Rooftop　solar　photovoltaic　(PV)　retrofitting　can　greatly　reduce　the　emissions　of　greenhouse　gases,　thus　contributing　to　carbon　neutrality.　Effective　assessment　of　carbon　emission　reduction　has　become　an　urgent　challenge　for　the　government　and　for　business　enterprises.　In　this　study,　we　propose　a　method　to　assess　accurately　the　potential　reduction　of　long-term　carbon　emission　by　installing　solar　PV　on　rooftops.　This　is　achieved　using　the　joint　action　of　GF-2　satellite　images,　Point　of　Interest　(POI)　data,　and　meteorological　data.　Firstly,　we　introduce　a　building　extraction　method　that　extends　the　DeepLabv3+　by　fusing　the　contextual　information　of　building　rooftops　in　GF-2　images　through　multi-sensory　fields.　Secondly,　a　ridgeline　detection　algorithm　for　rooftop　classification　is　proposed,　based　on　the　Hough　transform　and　Canny　edge　detection.　POI　semantic　information　is　used　to　calculate　the　usable　area　under　different　subsidy　policies.　Finally,　a　multilayer　perceptron　(MLP)　is　constructed　for　long-term　PV　electricity　generation　series　with　regional　meteorological　data,　and　carbon　emission　reduction　is　estimated　for　three　scenarios:　the　best,　the　general,　and　the　worst.　Experiments　were　conducted　with　GF-2　satellite　images　collected　in　Daxing　District,　Beijing,　China　in　2021.　Final　results　showed　that:　(1)　The　building　rooftop　recognition　method　achieved　overall　accuracy　of　95.56%;　(2)　The　best,　the　general　and　the　worst　amount　of　annual　carbon　emission　reductions　in　the　study　area　were　7,705,100　tons,　6,031,400　tons,　and　632,300　tons,　respectively;　(3)　Multi-source　data,　such　as　POIs　and　climate　factors　play　an　indispensable　role　for　long-term　estimation　of　carbon　emission　reduction.　The　method　and　conclusions　provide　a　feasible　approach　for　quantitative　assessment　of　carbon　reduction　and　policy　evaluation.