Precise　information　on　agricultural　parcels　is　crucial　for　effective　farm　management,　crop　mapping,　and　monitoring.　Current　techniques　often　encounter　difficulties　in　automatically　delineating　vectorized　parcels　from　remote　sensing　images,　especially　in　irregular-shaped　areas,　making　it　challenging　to　derive　closed　and　vectorized　boundaries.　To　address　this,　we　treat　parcel　delineation　as　identifying　valid　parcel　vertices　from　remote　sensing　images　to　generate　parcel　polygons.　We　introduce　a　Point-Line-Region　interactive　multitask　network　(PLR-Net)　that　jointly　learns　semantic　features　of　parcel　vertices,　boundaries,　and　regions　through　point-,　line-,　and　region-related　subtasks　within　a　multitask　learning　framework.　We　derived　an　attraction　field　map　(AFM)　to　enhance　the　feature　representation　of　parcel　boundaries　and　improve　the　detection　of　parcel　regions　while　maintaining　high　geometric　accuracy.　The　point-related　subtask　focuses　on　learning　features　of　parcel　vertices　to　obtain　preliminary　vertices,　which　are　then　refined　based　on　detected　boundary　pixels　to　derive　valid　parcel　vertices　for　polygon　generation.　We　designed　a　spatial　and　channel　excitation　module　for　feature　interaction　to　enhance　interactions　between　points,　lines,　and　regions.　Finally,　the　generated　parcel　polygons　are　refined　using　the　Douglas-Peucker　algorithm　to　regularize　polygon　shapes.　We　evaluated　PLR-Net　using　high-resolution　GF-2　satellite　images　from　the　Shandong,　Xinjiang,　and　Sichuan　provinces　of　China　and　medium-resolution　Sentinel-2　images　from　The　Netherlands.　Results　showed　that　our　method　outperformed　existing　state-of-the-art　techniques　(e.g.,　BsiNet,　SEANet,　and　Hisup)　in　pixel-　and　object-based　geometric　accuracy　across　all　datasets,　achieving　the　highest　IoU　and　polygonal　average　precision　on　GF2　datasets　(e.g.,　90.84%　and　82.00%　in　Xinjiang)　and　on　the　Sentinel-2　dataset　(75.86%　and　47.1%).　Moreover,　when　trained　on　the　Xinjiang　dataset,　the　model　successfully　transferred　to　the　Shandong　dataset,　achieving　an　IoU　score　of　83.98%.　These　results　demonstrate　that　PLR-Net　is　an　accurate,　robust,　and　transferable　method　suitable　for　extracting　vectorized　parcels　from　diverse　regions　and　types　of　remote　sensing　images.　The　source　codes　of　our　model　are　available　at　https://github.com/mengmengli01/PLR-Net-demo/tree/main.