The　widespread　adoption　of　photovoltaic　(PV)　technology　for　renewable　energy　necessitates　accurate　segmentation　of　PV　panels　to　estimate　installation　capacity.　However,　achieving　highly　efficient　and　precise　segmentation　methods　remains　a　pressing　challenge.　Recent　advancements　in　artificial　intelligence　and　remote　sensing　techniques　have　shown　promise　in　PV　segmentation.　Nevertheless,　real-world　scenarios　introduce　complexities　such　as　diverse　sensing　platforms,　sensors,　panel　categories,　and　testing　regions.　These　factors　contribute　to　resolution,　size,　and　foreground-background　class　imbalances,　impeding　accurate　and　generalized　PV　panel　segmentation　over　large　areas.　To　address　these　challenges,　we　propose　GenPV,　a　deep　learning　model　that　leverages　data　distribution　analysis　and　PV　panel　characteristics　to　enhance　segmentation　accuracy　and　generalization.　GenPV　employs　a　multi-scale　feature　learning　approach,　utilizing　an　enhanced　feature　pyramid　network　to　fuse　data　features　from　multiple　resolutions,　effectively　addressing　resolution　imbalance.　Moreover,　inductive　learning　is　employed　through　a　multitask　approach,　facilitating　the　detection　and　identification　of　both　small　and　large-sized　PV　panels　to　mitigate　size　imbalance.　To　address　significant　class　imbalance　in　PV　panel　recognition　tasks,　we　integrate　the　Focal　loss　function　for　effective　hard　sample　mining.　Through　experimental　evaluation　conducted　in　Heilbronn,　Germany,　our　proposed　method　demonstrates　superior　performance　compared　to　stateof-the-art　approaches　in　PV　panel　segmentation.　The　results　exhibit　progressively　higher　accuracy　and　improved　generalization　capability.　These　findings　highlight　the　potential　of　our　method　to　serve　as　an　advanced　and　practical　tool　for　PV　segmentation　in　the　renewable　energy　field.