The　widespread　adoption　of　distributed　photovoltaic　(PV)　systems　highlights　the　need　for　sophisticated　segmentation　technologies　that　can　accurately　identify　PV　panels,　essential　for　calculating　potential　capacity　and　informing　development　strategies.　Although　artificial　intelligence　has　significantly　advanced　the　accuracy　and　reliability　of　PV　panel　segmentation,　real-world　complexities　such　as　diverse　panel　types,　installation　methods,　and　varied　backgrounds　pose　challenges　to　model　adaptability　and　generalization.　This　research　introduces　a　method　that　enhances　PV　panel　segmentation　by　employing　the　enhanced　Segment　Anything　Model,　which　has　been　extensively　pre-trained　using　a　comprehensive　real-world　dataset　to　incorporate　multimodal　semantic　information,　thus　improving　generalization.　Additionally,　a　fine-tuning　process　has　been　integrated　to　better　absorb　critical　features　from　the　training　data,　increasing　the　model＇s　sensitivity　to　the　unique　characteristics　of　specific　PV　installations.　Field　tests　in　Heilbronn,　Germany,　confirm　the　method＇s　superior　performance　and　flexibility,　underscoring　its　potential　to　support　strategic　planning　for　large-scale　PV　deployment.　©　2024,　Scanditale　AB.　All　rights　reserved.