Deeplabv3+ network does not make full use of multi-stage feature information in urban street view image segmentation,which leads to the shortcomings of large targets with holes,imprecise segmentation of edge target and so on.Considering the natural spatial position particularity of urban street view data,this paper proposes to introduce a height-driven effective attention model (HEAM) and a multi-stage feature fusion model (MFFM) on the basis of Deeplabv3+ network,and it embeds HEAM into the feature extraction network and atrous spatial pyramid pooling (ASPP) structure,which makes it pay attention to more spatial position information in the vertical direction.MFFM integrates multi-layer feature images to form multiple branches in the network and connect them to the network decoding end in turn.Successive up-sampling is used to improve the continuity of pixels during decoding.The improved network is verified and tested by CamVid urban street view data set.The results show that the network can effectively improve the deficiency of DeepLabv3+,and the location priori of the data set is properly used to enhance the segmentation effect.Mean intersection over union (MIoU) on CamVid test set reaches 68.2%.