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Several interesting machine learning and signal processing contests have are currently active, including among others the a nature conservancy challenge aimed at monitoring of fisheries and a challenge focusing on satellite imagery feature detection.
In the first of these challenges the Nature Conservancy is inviting the Kaggle community to develop algorithms to automatically detect and classify species of tunas, sharks and more that fishing boats catch, which will accelerate the video review process. Faster review and more reliable data will enable countries to reallocate human capital to management and enforcement activities which will have a positive impact on conservation and our planet.
The second challenge is organized by the Defence Science and Technology Laboratory (Dstl) and its goal is to accurately classify features in overhead imagery. Automating feature labeling will not only help Dstl make smart decisions more quickly around the defense and security of the UK, but also bring innovation to computer vision methodologies applied to satellite imagery.
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