From Rooftops to Ecosystem Services: Deep Learning–Driven Green Roof Potential Assessment in Bern, Switzerland

Development of effective urban climate adaptation and mitigation strategies requires comprehensive spatial information of rooftops and buildings. The aforementioned information is important for assessing the ecosystem services provided by green and blue infrastructure in urban areas, especially for urban heat island (UHI) mitigation and energy conservation. While green roofs are widely acknowledged as a promising solution for enhancing thermal comfort in urban climate, most existing research tends to focus either on mapping current green rooftops or the potential rooftops to implement green rooftops.

This study presents a modified deep convolutional neural network-based rooftop classification framework, based on the Roofpedia framework originally created by the Urban Analytics Lab at the National University of Singapore (NUS). The model leverages high-resolution aerial imagery and incorporates slope of the rooftop to assess green roof suitability. The proposed model uses publicly available geospatial datasets from Swisstopo such as aerial images from SwissImage dataset, elevation data from the swissALTI3D digital terrain model, and building footprints from the swissTLM3D vector dataset.

When the study applies the implemented model to Bern, Switzerland, the model provides the output with labelling the rooftops into four categories: (1) existing green roofs, (2) rooftops suitable for green roof installation, (3) rooftops with solar panels, and (4) flat rooftops which are unsuitable for roof greening. To improve the accuracy and practicality of the classification, roof slope thresholds derived from terrain model were integrated alongside spectral analysis to reflect real-world installation conditions.

The model demonstrated high predictive performance with training loss of 0.0134, mean Intersection over Union (mIoU) of 0.908, and Matthews Correlation Coefficient (MCC) of 0.901. Validation metric demonstrated the robustness with validation loss of 0.0292, mIoU of 0.843, and MCC of 0.822. Comparison with the original Roofpedia framework, the modified model shows significant improvements in multi-class rooftop classification, particularly in identifying realistic opportunities for green roof expansion.

The inclusion of potential green rooftop class, combined with slope-based constraint, allows for a practical and realistic assessment of rooftop suitability for green roof installation. The modified Roofpedia model assists urban planners and decision makers with evidence-based information to support future green infrastructure deployment in Bern and other Swiss cities. Furthermore, the proposed framework is transferable and can be readily replicated in cities worldwide.