For a new online work, I’m creating a radar visualisation in which each of the dots will be assigned an image and text that reads ‘I AM NOT TARGET PRACTICE’.

The images that I’m hoping to incorporate are of cloud data centres run by AWS, Google, Microsoft, IBM, and Oracle. I took screenshots using imagery from the ESRI satellite and focused on the data centres in Virginia because it has the highest density of such places in the world.

I’m planning on overlaying these images with cloud satellite imagery so the view of them is partially obscured.

(Alt text for image gallery: satellite imagery of data centres, many are angular and white with fans mounted on the roof).

A series of images made by combining maps that show surveillance network Five Eyes locations in the UK and cloud optical thickness.

The icon of the red triangle with the eye within it was the default setting in the tool I used to create these images. Note: to change in future.

This week I’ve been experimenting with layering up cloud imagery and the control dashboards used within missile control centres.

I’d been thinking about the false premise that more data guarantees more clarity, something that James Bridle talks about in his book New Dark Age. It’s a premise often used to bolster the perpetuation of surveillance technologies. But as Bridle alludes to, more data also means greater complexity and increased potential for confusion and comprehension. It is an uneasy paradox that destabilises the idea that more data enables us to see more clearly, when the reality is more cloudiness.

We have likely heard or experienced the temptations of the ‘big red button’ – do not press, urging us to do the opposite. But I’ve also been thinking that even the existence of a button sets the stage for the following events. It is there so the temptation is to use it (this is one of the ongoing arguments against the likes of Trident). So my experiments this week also looked at the aesthetics of disappearing dashboard controls, blurring into this clouded vision.

The images of dashboards are released under a Creative Commons license by photographer Todd Lapin and show the control panels within SF-88, a former Nike missile base in the Marin Headlands, US. Nike missiles were anti-aircraft missiles often equipped with nuclear weapons between the 1950s and 1970s during the Cold War. Here is a link to the Creative Commons license: https://creativecommons.org/licenses/by-nc/2.0/

The cloud videos are from a dataset created by the Multimodal Vision Research Laboratory (credit below) and also feature videos collected by Martin Setvak.

Cloud dataset credits: Jacobs N, Abrams A, Pless R. 2013. Two Cloud-Based Cues for Estimating Scene Structure and Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 35:2526–2538. DOI: 10.1109/TPAMI.2013.55, and Jacobs N, Bies B, Pless R. 2010. Using Cloud Shadows to Infer Scene Structure and Camera Calibration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1102–1109. DOI: 10.1109/CVPR.2010.5540093.

Adversarial.io is a tool created to evade technologies of image recognition and reveal how differently machines and humans interpret images. Subtle noise added to images can completely alter how an algorithm will classify a photo, while in terms of human perception, there is little change to the original image.

I find it interesting in the context of my project because of the high confidence with which the M-10 computer declared its identification of the missiles (which turned out to be sunlight reflecting off clouds).

In my experiments, I gathered photos of nuclear missiles which, through such added noise, became sewing machines, freight cars, obelisks and totem poles in the eyes of computer vision.

Some of the nouns used to describe these missiles were quite obscure! I had to look up the definitions for: a stupa (a dome-like building usually containing relics and used for meditation), a barracouta (type of fish) and thresher (an agricultural machine for separating grains).