Extracting　roads　from　remote　sensing　images　is　of　significant　importance　for　automatic　road　network　updating,　urban　planning,　and　construction.　However,　various　factors　in　complex　scenes　(e.g.,　high　vegetation　coverage　occlusions)　may　lead　to　fragmentation　in　the　extracted　road　networks　and　also　affect　the　robustness　of　road　extraction　methods.　This　study　proposes　a　multi-scale　road　extraction　method　with　asymmetric　generative　adversarial　learning　(MS-AGAN).　First,　we　design　an　asymmetric　GAN　with　a　multi-scale　feature　encoder　to　better　utilize　the　context　information　in　high-resolution　remote　sensing　images　(HRSIs).　Atrous　spatial　pyramid　pooling　(ASPP)　and　feature　fusion　are　integrated　into　the　asymmetric　encoder-decoder　structure　to　avoid　feature　redundancy　caused　by　multi-level　cascading　operations　and　enhance　the　generator　network＇s　ability　to　extract　fine-grained　road　information　at　the　pixel　level.　Second,　to　maintain　road　connectivity,　topologic　features　are　considered　in　the　pixel　segmentation　process.　A　linear　structural　similarity　loss　(LSSIM)　is　introduced　into　the　loss　function　of　MS-AGAN,　which　guides　MS-AGAN　to　generate　more　accurate　segmentation　results.　Finally,　to　fairly　evaluate　the　performance　of　deep　models　under　complex　backgrounds,　the　Bayesian　error　rate　(BER)　is　introduced　into　the　field　of　road　extraction　for　the　first　time.　Experiments　are　conducted　via　Gaofen-2　(GF-2)　high-resolution　remote　sensing　images　with　high　vegetation　coverage　in　the　Daxing　District　of　Beijing,　China,　and　the　public　DeepGlobe　dataset.　The　performance　of　MS-AGAN　is　compared　with　a　list　of　advanced　models,　including　RCFSNet,　CoANet,　UNet,　DeepLabV3+,　and　DiResNet.　The　final　results　show　that　(1)　with　respect　to　road　extraction　performance,　the　Recall,　F1,　and　IoU　values　of　MS-AGAN　on　the　Daxing　dataset　are　2.17%,　0.04%,　and　2.63%　higher　than　the　baselines.　On　DeepGlobe,　the　Recall,　F1,　and　IoU　of　MS-AGAN　improve　by　1.12%,　0.42%,　and　0.25%,　respectively.　(2)　On　road　connectivity,　the　Conn　index　of　MS-AGAN　from　the　Daxing　dataset　is　46.39%,　with　an　improvement　of　0.62%　over　the　baselines,　and　the　Conn　index　of　MS-AGAN　on　DeepGlobe　is　70.08%,　holding　an　improvement　of　1.73%　over　CoANet.　The　quantitative　and　qualitative　analyses　both　demonstrate　the　superiority　of　MS-AGAN　in　preserving　road　connectivity.　(3)　In　particular,　the　BER　of　MS-AGAN　is　20.86%　over　the　Daxing　dataset　with　a　0.22%　decrease　compared　to　the　best　baselines　and　11.77%　on　DeepGlobe　with　a　0.85%　decrease　compared　to　the　best　baselines.　The　proposed　MS-AGAN　provides　an　efficient,　cost-effective,　and　reliable　method　for　the　dynamic　updating　of　road　networks　via　HRSIs.