Community-led data collection approach leading to energy labeling and retrofit decision framework with case application

• Community data fused with physics, portable decision surfaces for retrofits • Validated with heat flux meters and energy audits for roof and wall insulation • Novel decision framework to rank retrofit urgency, guide shallow and deep retrofit • Electrification potential aligns with envelope work, target heat pumps, and hot water Neighborhood-scale energy planning is often constrained by limited and inconsistent ground-truth data. Although community-led data collection has been effective in other sectors, its application to building energy modeling and retrofit decision-making remains limited, with no standardized or validated framework available. This study presents a community-led data collection and modeling workflow that couples resident-gathered measurements with physics-based analysis and a transparent retrofit decision framework. In Georgetown, PE, Canada, trained volunteers collected thermographic images, energy-systems data, occupancy, building characteristics, etc., for 157 residential buildings using research ethics-approved protocols and standardized kits. Infrared images, fused with local weather data, drive an exterior-surface heat-balance model to estimate building envelope thermal resistances. Validations against in-situ heat flux meters (walls of 8 buildings) are performed along with a comparative analysis with energy audit data for 24 buildings. A composite envelope performance score (EPS) ranked retrofit tiers and identified systematic shortfalls guiding targeted retrofit strategies. Demonstrated in a real-world case study, the community-led protocol, processing pipeline, and decision rules are portable and scalable to other communities with different housing stocks and envelope measurements. The originality of this study lies in its first-time designation of a standardized community-led protocol that integrates physics-based modeling and retrofit decision rules at the neighborhood scale. This integration transforms resident-gathered observations into simulation-ready evidence and actionable retrofit pathways in energy planning.