Acquiring information from afar is often quite important, whether to reduce the cost of ground investigation, to get a wider view, or perhaps to conceal surveillance activities. A couple weeks ago at the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining you had a ‘social good’ paper on using remote sensing data to predict which villages have more poor people than others, based on whether there were more houses with metal roofs or with thatch roofs. An earlier presentation of this work was given at DataKind, under whose auspices the work was carried out together with the the charity GiveDirectly.
Congratulations on the best paper award for this work!
Incidentally I also enjoyed your work on tennis analytics at the same conference and was therefore glad I attended part of the Large-Scale Sports Analytics workshop, in addition to the data-driven educational assessment workshop I was running.
Coming back to remote sensing and somewhat related to the last post, remote sensing of waste production can potentially be used to sense alien civilizations. Though more apropos to your work, apparently night-time light remote sensing is becoming a common approach to poverty detection, thinking of night lights as light pollution. A few papers in a variety of journals on this topic include this, this, and this. I wonder, though, whether there is a way to measure “signal pollution” as a way to do remote sensing to build on the idea of information metabolism. With information pollution, maybe it is low-entropy signals one should look for, rather than high-entropy signals.
Perhaps artistic things you can see from the air?