Accurate　information　on　land　use　classification　is　essential　for　promoting　sustainable　urban　growth,　preserving　the　environment,　safeguarding　public　health,　and　enhancing　socioeconomic　prosperity.　This　paper　explores　the　application　of　street　view　image　(SVI)　assisted　remote　sensing　images　(RSI)　for　urban　land　use　classification.　In　this　paper,　we　introduced　a　dual-stream　network　integrating　a　large　model　and　CNN　for　feature　extraction,　termed　the　Large-VGG　Dual-Stream　Network　(LVDNet),　which　extract　RSI　semantic　features　using　a　CNN　backbone,　while　SVI　semantic　sementic　features　are　obtained　from　a　large　model　trained　on　real-world　data,　with　feature　fusion　achieved　through　cross-learning.　We　constructed　a　new　dataset　ZZ　for　experiments,　in　which　the　spatial　correspond　between　street　view　images　and　remote　sensing　imagery　is　established.　Our　findings　indicate　that　the　proposed　method　performs　effectively　in　datasets,　demonstrates　SVI　significantly　enhance　the　accuracy　of　urban　land　use　classification,　particularly　in　identifying　complex　urban　functional　areas.　©　2025　IEEE.