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In recent years, the automatic ground object extraction from remote sensing images has been dramatically advanced by the fully convolutional networks (FCNs). It is an effective method to fuse high-resolution images and light detection and ranging (LiDAR) data in FCNs to improve the extraction accuracy and the robustness. However, the existing FCN-based fusion networks still face challenges in efficiency and accuracy. Methods: We propose a knowledge-aware and multi-scale fusion network (KMF‐ Net) for robust and accurate ground object extraction. The proposed network incorporates a knowledge aware module in the network encoder for better exploiting remote sensing knowledge between pixels. A series-parallel hybrid convolution module is developed to enhance multi-scale representative features from ground objects. Moreover, the network decoder uses a gradual bilinear interpolation strategy to obtain fine grained extraction results. Results: We evaluate KMFNet in the LuoJiaNET with four current mainstream ground object extraction methods (GRRNet, V-FuseNet, DLR and Res-U-Net) on ISPRS 2D semantic segmentation dataset. The comparative evaluation results show that KMFNet can obtain the best overall ac‐ curacy. Compared with the other four methods, KMFNet achieves a better effect by improving the overall accuracy by 3.20% and 2.82% on average in ISPRS-Vaihingen dataset and ISPRS-Potsdam dataset, re‐ spectively. Conclusions: KMFNet achieves the best extraction results by capturing the intrinsic pixel relationships and strengths the multi-scale representative and detailed features of ground objects. It shows great potential in high-precision remote sensing mapping applications. [ABSTRACT FROM AUTHOR]