Correct　facade　image　parsing　is　essential　to　the　semantic　understanding　of　outdoor　scenes.　Unfortunately,　there　are　often　various　occlusions　in　front　of　buildings,　which　fails　many　existing　methods.　In　this　paper,　we　propose　an　end-to-end　deep　network　for　facade　parsing　with　occlusions.　The　network　learns　to　decompose　an　input　image　into　visible　and　invisible　parts　by　occlusion　reasoning.　Then,　a　context　aggregation　module　is　proposed　to　collect　nonlocal　cues　for　semantic　segmentation　of　the　visible　part.　In　addition,　considering　the　regularity　of　man-made　buildings,　a　repetitive　pattern　completion　branch　is　designed　to　infer　the　contents　in　the　invisible　regions　by　referring　to　the　visible　part.　Finally,　the　parsing　map　of　the　input　facade　image　is　generated　by　fusing　the　results　of　the　visible　and　invisible　results.　Experiments　on　both　synthetic　and　real　datasets　demonstrate　that　the　proposed　method　outperforms　state-of-the-art　methods　in　parsing　facades　with　occlusions.　Moreover,　we　applied　our　method　in　applications　of　image　inpainting　and　3D　semantic　modeling.