The　acquisition　of　road　information　from　remote　sensing　images　is　of　significant　value　with　regard　to　intelligent　transportation　research.　This　study　focuses　on　enhancing　the　contour-learning　ability　to　mitigate　the　phenomenon　of　fragmented　road　segments　and　missing　connections　in　road　extraction.　A　novel　Deep　Feature-Review　(FR)　Transmit　Network　(TransNet)　is　proposed　to　review　and　facilitate　the　flow　of　contour　features　into　an　encoder　network.　Meanwhile,　multiscale　features　are　linked　via　a　bridge　between　the　encoder　and　the　decoder.　Compared　with　the　state-of-the-art　models　such　as　fully　convolutional　network　(FCN),　SegNet,　DeepLabv3,　D-LinkNet,　spatial　consistency-FCN,　and　generative　adversarial　network　(GAN),　the　proposed　network　achieves　better　overall　performance　for　the　Massachusetts　Roads　data　set,　with　accuracy,　precision,　recall,　and　mean　intersection-over-union　(IoU)　scores　of　97.48%,　83.72%,　78.13%,　and　0.6286%,　respectively.　For　the　DeepGlobe　Road　Extraction　data　set,　the　proposed　network　outperforms　FCN,　SegNet,　DeepLabv3,　D-LinkNet,　and　Deep　TransNet,　achieving　accuracy,　precision,　recall,　and　mean　IoU　scores　of　98.70%,　87.30%,　81.15%,　and　0.7244%,　respectively.　Overall,　these　experiments　indicate　that　the　proposed　network　can　effectively　address　the　phenomenon　of　fragmented　road　segments　and　poor　connectivity　in　remote　sensing　images,　indicating　its　potential　for　utilization　in　practical　intelligent　transportation　scenarios.