This　paper　presents　a　semantic　edge-aware　multi-task　neural　network　(SEANet)　to　obtain　closed　boundaries　when　delineating　agricultural　parcels　from　remote　sensing　images.　It　derives　closed　boundaries　from　remote　sensing　images　and　improves　conventional　semantic　segmentation　methods　for　the　extraction　of　small　and　irregular　agricultural　parcels.　SEANet　integrates　three　correlated　tasks:　mask　prediction,　edge　prediction,　and　distance　map　estimation.　Related　features　learned　from　these　tasks　improve　the　generalizability　of　the　network.　We　regard　boundary　extraction　as　an　edge　detection　task　and　extract　rich　semantic　edge　features　at　multiple　levels　to　improve　the　geometric　accuracy　of　parcel　delineation.　Moreover,　we　develop　a　new　multi-task　loss　that　considers　the　uncertainty　of　different　tasks.　We　conducted　experiments　on　three　high-resolution　Gaofen-2　images　in　Shandong,　Xinjiang,　and　Sichuan　provinces,　China,　and　on　two　medium-resolution　Sentinel-2　images　from　Denmark　and　the　Netherlands.　Results　showed　that　our　method　produced　a　better　layout　of　agricultural　parcels,　with　higher　attribute　and　geometric　accuracy　than　the　existing　ResUNet,　ResUNet-a,　R2UNet,　and　BsiNet　methods　on　the　Shandong　and　Denmark　datasets.　The　total　extraction　errors　of　the　parcels　produced　by　our　method　were　0.214,　0.127,　0.176,　0.211,　and　0.184　for　the　five　datasets,　respectively.　Our　method　also　obtains　closed　boundaries　by　one　single　segmentation,　leading　to　superiority　as　compared　with　existing　multi-task　networks.　We　showed　that　it　could　be　applied　to　images　with　different　spatial　resolutions　for　parcel　delineation.　Finally,　our　method　trained　on　the　Xinjiang　dataset　could　be　successfully　transferred　to　the　Shandong　dataset　with　different　dates　and　landscapes.　Similarly,　we　obtained　satisfactory　results　when　transferring　from　the　Denmark　dataset　to　the　Netherlands　dataset.　We　conclude　that　SEANet　is　an　accurate,　robust,　and　transferable　method　for　various　areas　and　different　remote　sensing　images.　The　codes　of　our　model　are　available　at　htt　ps://github.com/long123524/SEANet_torch.