The　semantic　parsing　of　building　facade　images　is　a　fundamental　yet　challenging　task　in　urban　scene　understanding.　Existing　works　sought　to　tackle　this　task　by　using　facade　grammars　or　convolutional　neural　networks　(CNNs).　The　former　can　hardly　generate　parsing　results　coherent　with　real　images　while　the　latter　often　fails　to　capture　relationships　among　facade　elements.　In　this　letter,　we　propose　a　pyramid　atrous　large　kernel　(ALK)　network　(ALKNet)　for　the　semantic　segmentation　of　facade　images.　The　pyramid　ALKNet　captures　long-range　dependencies　among　building　elements　by　using　ALK　modules　in　multiscale　feature　maps.　It　makes　full　use　of　the　regular　structures　of　facades　to　aggregate　useful　nonlocal　context　information　and　thereby　is　capable　of　dealing　with　challenging　image　regions　caused　by　occlusions,　ambiguities,　and　so　on.　Experiments　on　both　rectified　and　unrectified　facade　data　sets　show　that　ALKNet　has　better　performances　than　those　of　state-of-the-art　methods.