Orbital Insight’s Global Water Reserves: Automatic Detection of Water in Landsat Imagery using Deep Learning

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.

Reflexionskurven

Spectral reflection curve of water, soil and vegetation, overlaid with the spectral bands of Landsat 7. Source: http://www.seos-project.eu/modules/remotesensing/remotesensing-c01-p05.html

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.

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About WQ

I received my PhD (2013) in Remote Sensing, Earth and Space Science at the Dept. of Aerospace Engineering Sciences, University of Colorado, Boulder, USA, under a Fulbright fellowship. Currently, I'm an Assistant Professor in the Dept. of Space Science at Institute of Space Technology (IST), Islamabad, Pakistan, where I have been a founding member of the Geospatial Research & Education Lab (GREL). My general expertise is in Remote Sensing where I have worked with various remote sensing datasets through my career, while for my PhD thesis I specifically worked on Remote Sensing using SAR (Synthetic Aperture Radar) and Oceanography, working extensively on development of techniques to measure ocean surface currents from space-borne SAR intensity images and interferometric data. My research interests are: Remote sensing, Synthetic Aperture Radar (SAR) imagery and interferometric data processing & analysis, Visible/Infrared/High-resolution satellite image processing & analysis, Oceanography, Earth system study and modelling, LIDAR data processing and analysis, Scientific programming. I am a reviewer for IEEE Transactions on Geoscience & Remote Sensing, Forest Ecosystems, GIScience & Remote Sensing, Journal of African Earth Sciences, and Italian Journal of Agronomy. I am an alumnus of Pakistan National Physics Talent Contest (NPTC), an alumnus of the Lindau Nobel Laureate Meetings, a Fulbright alumnus, and the Pakistan National Point of Contact for Space Generation Advisory Council (SGAC). I was an invited speaker at the TEDxIslamabad event held in Nov., 2014. I've served as mentor in the NASA International Space App Challenge Islamabad events in April 2015 and April 2016.

One thought on “Orbital Insight’s Global Water Reserves: Automatic Detection of Water in Landsat Imagery using Deep Learning

  1. Pingback: Mapping 3 Decades of Global Surface Water Occurrence with Landsat | EarthEnable

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