Facade Segmentation for Solar Photovoltaic Suitability

TL;DR

This paper introduces an automated pipeline that uses machine learning to identify suitable facade surfaces for photovoltaic installation, estimating solar energy potential for urban decarbonization.

Contribution

It presents a novel method that combines architectural facade data with semantic segmentation to assess PV suitability and layout, filling a gap in automated facade-level PV planning.

Findings

Installable BIPV potential is lower than theoretical estimates.

Pipeline achieves accurate facade segmentation and PV suitability mapping.

Method scalable with increasing facade imagery availability.

Abstract

Building integrated photovoltaic (BIPV) facades represent a promising pathway towards urban decarbonization, especially where roof areas are insufficient and ground-mounted arrays are infeasible. Although machine learning-based approaches to support photovoltaic (PV) planning on rooftops are well researched, automated approaches for facades still remain scarce and oversimplified. This paper therefore presents a pipeline that integrates detailed information on the architectural composition of the facade to automatically identify suitable surfaces for PV application and estimate the solar energy potential. The pipeline fine-tunes SegFormer-B5 on the CMP Facades dataset and converts semantic predictions into facade-level PV suitability masks and PV panel layouts considering module sizes and clearances. Applied to a dataset of 373 facades with known dimensions from ten cities, the results show that installable BIPV potential is significantly lower than theoretical potential, thus providing valuable insights for reliable urban energy planning. With the growing availability of facade imagery, the proposed pipeline can be scaled to support BIPV planning in cities worldwide.