Since May we have been lucky enough to have Emily Dautenhahn, a Math major from the University of Kentucky embedded with us for an internship. As her time with us draws to a close she has reflected on her trip with the following blog post. We wish her all the best in the future. She was very quick to pick up on elements related to 3d point cloud analysis and I’m sure was pleased to learn how do many of our geospatial applications are built on some heavy mathematics – Collinearity equation anyone? .

 

Over to you Emily..

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A little over eight weeks ago, I stepped off of a plane and into New Zealand, my first time traveling outside of the United States. The excitement I felt about my journey had been had been sapped by many long hours in airports and on planes, to be replaced by terror at finding myself alone, in a completely unfamiliar environment. It occurred to me that perhaps this had been a terrible idea.

Having just finished my second of four years at the University of Kentucky, where I am pursing degrees in math and history, I had decided to go abroad this summer in hopes of learning something and to avoid sitting around at home. After choosing an internship program in New Zealand, I was delighted to hear that I had been placed with the Unmanned Aerial Vehicle (UAV) team at the Auckland University of Technology (AUT). This seemed like a fantastic opportunity, and while there have definitely been rough patches, I am very glad that I came here.

Over the eight weeks, I have done a little bit of everything, from image processing to collecting samples of trees out in the field to working with millions of data points. All of this has been a sharp turn away from what I have been learning in class. Having become accustomed to the theoretical, being thrown into the deep end of a field that is so clearly applied was a bit of a struggle. However, the underlying question of how things work was still very much present. It was a bit refreshing to be able to see the immediate real world applications of what I was learning.

For example, most of what I have been looking at has involved attempting to create digital elevation models (DEMs) and canopy height models out of point clouds generated from both UAVs and Light Ranging and Detection (LIDAR). The applications of such models are quite extensive, including hydrology and measurements of canopy cover. The models and data I’ve been working with all connect to features in the real world, although, as I’ve seen, they can be far from perfect representations.

While mathematics has not been at the forefront of what I have done here, there are plenty of equations and algorithms lurking in the background that are crucial to working with data collected through remote sensing. This has opened up the door of applied math, as well as made me aware of a wide range of environmental issues. Ultimately, I dove into a topic I knew next to nothing about and am leaving with a much better understanding and appreciation of it. As I prepare to once again step on a plane, this time headed back to the familiar, I know that my time here will not be easy to forget.