  Driven by advancements in photovoltaic (PV) technology, solar energy has
emerged as a promising renewable energy source, due to its ease of integration
onto building rooftops, facades, and windows. For the emerging cities, the lack
of detailed street-level data presents a challenge for effectively assessing
the potential of building-integrated photovoltaic (BIPV). To address this, this
study introduces SolarSAM, a novel BIPV evaluation method that leverages remote
sensing imagery and deep learning techniques, and an emerging city in northern
China is utilized to validate the model performance. During the process,
SolarSAM segmented various building rooftops using text prompt guided semantic
segmentation. Separate PV models were then developed for Rooftop PV,
Facade-integrated PV, and PV windows systems, using this segmented data and
local climate information. The potential for BIPV installation, solar power
generation, and city-wide power self-sufficiency were assessed, revealing that
the annual BIPV power generation potential surpassed the city's total
electricity consumption by a factor of 2.5. Economic and environmental analysis
were also conducted, including levelized cost of electricity and carbon
reduction calculations, comparing different BIPV systems across various
building categories. These findings demonstrated the model's performance and
reveled the potential of BIPV power generation in the future.
