Facade　image　parsing　is　essential　to　the　semantic　understanding　and　3-D　reconstruction　of　urban　scenes.　Considering　the　occlusion　and　appearance　ambiguity　in　single-view　images　and　the　easy　acquisition　of　multiple　views,　in　this　letter,　we　propose　a　multiview　enhanced　deep　architecture　for　facade　parsing.　The　highlight　of　this　architecture　is　a　cross-view　feature　aggregation　module　that　can　learn　to　choose　and　fuse　useful　convolutional　neural　network　(CNN)　features　from　nearby　views　to　enhance　the　representation　of　a　target　view.　Benefitting　from　the　multiview　enhanced　representation,　the　proposed　architecture　can　better　deal　with　the　ambiguity　and　occlusion　issues.　Moreover,　our　cross-view　feature　aggregation　module　can　be　straightforwardly　integrated　into　existing　single-image　parsing　frameworks.　Extensive　comparison　experiments　and　ablation　studies　are　conducted　to　demonstrate　the　good　performance　of　the　proposed　method　and　the　validity　and　transportability　of　the　cross-view　feature　aggregation　module.