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Mapping"),n.createElement("div",{className:"prose"},n.createElement("figure",{className:"mb-4"},n.createElement("img",{style:{maxHeight:"24rem"},className:"object-cover w-full",src:"https://source.unsplash.com/d7FbDJkJSFw/624x384",alt:"Blog Post Cover Image"}),n.createElement("figcaption",{className:"text-xs text-gray-600 text-center mt-1"},"Photo by"," ",n.createElement("a",{className:"underline",href:"https://unsplash.com/photos/d7FbDJkJSFw"},"Markus Spiske")," ","on"," ",n.createElement("a",{className:"underline",href:"https://unsplash.com/"},"Unsplash"))),n.createElement("h2",null,"Why?"),n.createElement("p",null,"Solar PV mapping is very closely related to our PV Forecasting project. Forecasting will provide solar PV yield predictions for any location. To be able to arrive at an accurate forecast for solar power generation out-turn for entire geographical regions, we need to know where all the PV panels are!"),n.createElement("h2",null,"What?"),n.createElement("p",null,"The OpenStreetMap (OSM) community has already done an incredible job mapping over 100,000 PV installations in the UK (out of over 1 million we believe to exist in the UK) and many more across the world. Our goal is to help grow the effort and drastically increase the amount of PV mapped in OSM."),n.createElement("p",null,"Recent research (",n.createElement("a",{href:"http://web.stanford.edu/group/deepsolar/home"},"DeepSolar")," ","and",n.createElement("a",{href:"https://arxiv.org/pdf/1902.10895.pdf"},"SolarMapper from DUKE")," ","are just two examples) has shown the power of machine learning (ML) for identifying PV panels in satellite and aerial imagery at scale. 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