Writing blues

After having been ill the last week and a half I’m currently trying to get back into the swing of writing, which I find is largely the hardest part of research where really it doesn’t/shouldn’t need to be. One thing in particular I find very difficult is starting – I often pore over the first words/sentence for a very long time when I do sit down to write.

One forward step I’ve come to in an attempt to mitigate this is to give myself as many opportunities as possible to start writing. While obviously this could involve carrying a pen and paper around everywhere and waiting for inspiration to hit, I think the practicalities of translating esoteric squiggles and keeping the notes in decent order a bit beyond me, so I rarely give it a proper go.

Enter the bluetooth keyboard, a product recommended to me by my supervisor to ensuring you can start taking notes/writing wherever you are. I was skeptical at first, due to the variable key size and slight faff of connecting via bluetooth to my phone, but after giving it a couple of hours on a recent visit to the RGS I was sold. Currently I’m typing up a version of this blog post on my phone sitting on a train from Holyhead to Chester on the way back to London. I’m getting great pleasure from watching the trees go by after every few sentences!


Product photo from Microsoft’s site

While I know this entry will read like an advertorial, that isn’t the intention, I’m just very wary of the summer’s PhD writing ahead, and am glad to have an excuse to do the lion’s share sitting in a park rather than in my stuffy office! For now, back to writing, though I’m preparing a more technical blog post which should be finished later tomorrow.


Spotted from the train in Wales

Sentinel bot source

I’ve been sick the last few days, which hasn’t helped in staying focused so I decided to do a few menial tasks, such as cleaning up my references, and some a little bit more involved but not really that demanding, such as adding documentation to the twitter bot I wrote.

While it’s still a bit messy, I think it’s due time I started putting up some code online, particularly because I love doing it so much. When you code for yourself, however, you don’t have to face the wrath of the computer scientists telling you what you’re doing wrong! It’s actually similar in feeling to editing writing, the more you do it the better you get.

As such, I’ve been using Pycharm lately which has forced me to start using PEP8 styling and I have to say it’s been a blessing. There are so many more reasons than I ever thought for using a very high level IDE and I’ll never go back to hacky notepad++ scripts, love it as I may.

In any case, I hope to have some time someday to add functionality – for example have people tweet coordinates + a date @sentinel_bot and have it respond with a decent image close to the request. This kind of very basic engagement for people who mightn’t be bothered going to Earth Explorer or are dissatisfied with Google Earth’s mosaicing or lack of coverage over a certain time period.

The Sentinel missions offer a great deal of opportunity for scientists in the future, and I’ll be trying my best to think of more ways to engage the community as a result.

Find the source code here, please be gentle, it was for fun 🙂


Photogrammetry rules of thumb

I’ve uploaded a CloudCompare file of some fieldwork I did last year to my website here. It uses the UK national LiDAR inventory data, mentioned in the post here. I think it espouses lots of the fundamentals discussed here, and is a good starting point for thinking about network design.

80% overlap

This dates way back, and I’m unsure of where I heard it first, but 80% overlap between images in a photogrammetric block with a nadir viewing geometry is an old rule of thumb from aerial imaging (here’s a quick example I found from 1955), and carries through to SfM surveying. I think it should likely be a first port of call for amateurs doing surveys of surfaces, as it’s very easy to jot down an estimate before undertaking a survey. For this, we should consider just camera positions orthogonal to the surface normal (see this post) and estimate a ground sample distance to aid us with camera spacing from there.

1:1000 rule

This has become superseded in recent years, but is still a decent rule of thumb for beginners in photogrammetry. It says that, in general (very general!), the surface precision of a photogrammetric block will be around 1/1000th of the distance to the surface. Thus, if we are imaging a cliff face from 30m away, we can realistically expect accuracy to within 3 cm of that cliff. This is very useful, especially if you know beforehand the required accuracy of the survey. This is also a more stable starting point than GSD, whose quality as a metric which can vary widely depending on your camera selection.

Convergent viewing geometry

Multi-angular data is intuitively desirable to gather, with the additional data comes additional data processing considerations, but recently published literature has suggested that adding these views has the secondary effect of mitigating systematic errors within photogrammetric bundles. Thus, when imaging a surface, try and add cameras at off angles from the surface normal in order to build a ‘strong’ imaging network, to avoid systematic error creeping in.

Shoot in RAW where possible

Whilst maybe unnecessary for many applications, RAW images allow the user to capture a much great range of colour within an image, owing to the fact that colours are written on 12/14 bits rather than the 8 of JPG images. Adding to this, jpg compression can impact the quality of the 3D point clouds, so using uncompressed images is advised.

Mind your motion

Whilst SfM suggests that the camera is moving, we need to bear in mind that moving cameras are subject to blur, and this is sometimes difficult to detect, especially when shooting in tough conditions where you can’t afford to look at previews. Thus, you can pre-calculate a reasonable top speed for the camera to be moving, and stick to that. We recommend a maximum of 1.5 pixels in GSD over the course of each exposure given the literature and as advised by the OS.

Don’t overparameterize the lens model

Very recently, studies have suggested that overparameterizing the lens model, particularly when poorer quality equipment is being used without good ground control, can lead to a completely unsuitable lens model being fit which will impact the quality of results. The advice – only fit f, cx, cy, k1 and k2 parameters if you’re unsure of what you’re doing. This is far from the default settings in most software packages!


I had a few more points in my long list, but for now these 6 will suffice. Whilst I held back on camera selection here you can read my previous camera selection post for some insight into what you should be looking for. Hope this helps!