![]() Here we explore the Amazon Elastic Cloud Compute (EC2) offerings for GPU computing. If one does not have their own personal GPU computing platform, one option to get started is to use cloud GPU instances. The SpaceNet 6 baseline (as with previous SpaceNet Challenges) relies upon a deep-learning architecture that requires GPUs to run in a timely manner. ![]() The downside is that not everyone has access to powerful GPUs. The rapid advance in computer vision has been driven by a number of features, but graphical processing unit (GPU) computing has been key. The SpaceNet partners have extended $500 in compute credits to competitors who achieve a performance threshold equivalent to the baseline model, thereby enabling extensive experimentation for this challenge. In this post we detail the simple steps required to train and test the SpaceNet 6 deep learning baseline model on an AWS GPU instance for less than the cost of a tank of gas. Deep learning models have shown great promise in past SpaceNet challenges, but require the use of dedicated GPU hardware that not all parties may have access to. The challenge is ongoing, with over a month remaining until the May 1 deadline. The SpaceNet 6 Challenge asks participants to extract building footprints from a multimodal remote sensing dataset comprising both synthetic aperture radar (SAR), as well as electro-optical imagery. SpaceNet is run in collaboration with CosmiQ Works, Maxar Technologies, Intel AI, Amazon Web Services (AWS), Capella Space, Topcoder, IEEE GRSS, and the National Geospatial Intelligence Agency ( NGA ). building footprint & road network detection). Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e.
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