Semantic　change　detection　(SCD)　aims　to　identify　potential　Earth　surface　changes,　including　their　location　and　class,　from　multi-temporal　remote　sensing　images.　However,　the　under-detection　and　pseudo-change　issues　in　existing　SCD　methods　severally　limit　their　effectiveness　in　diverse　ground　scenarios.　To　address　these　issues,　a　semantic　map-guided　network,　namely　SMGNet,　is　proposed　based　on　a　multitask　architecture　designed　to　identify　potential　land　cover　changes　from　bi-temporal　high-resolution　remote　sensing　images.　A　robust　feature　extractor　is　first　developed　to　extract　multi-scale　contextual　information　while　retaining　fine-grained　spatial　details,　thus　enhancing　the　semantic　representation　of　complex　objects　with　irregular　shapes　and　large　sizes.　To　address　the　issue　of　under-detection,　we　integrate　historical　semantic　information　derived　from　pre-temporal　land　cover　maps　into　the　model　using　a　semantic　map　encoder　module.　A　semantic　fusion　module　based　on　Bayesian　theory　is　developed　to　highlight　salient　changed　information,　thus　reducing　pseudo-changes　caused　by　the　same　ground　objects　with　spectra　variations.　Experimental　results　obtained　in　a　public　SCD　dataset　demonstrate　the　effectiveness　of　the　proposed　method　in　identifying　various　semantic　changes.　Results　indicate　that　the　proposed　SMGNet　achieved　the　highest　detection　accuracy,　exceeding　nine　existing　methods　by　14.81%　to　41.28%　and　8.45%　to　40.31%　in　terms　of　SeK　and　F1scd　metrics　on　the　HRSCD　dataset,　respectively.　The　proposed　method　effectively　alleviated　pseudo-changes　induced　by　spectra　and　temporal　differences,　and　accurately　detecting　these　changed　objects　with　irregular　shapes　and　large　sizes.　The　detected　results　exhibited　high　inter-class　compactness　and　well-defined　boundaries.　©　2004-2012　IEEE.