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+have+made+significant+advances+in+detecting+roads+from+satellite+images.+However%252C+existing+CNN+approaches+are+generally+repurposed+semantic+segmentation+architectures+and+suffer+from+the+poor+delineation+of+long+and+curved+regions.+Lack+of+overall+road+topology+and+structure+information+further+deteriorates+their+performance+on+challenging+remote+sensing+images.+This+paper+presents+a+novel+multi-task+graph+neural+network+(GNN)+which+simultaneously+detects+both+road+regions+and+road+borders%253B+the+inter-play+between+these+two+tasks+unlocks+superior+performance+from+two+perspectives%3a+(1)+the+hierarchically+detected+road+borders+enable+the+network+to+capture+and+encode+holistic+road+structure+to+enhance+road+connectivity+(2)+identifying+the+intrinsic+correlation+of+semantic+landcover+regions+mitigates+the+difficulty+in+recognizing+roads+cluttered+by+regions+with+similar+appearance.+Experiments+on+challenging+dataset+demonstrate+that+the+proposed+architecture+can+improve+the+road+border+delineation+and+road+extraction+accuracy+compared+with+the+existing+methods.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.63af978404f8e30c203e4d9d98f2272e%3frik%3dEvsRu1klN8FWPA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Nested+Graph+Network&FORM=IRPRST&ck=45277041F9C368E6D7B407C7B7BE01E8&selectedIndex=3&itb=0)
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