A Question of Geospatial: Q&A Session with TCarta's Technical Specialist, Gareth Bennett

  • Gareth began his career in remote sensing and geospatial sciences shortly after graduating with a degree in Oceanography. After joining TCarta in October 2016, Gareth has transitioned into the role of Technical Specialist, where he assesses, researches, troubleshoots and streamlines all technology used by TCarta in the creation and delivery of satellite-derived products to our clients. 

    How did your academic work lead to your current position? 

    My academic work centered around Oceanography which is the study of the ocean and its chemical, biological, geological ,and physical aspects. My focus in my last year was the hydrographical/physical aspects of the ocean; this was due to the exposure I got during fieldwork. As a result, I was introduced to remote sensing the oceans from space; alongside seafloor surveying from a boat. I was also introduced to coding at this time; becoming familiar with both R and Matlab during my time at university. 

    What excites you about your work?

    Everything! From my co-workers to the work we do day in and day out. TCarta is an organisation who’s staff are experienced and motivated across multiple disciplines. We work all across the globe and so each day is completely different to the next. As a result, even though I come from a marine background,  I am able to learn something new each day and expand my knowledge into disciplines I knew little about before working for TCarta.  

    What is a typical day like as a Technical Specialist? If typical at all!

    A typical day for me starts with catching up on emails and planning and prioritising my tasks for the day ahead. I mainly work on internal projects;  which could be Research and Development based - or Workflow Improvement. Alongside this, I also work on external projects for clients which involves Satellite Derived Bathymetry, Land Use, and Land Cover Mapping.

    Why is machine learning important to integrate into your work?

    Automation of manual processes as well as machine learning is something that will make TCarta more efficient now and in the future. As data volumes increase we need to take full advantage of available computing to produce accurate products/outputs in minimal time - negating the need for human interaction thereby reducing the probability of “human error”.

    Which professional resources would you say, “I can’t live without it!”

    While maybe not that "professional", StackExchange/StackOverflow at times can be useful for putting yourself on the right track when solving problems. Also a fair amount of the packages I use in Python/R have very good documentation that I would be lost without.

    What bit of advice would you give someone who wants to succeed in a geospatial science profession?

    My first piece of advice would be to learn a programming language useful to geospatial science, whether that be R or Python at a basic level. I started in R and now know both R and Python. I suggest either or - as both languages have plenty of packages centered on geospatial science that help with the work TCarta does. My second would be to have a basic understanding of the different geospatial software that is available in the field. My third would be the world of geospatial science is large, and encompasses a staggering number of disciplines. A broad range of knowledge within the varying disciplines will enable oneself to do/feed into lots “of the work that TCarta does, rather than a few.

    And finally, How do you feel your role contributes to TCarta’s success?

    By automating, optimising and streamlining TCarta's technical function, I am able to optimise and improve the delivery and quality of service to the people most important to us - our clients! There is much value in the automation of workflows, and by harnessing the power of machine learning, the latest in research and development techniques, and AI, I am able to make a significant contribution to the improvement of our overall delivery and product mechanism.