2026-07-01 –, A13
How dependent is your town on the electricity grid? How many buildings are supplied by rooftop solar panels? How much unused potential is there to leverage the power of the sun?
We 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 neighbourhood level. The input data is orthophotos and OpenStreetMap building footprints, which we feed into a 4-channel image classification model.
The results of the model are visualised in the Rooftop Solar assessment tool of the Climate Action Navigator (https://climate-action.heigit.org) from HeiGIT (https://heigit.org).
In 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 creation of training data for our supervised learning approach.
We used open source software including Python, pytorch, and supporting libraries for developing and training our model. We also used free and open data from OpenStreetMap and digital orthophotos for Germany.
Our project, the Climate Action Navigator (GNU v3), is also FOSS4G: https://climate-action.heigit.org
Assign a number between 1 and 4 indicating the level of technical complexity of your contribution.: 2: some technical/thematic skills required Select at least one general theme that best defines your proposal: Analysis, manipulation and visualization of geospatial data, Sensors, remote sensing, laser-scanning;, Applications of FOSS4G (disaster management, cartography, environment monitoring etc)) Under which license do you make your contribution available? The conference contribution comprises the abstract, the text contribution for the conference proceedings, the presentation materials as well as the video recording and live transmission of the presentation: CC BY
Gefei Kong is a GeoAI researcher specialising in GeoAI (computer vision), mutlimodal geo data processing, and geospatial analysis, with a PhD in Engineering. She has contributed to multidisciplinary projects, applying her knowledge of GeoAI and GIS to support urban 2D/3D data infrastructure and downstream climate actions and urban planning insights. Driven by both technological innovation and societal impact, she is motivated to develop solutions that support more sustainable and resilient futures.