A few months ago, an article in MIT Technology Review showed how Orbital Insight utilized deep learning to automatically monitor and analyze water levels over the whole world on a weekly basis utilising publicly available Landsat 7 / 8 imagery.
It is interesting to note that on a basic level, detecting water in Landsat images is not a too complex problem, as water is known to have a very weak reflectance in NIR, and very often just using Band 4 in Landsat 7 can give a clear indication and differentiation of water from other land surface features.
The amazing thing that Orbital Insight has done is to largely automate this whole processing, and build a process chain to utilizing the huge Landsat archive and do this on a running weekly to bi-weekly basis. I’m sure there is a certain degree of accuracy in this, which I hope will be reported somewhere soon (maybe it has been already, but I have not come across it). Cloud shadows and mountain shadows can give significant errors in detecting water in Landsat imagery. Orbital Insight is analyzing huge chunks of images, turning it practically into a big data problem, and the task of automatically adjusting the algorithm for multiple images in time and spread all over the world is a big achievement because of varying local conditions.
Learn more about Orbital Insight’s Global Water Reserves product here.