Sheikh Fakhar Khalid

A spatial scientist with a robust background in computational intelligence and an active role in advancing geospatial 3D computer vision. My expertise spans Spatial Cognition, AI within Geospatial Sciences, Spatial Analytics, Graphs, Computer Vision, and Meta-heuristic Algorithms. I specialise in spatial statistics, data analytics, and spatial algorithm development, including semantic segmentation of point clouds from UAV and LiDAR sources.

As Head of Data at Sensat, I lead both the data engineering and AI research teams, managing a diverse portfolio of geospatial data products. I bring a wealth of experience in spatial reasoning and environmental modelling, as well as hands-on work with flight autonomy for Unmanned Aerial Platforms. My passion for public speaking and high-paced tech start-ups is matched only by my enthusiasm for AI, spatial data (2D or 3D), and a good cup of coffee.

1. Hu, Q., Yang, B., Khalid, S., Xiao, W., Trigoni, N., & Markham, A. (2021). Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4975–4985.

2. Hu, Q., Yang, B., Khalid, S., Clark, R., Xiao, W., Guo, Y., Leonardis, A., Trigoni, N., & Markham, A. (2021). The 1st Challenge on Large Scale Point-cloud Analysis for Urban Scenes Understanding. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCV Workshops). https://urban3dchallenge.github.io/

3. Hu, Q., Yang, B., Khalid, S., et al. (2022). SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds. International Journal of Computer Vision, 130, 316–343. https://doi.org/10.1007/s11263-021-01554-9

4. Hu, Q., Yang, B., Khalid, S., et al. (2022). The 2nd Challenge on Large Scale Point-cloud Analysis for Urban Scenes Understanding (Urban3D). Proceedings of the European Conference on Computer Vision Workshops (ECCV Workshops). https://urban3dchallenge.github.io/2022/

5. Hu, Q., Yang, B., Khalid, S., et al. (2023). The 3rd Challenge on Large Scale Point-cloud Analysis for Urban Scenes Understanding (Urban3D). Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCV Workshops). https://urban3dchallenge.github.io/

6. Khalid, S., & Ferreira, S. (2015). Evaluation of the Potential of Friction Surface Analysis in Modelling Hurricane Wind Damage in an Urban Environment. Natural Hazards, 77(2), 1111–1124. https://doi.org/10.1007/s11069-015-1644-5

7. Khalid, S., & Ferreira, S. (2008). Hazard and Risk Assessment from Hurricane Ivan (2004) in Grenada Using Geographical Information Systems and Remote Sensing. Journal of Maps, 4(1), 219–234. https://doi.org/10.1080/17445647.2008.9710827

Khalid, S. (2021). SensatUrban: An Urban-Scale Photogrammetric Point Cloud Dataset for Semantic Segmentation. Technical Report, Sensat.

• Abstract: Introduces the SensatUrban dataset, an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points—five times larger than existing datasets. Covers approximately 6 km² from two UK cities, with each 3D point labeled into one of 13 semantic classes such as ground, vegetation, and cars.

2. Khalid, S. (2015–Present). Autonomous UAV Swarms and Broods: Development of Fully Autonomous Micro-Drone Flight Algorithms. Research Report, University of Greenwich.

• Abstract: Details the programming and development of micro-drone flight algorithms for fully autonomous swarm operations. Objectives include eliminating pilot bias and enabling drones to learn efficient sampling methods using Python and Raspberry Pi for communication hubs, along with dead reckoning and predictive analysis for GPS-free indoor navigation.

3. Khalid, S. (2015–2016). Immersive Spatial Visual Analytics: An Oculus Rift-Based Visualization System for Spatial Data Decision Support. Research Report, University of Greenwich.

• Abstract: Describes the development of an Oculus Rift-based visualization system for immersive spatial data analysis and decision-making support. Utilizes Unity Engine and UnityScript, integrating LeapMotion hand gesture controls for interactive data manipulation in a virtual reality environment.

4. Khalid, S. (2012–2015). GE-UZ: Geoinformatics Enabling Sustainable Development in Uzbekistan. Proceedings of the TEMPUS Programme, University of Greenwich.

• Abstract: Covers the development and implementation of the first MSc programme in Geoinformatics in Uzbekistan. Involves academic quality assurance procedures, curriculum development, and teacher training in alignment with the Bologna Agreement and UK quality assurance codes, including budget planning and staff management.

5. Khalid, S. (2012–2014). Geography of Inshore Fishing: Online Cartographic Outputs and Socio-Demographic Analysis. European Union Interreg IVa 2 Seas Programme Report, University of Greenwich.

• Abstract: Discusses consultancy work with Belgian partners on using Google APIs for online cartographic outputs and analyzing socio-demographic and environmental data. Includes contextual analysis of interviews using NVivo to understand sentiments within local fishing communities and organizing a regional conference to disseminate findings.

6. Khalid, S. (2009–2012). CHARM III: Channel Integrated Approach for Marine Resource Management. European Union Interreg IVa Project Report, University of Greenwich.

• Abstract: As GIS lead, managed a multimillion-euro project involving 11 European partners. Delivered four analytical products, including Java-based web mapping solutions, a .NET-based habitat predictor and conservation tool, and a spatio-temporal benthic modelling algorithm in C++. Ensured all spatial outputs were INSPIRE compliant.