF: 2.474)
  • Zhai, Xiaojun, et al, (2016) ‘MLP Neural Network Based Gas Classification System on Zynq SoC’, IEEE Access, Vol. 4, pp. 8138 – 8146, doi: 10.1109/access.2016.2619181. (IF: 1.249)
  • Zhai, X., Vladimirova, T. (2016) ‘Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration’, Journal of Aerospace Information Systems, 13 (1):46 (IF: 0.302)
  • Che, Y., Xue, Y., Mei, L., Guang, J., She, L., Guo, J., Hu, Y., Xu, H., He, X., Di, A., and Fan, C.: Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China, Chem. Phys., 16, 9655-9674, doi:10.5194/acp-16-9655-2016, 2016. (IF: 5.114)
  • Jia Liu, Dustin Feld, Yong Xue, Jochen Garcke, Thomas Soddemann, Peiyuan Pan, 2016, An efficient geosciences workflow on multi-core processors and GPUs: a case study for Aerosol Optical Depth retrieval from MODIS satellite data. International Journal of Digital Earth, 9, Pages 748-765. (doi: 10.1080/17538947.2015.1130087) (IF: 3.291)
  • Liu Longli, Xue Yong, Guang Jie, Liu Jia, 2016, Remote Sensing Information Service Grid Node and the Research of Data Compression and Task Allocation. Remote Sensing Technology and Application, Vol. 31(2): 247-254. (DOI:10.11873/j.issn.1004-0323.2016.2.0247)
  • Jia Liu, Yong Xue, Dominic Palmer-Brown, Ziqiang Chen, Xingwei He, 2015, High-throughput Geocomputational Workflows in a Grid Environment. IEEE Computer. Vol. 48, Issue 11, Pages 70-80. (DOI: 10.1109/MC.2015.331) (IF: 1.443)
  • Longli Liu, Yong Xue, Jie Guang, Jia Liu, 2015, Description of an ontology-based remote sensing model service with an integrated framework environment for remote sensing applications. Remote Sensing Letters, Volume 6, Issue 10, pages 804-813. (DOI: 10.1080/2150704X.2015.1082207). (IF: 1.573)
  • Bagdasar, N. Popovici, ‘Local maximum points of explicitly quasiconvex functions’, pp9 (2015)
  • Liu, D. Feld, Y. Xue, J. Garcke, and T. Soddemann, 2015, “Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth from Satellite Data: Implementation, Performance, and Energy Efficiency”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, Page(s):2306 – 2317. (doi: 10.1109/JSTARS.2015.2438893) (IF: 3.026)
  • Self, R. and Voorhis, D. (2014) ‘Designing Big Data Analytics Undergraduate and Postgraduate Programmes for Employability’, Proceedings of Big Data and Analytics EDCON, Las Vegas, USA. 25-26 October.
  • Voorhis, D. and Thompson, T. (2014) ‘Planning in the cloud: Massively parallel planning’ in ACM 7th International Conference on Utility and Cloud Computing (UCC), London, England, 8-11 December.
  • Bagdasar, P. J. Larcombe, ‘On the characterization of periodic Horadam sequences’ pp22 (2014).
  • Yong Xue, Ziqiang Chen, Hui Xu, Jianwen Ai, Shuzheng Jiang, Yingjie Li, Ying Wang, Jie Guang, Linlu Mei, Xijuan Jiao, Xingwei He, Tingting Hou, 2011, A High Throughput Geocomputing System for Remote Sensing Quantitative Retrieval and a Case Study. International Journal of Applied Earth Observation and Geoinformation, 13 (1), pp.902–911. (DOI:10.1016/j.jag.2011.06.006) (IF: 3.93)
  • Yong Xue, Dominic Palmer-Brown, Huadong Guo, 2011, The Use of High Performance and High Throughput Computing for the Fertilization of Digital Earth and Global Change Study. International Journal of Digital Earth, 4, No. 3, pp185-210. (DOI: 10.1080/17538947.2010.535569) (IF: 2.292)
  • Yong Xue, Jianwen Ai, Wei Wan, Huadong Guo, Yingjie Li, Ying Wang, Jie Guang, Linlu Mei, and Hui Xu, 2011, Grid-Enabled High-Performance Quantitative Aerosol Retrieval from Remotely Sensed Data. Computers & Geosciences, vol. 37, pp.202-206. (DOI:10.1016/j.cageo.2010.07.004). (IF: 3.93)
  • Yong Xue, Jianwen Ai, Wei Wan, Yingjie Li, Ying Wang, Jie Guang, Linlu Mei, Hui Xu, Qiang Li, and Linyan Bai, 2010, Workload and Task Management of Grid- Enabled Quantitative Aerosol Retrieval from Remotely-Sensed Data. Future Generation Computer Systems, 20, pp.590-598. (DOI: 10.1016/j.future.2009.11.003) (IF: 3.997)
  • Yong Xue, Wei Wan, Yingjie Li, Jie Guang, Linyian Bai, Ying Wang and Jianwen Ai, 2008, A Data Intensive Scientific Computing Framework for Quantitative Retrieval of Geophysical Parameters using Satellite Data, IEEE Computer, Vol. 41(4), pp.33-40. (IF: 1.755)
  • Cheng, Jicheng, Guo, Huadong, and Xue, Y., Digital Earth, Scientific Press, 2007.