Rapid Spatial Analytics
Cynthia Yu, P2.01
My research mainly focuses on developing an automatic method to reconstruct 3D building models from segmented point cloud data which can be extracted from terrestrial laser scanning devices.
I recently have completed preparing and analysing the DXF files which store the segmented data for test purpose, and also building a framework for parsing the DXF file, converting it into 3D shapes through the triangular mesh merge method, and finally visualising the result.
More recently, I started to work on a grammar-based method to reconstruct a semantically structured building model based on structured geometry results from the segment data sets. The semantic model interprets the geometry structures with building information, which can be further exported as BIM models. This work is planned to contribute a paper to the top-tier conference held in Sydney in December.
Apart from research work, I am also very much looking forward to attending the annual CRCSI conference held in Melbourne in November, as it will be the first time I get the chance to travel to the east part of Australia.
nic donnelly, P1.02
For my PhD, I am researching how Synthetic Aperture Radar (SAR) data can be incorporated into a national geodetic datum or reference frame. As part of this, I led a piece of work, which included CRCSI industry partners, to research and document a framework for a modernised datum.
The outcome of this research was that a system consisting of two reference frames, one global and one local, was recommended as providing a solution that meets the greatest range of user needs. The global frame facilitates the use of technology such as GNSS, while the local frame provides coordinates that are time-invariant.
A paper was presented to the Australia/New Zealand Permanent Committee for Geodesy (PCG), which subsequently recommended that a two-frame system be adopted for Australia. A refined version of the paper was recently accepted for publication in the International Association of Geodesy Symposium Series.
daniel hogg, P4.45
Natural hazard disasters are stressful and traumatic events that can contribute to the development of adverse mental health outcomes. Increased migration is commonly observed after such events and can put even more pressure on affected and traumatized populations.
In a recent study, we examined the role of relocation on mood and anxiety disorders and found that Christchurch residents relocating after the 22nd February 2011 earthquake showed higher mood and anxiety disorder rates than stayers in the short-term aftermath of the disaster. On the other hand, within-city movers showed an improvement up to 18 months post-disaster compared to out-of-city movers and stayers. Furthermore, mood and anxiety disorders were more prevalent among severely affected residents indicating a dose-response relationship.
In a next step, we will assess the effects of different community disruptions and community resilience on mood and anxiety.
hamish mcnair, P3.02
I recently attended a workshop run by the European Network Exploring Research into Geospatial Information Crowdsourcing on Crowdsourced Geographic Information and Citizen Science. The diverse range of participants and instructors (both in terms of expertise and nationality) provided a unique perspective on this rapidly developing field and how I can contribute to its progress through my research into trust of such data.
My focus is on not only the trustworthiness of spatial data at collection but how this trust is modified depending on its purpose, methods of analysis applied and presentation. One of the main themes raised was not only how to assess such trust but also how to convey what this actually means to potential users – whatever their background and experience.
Also, gaining an insight into European organisation and methodologies gave me an appreciation for the openness of Australia and New Zealand’s attitude towards spatial data use and distribution, underlining how important it is that we ensure such valuable resources can be utilised by all.