Urban　green　spaces　are　critical　components　of　urban　ecosystems,　playing　an　irreplaceable　role　in　improving　the　ecological　environment　and　enhancing　quality　of　life.　High-precision　identification　of　urban　green　spaces　is　fundamental　for　urban　renewal　and　optimizing　green　infrastructure.　However,　research　on　the　identification　and　spatial　heterogeneity　of　green　spaces　in　megacities　remains　relatively　limited.　This　study,　taking　Xi＇an　as　an　example,　integrates　urban　street　view　images　and　GF-2　(Gaofen-2)　satellite　imagery,　employing　methods　such　as　ISODATA　classification,　K-Means　classification,　and　convolutional　neural　networks　to　achieve　multi-dimensional,　downscaled,　and　high-precision　identification　and　analysis　of　green　spaces.　The　results　indicate　the　following:　(1)　The　K-Means　classification　method　demonstrates　significantly　higher　accuracy　(84.5%)　compared　to　the　ISODATA　classification　method　(62.4%)　and　more　accurately　maps　the　spatial　characteristics　and　heterogeneity　patterns　of　green　spaces.　The　green　space　coverage　identified　by　the　K-Means　method　is　0.277　0,　which　is　lower　than　the　0.360　7　identified　by　ISODATA.　(2)　The　average　Green　View　Index　(GVI)　of　streets　in　Xi＇an＇s　main　urban　area　is　0.156　0,　indicating　a　generally　good　level　of　street　greening.　However,　there　is　notable　polarization　across　different　roads,　with　30%　of　sampling　points　having　a　GVI　below　0.080　0.　Overall,　the　GVI　of　higher-grade　roads　is　greater　than　that　of　lower-grade　roads,　following　the　trend:　primary　roads　＞　secondary　roads　＞　trunk　roads　＞　tertiary　roads.　(3)　There　is　a　positive　correlation　between　the　GVI　of　streets　and　the　vegetation　coverage　in　their　surrounding　areas　in　Xi＇an＇s　main　urban　area.　However,　this　correlation　weakens　in　certain　road　sections,　reflecting　differences　between　vertical　cross-sections　and　overhead　views　of　the　streets.　Combining　these　perspectives　provides　a　more　accurate　assessment　and　quantification　of　urban　green　spaces.　This　study　provides　a　reference　for　green　space　planning,　green　infrastructure　construction,　and　smart　management　in　Xi＇an,　as　well　as　technical　guidance　for　high-precision　identification　and　spatial　analysis　of　urban　green　spaces　in　other　cities.　©　2024　Science　Press.　All　rights　reserved.