EGU this year was a bittersweet affair, as I actually didn’t make the conference myself, despite having two posters presented on my behalf. I enjoy EGU, but this year my aim is to get to a few new conferences, and having already attended the amazing big data from space conference (BiDS) in Munich in February, I’m hungry to branch out as much as possible. Also on the agenda this year is FOSS4G (I have always wanted to go!) and RSPSoc’s conference in Oxford (this is one I think I will go to every year).
That being said, I did still submit two abstracts, both for posters sessions, with colleagues
of mine presenting on my behalf. The first was another extension of my PhD work, which focused principally on image quality of data collected in the field for use in photogrammetric work and it’s effect on the accuracy and precision of photogrammetric products.
This extension used new innovations within the field to further dive into this relationship, by using Mike James’ precision maps (James et. al 2017). In essence it investigates how stable sparse point clouds are when systematically corrupted with noise (in all of the camera positions, parameters and points within the cloud). His research tries to refine a big unknown within bundle adjustment using structure-from-motion, how do we account for variability in the precision of measurement when presenting results. Due to bundle adjustment’s stochasticity, we can never guarantee that out point cloud accurately reflects real life, but by simulating this sensor variation, we can get an idea of how stable this is.
Pdf version available here
In all, the research points to the fact that compressing data is generally a bad thing, causing point clouds to be relatively imprecise and inaccurate when compared with uncompressed data. It would be interesting to extend this to other common degradations to image data (blur, over/underexposure, noise) to see how each of those influences the eventual precision of the cloud.
Secondly, I submitted a poster regarding a simple app I made to present Sentinel2 data to a user. This uses data from an area in Greece, and geoserver to serve the imagery behind a docker-compose network on an AWS server. It’s very simple, but after attending BiDs, I think there is an emerging niche for delivery of specific types of data rapidly at regional scales, with a loss of generality. Many of the solutions at the BiDS were fully general, allowing for arbitrary scripts to be run on raw data on servers – something comparable to what Sentinel-hub offer. By pruning this back, and using tools like docker-compose, we can speed up the spin-up and delivery of products, and offer solutions that don’t need HPCs to run on.
Sample of the app
Lastly, I’ve simplified my personal website massively in an attempt to declutter. I’ve just pinched a template from Github in order to not sink too much time into it, so many thanks to Ryan Fitzgerald for his great work.
That’s all for now, I’ll be writing about KisanHub in the next blog!