In this blog we will use Image classification to detect roads in aerial images. Nowadays, also object detection has become mainstream and in the next (few) years we will probably see more and more applications using image segmentation (see figure 1).įigure 1: Tasks in Computer vision can be categorized as image classification, object detection or segmentation tasks. Monitoring and keeping track of all of these changes has always been a really labour intensive job. If we could get fresh satellite images every day and use Deep Learning to immediately update all of our maps, it would a big help for everyone working in this field!ĭevelopments in the field of Deep Learning are happening so fast that ‘simple’ image classification, which was a big hype a few years ago, already seems outdated. ![]() This of course comes along with new infrastructures, new buildings and neighbourhoods and changing landscapes. In a time of global urbanisation, cities are expanding, growing and changing continuously. If this is possible, the practical applications of it will be enormous. ![]() Inspired by Kaggle’s Satellite Imagery Feature Detection challenge, I would like to find out how easy it is to detect features (roads in this particular case) in satellite and aerial images. If you have been following the latest technical developments you probably know that CNNs are used for face recognition, object detection, analysis of medical images, automatic inspection in manufacturing processes, natural language processing tasks, any many other applications. You could say that you’re only limited by your imagination and creativity (and of course motivation, energy and time) to find practical applications for CNNs. It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods. In the previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets.
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