I am always happy to hear from students who would like to do a PhD or MSc research project with me, fellow researchers who would like to collaborate on proposals, and industry seeking to collaborate on a research project with commercialisation focus. Please contact me. For students, please also see information on funding options under postgraduate vacancies.
Please also see my publication list.


Horizon2020 project mapKite.

BigSkyEarth EU COST action.
Substitute MC for Ireland.
Big Sky Earth COST action


Crowd-Sourced Urban Geo Data for Disaster Management

This project makes a contribution in the area of natural disaster management by designing a system that intelligently manages crowd-sourced urban geographic data.
Recent natural disasters have created an unprecedented awareness for the importance of disaster management, specifically within an urban environment. 

Image from Reddit, Mapped

Much grief has been caused in the after math of recent natural catastrophes and a general feeling that despite the masses of data available, local authorities have been incapable of successfully planning evacuation strategies.
At the same time a myriad of public sources are providing free geographic content on a daily basis. This project aims to harvest these new opportunities and provide a framework that will proactively and intelligently assist disaster management in urban environments.

Context Merge from Crowd-Sourced Spatial Data

This project makes a contribution in the area of aggregating and merging crowd-sourced information in order to integrate text as well as spatial data layers. The recent decade has seen an unprecedented surge in publicly available crowd-sourced data and even large companies, such as Google have started to embrace the potential in their Google maps traffic information feature. Crowd-sourced projects have enjoyed a wide recognition and utilisation since having been mentioned in as such for the first time around 2006 in Wire magazine. It describes the phenomenon of a group of people of the general public providing resources and time in order to accomplish a project.

Same feature, different representation

Nearly in parallel, we see that service providers of traditionally only text-based information carriers, such as twitter, have started to incorporate location to their feeds. On one side this novel opportunity to harness new sources of crowd-sourced spatial information is promising. On the other side, the biggest challenge lies in merging spatial information that references the same location or spatial structure, but originates from heterogeneous data sources in order to perform meaningful analysis. This project bridges this gap by designing novel techniques for spatial context merge.

Spatial Data Analytics

Lineage, positional accuracy, attribute accuracy, completeness and semantic accuracy are just some of the quality assessment dimensions used to determine the quality of spatial data. Naturally, cartographers have been traditionally most occupied with the quality of spatial data. 
More recently though, most likely through the relatively novel spatial data acquisition, such as Light Detection and Ranging (LiDAR), spatial data quality is an issue that now interests a myriad of disciplines. Being enveloped as a a subset of the recent big data explosion, the increasing ease of using and providing spatial data, spatial data statistics are an increasingly important area of research. For example geographically weighted regression (GWR) is one of the techniques used to explain relationships between variables that otherwise cannot be explained with global models. 
This research harnesses the emergence of multiple spatial data sources for meaningful statistical data analysis.

Storage And Indexing of Large Point Cloud Data

In recent years, three-dimensional (3D) data has become increasingly available, in part as a result of significant technological progresses in Light Detection and Ranging (LiDAR). 

Point Cloud, Dublin

LiDAR provides longitude and latitude information delivered in conjunction with a GPS device, and elevation information generated by a pulse or phase laser scanner, which together provide an effective way of acquiring accurate 3D information of a terrestrial or manmade feature. The main advantages of LiDAR over conventional surveying methods lie in the high accuracy of the data and the relatively little time needed to scan large geographical areas. LiDAR scans provide a vast amount of data points that result in especially rich, complex point clouds. Spatial Information Systems (SISs) are critical to the hosting , querying, and analysing of such data sets. Feature-rich SISs have been well-documented. 

TIN, Dublin

However, the implementation of support for 3D capabilities in such systems is only recently been addressed. And large point cloud data is not their main focal point. This project aims to overcome shortcomings of current technology and provides support for storing, querying and analysing LiDAR data without the need of Digital Elevation Models (DEMs) or Triangular Irregular Networks (TINs), but harvesting the information in its point cloud nature.