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UID:pretalx-foss4g-europe-2026-ENYAFG@talks.osgeo.org
DTSTART;TZID=EET:20260701T120000
DTEND;TZID=EET:20260701T123000
DESCRIPTION:How dependent is your town on the electricity grid? How many bu
 ildings are supplied by rooftop solar panels? How much unused potential is
  there to leverage the power of the sun?\n\nWe built a deep learning model
  using FOSS4G and open remote sensing data for Germany. With this model\, 
 you can detect which buildings have rooftop solar panels at a neighbourhoo
 d level. The input data is orthophotos and OpenStreetMap building footprin
 ts\, which we feed into a 4-channel image classification model.\n\nThe res
 ults of the model are visualised in the Rooftop Solar assessment tool of t
 he Climate Action Navigator (https://climate-action.heigit.org) from HeiGI
 T (https://heigit.org).\n\nIn this talk\, we will demonstrate our results 
 through our assessment tool. We will also explain the design of our model 
 and how we used OpenStreetMap tagging to significantly speed up the creati
 on of training data for our supervised learning approach.
DTSTAMP:20260605T082534Z
LOCATION:A13
SUMMARY:Detecting Rooftop Solar Panels with Deep Learning\, using Open Remo
 te Sensing Data and OpenStreetMap - Gefei Kong
URL:https://talks.osgeo.org/foss4g-europe-2026/talk/ENYAFG/
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