8/31/2023 0 Comments Nvidia spacenet![]() I’m editing for the first time and scared of making mistakes. ![]() Where do referenced results come from? If we find referenced results in a table to other papers, we show a parsed reference box that editors can use to annotate to get these extra results from other papers. Where do suggested results come from? We have a machine learning model running in the background that makes suggestions on papers. Blue is a referenced result that originates from a different paper. What do the colors mean? Green means the result is approved and shown on the website. A result consists of a metric value, model name, dataset name and task name. What are the colored boxes on the right hand side? These show results extracted from the paper and linked to tables on the left hand side. It shows extracted results on the right hand side that match the taxonomy on Papers With Code. What is this page? This page shows tables extracted from arXiv papers on the left-hand side. In this paper we discuss the SpaceNet imagery, labels, evaluation metrics, prize challenge results to date, and future plans for the SpaceNet challenge series. The first two of these competitions focused on automated building footprint extraction, and the most recent challenge focused on road network extraction. The SpaceNet partners also launched a series of public prize competitions to encourage improvement of remote sensing machine learning algorithms. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. ![]() Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. SpaceNet: A Remote Sensing Dataset and Challenge Seriesįoundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical.
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