Current　agricultural　parcels　(AP)　extraction　faces　two　main　limitations:　(1)　existing　AP　delineation　methods　fail　to　fully　utilize　low-level　information　(e.g.,　parcel　boundary　information),　leading　to　unsatisfactory　performance　under　certain　circumstances;　(2)　the　lack　of　large-scale,　high-resolution　AP　benchmark　datasets　in　China　hinders　comprehensive　model　evaluation　and　improvement.　To　address　the　first　limitation,　we　develop　a　hierarchical　semantic　boundary-guided　network　(HBGNet)　to　fully　leverage　boundary　semantics,　thereby　improving　AP　delineation.　It　integrates　two　branches,　a　core　branch　of　AP　feature　extraction　and　an　auxiliary　branch　related　to　boundary　feature　mining.　Specifically,　the　boundary　extract　branch　employes　a　module　based　on　Laplace　convolution　operator　to　enhance　the　model＇s　awareness　of　parcel　boundary.　For　AP　feature　extraction,　a　local-global　context　aggregation　module　is　designed　to　enhance　the　semantic　representation　of　AP,　improving　the　adaptability　across　different　AP　scenarios.　Meanwhile,　a　boundary-guided　module　is　developed　to　enhance　boundary　details　of　high-level　AP　semantic　information.　Ultimately,　a　multi-grained　feature　fusion　module　is　designed　to　enhance　the　capacity　of　HBGNet　to　extract　APs　with　various　sizes　and　shapes.　Regarding　the　second　limitation,　we　construct　the　first　large-scale　very　high-resolution　(VHR)　agricultural　parcel　dataset　(FHAPD)　across　seven　different　areas,　covering　more　than　10,000　km2,　using　data　from　GaoFen-1　(2-meter)　and　GaoFen-2　(1-meter).　Detailed　experiments　are　conducted　on　the　FHAPD,　a　publicly　European　dataset　(i.e.,　Al4boundaries),　and　medium-resolution　Sentinel-2　images　from　the　Netherlands　and　HBGNet　is　compared　with　other　eight　AP　delineation　methods.　Results　show　that　HBGNet　outperforms　the　other　eight　methods　in　attribute　and　geometry　accuracy.　The　Intersection　over　Union　(IOU),　F1-score　of　the　boundary　(Fbdy),　and　global　total-classification　(GTC)　exceed　other　methods　by　0.61　%-7.52　%,　0.8　%-36.3　%,　and　1.7　%-31.8　%,　respectively.　It　also　effectively　transfers　to　unseen　regions.　We　conclude　that　the　proposed　HBGNet　is　an　effective,　advanced,　and　transferable　method　for　diverse　agricultural　scenarios　and　remote　sensing　images.