![]() ![]() Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. ![]() ![]() Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. Satellite imagery is changing the way we understand and predict economic activity in the world. ![]()
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