ARTIFICIAL INTELLIGENCE
three systems that allowed us to evolve the overall accuracy of the process , creating a final deep learning system that was accurate to 90 %.”
Doidge went on to detail some of the issues involved in the progress of the project :
“ Our biggest issue for the hack was learning how to create an end to end flowline on the cloud . Where we could store data , process it , classify it , and then utilise the output in a meaningful way . The Microsoft advisors and our OS experts successfully created a unique solution for the roof hack . “ We also had an issue with ensuring our algorithms worked in a general sense , and geography is deceptively changeable and complex across GB . Since the roof hack where we identified this problem for roof generalisation , we solved this by curating a more geographically diverse data sets for our current deep learning training runs .”
Finally , Doidge describes other functions within the OS that are also utilising deep learning to achieve the required project outcomes :
“ We have various streams of deep learning at Ordnance Survey , as well as some traditional computer vision techniques and rule-based classification . These are all geared towards providing more for our customers and enhancing our offering to GB . Some of our upcoming projects include :
ImageLearn : Our deep learning programme in which we are training a model on our RGB imagery and using a MasterMap topography layer as a highly detailed labelling method for the landscape . We hypothesise that we can decode the signatures of processes that have shaped the landscape , or
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