Gefei Kong
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.
Session
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.