Some might say AI or more specifically machine learning is nothing new to Geographic Information Systems (GIS). They are partly correct as algorithms like cluster analysis and regression have been used for decades by GIS professionals. However, machine learning can also take geospatial analysis to new heights.
The largest hurdle we face with machine learning is training data. And, it’s not just the amount of training data that’s difficult to overcome but equally, the quality of training data. It’s a big problem and one that can be significantly compounded by “biases” in the data.
It was not so long ago, when getting access to high resolution aerial or satellite imagery was not only extremely expensive, but so was the cost of extracting data and value from it.
Over 500 industry leaders, academics, users, government officials and other interested parties gathered at Auckland’s fantastic ASB Bank Waterfront Theatre last week to discuss the advancements in AI and how they are shaping our future.
In the second blog in our series on processing underwater video, I’ll discuss some of our data gathering and classification techniques leading in to our next blog post which discusses semantic segmentation and object detection.