What?
Mapizy uses machine learning to automate discovery of changes to the built and natural environments at scale, with a mission to quantify the changing planet and create advanced analytics for industry.
Mapizy’s machine learning platform can rapidly process satellite, drone and terrestrial images and create geospatial analytics for mining, infrastructure, agriculture and forestry industry.
These include agriculture weeds, pests and crop diseases, forest inventory, land cover, remote mine rehabilitation and building footprints.
Where?
www.mapizy.com
Who?
Mehdi Ravanbakhsh and Pouria Ramzi have been doing intensive research since 2015 to develop the current geospatial products and are starting to experience traction in the marketplace in Australia and globally.
Quote
“A wide range of real-world problems can be solved through image data and AI. Many organisations and industry didn’t know that they can find answers for the questions they didn't know they could answer through imagery data. Using groundbreaking deep learning, Mapizy exploits image data to answer business questions, more quickly and accurately,” Mehdi Ravanbakhsh said.
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Mapizy has been working with clients across industries to build image-based software solutions that help them solve difficult challenges and achieve their business objectives. More specifically, their weed detection solution helped farmers to save up to 80 per cent on herbicide cost.
Mapizy’s computer vision solutions have been recognised by the American and UK Society of Photogrammetry and Remote Sensing through technology awards. The team recently signed a partnership agreement with UWA, and was chosen as finalist for Ag-focused Harvest and CSIRO ON accelerator programs.
