A lightweight network based DeepLabV3+ remote sensing image land feature segmentation method is proposed to address the errors caused by the loss of detail information and imbalanced categories in remote sensing image segmentation. Firstly, MobileNetV2 is adopted to replace the backbone network in original baseline network to improve training efficiency and reduce model complexity. Secondly, the dilation rate of atrous convolutions within ASPP structure is increased and max-pooling in final ASPP layer is incorporated to effectively capture context information at different scales. At the same time, SE attention mechanism is introduced into each branch of ASPP, and ECA attention mechanism is introduced after extracting shallow features to improve the model’s perception ability for different categories and details. Finally, the weighted Dice-Local joint loss function is used for optimization to address class imbalance issues. The improved model is validated on both the CCF and Huawei Ascend Cup competition datasets. Experimental results show that the proposed method outperforms original DeepLabV3+ model on both test sets, with various metrics showing different degrees of improvement. Among them, mIoU reaches 73.47% and 63.43%, representing improvements of 3.24% and 15.11%, respectively. The accuracy reaches 88.28% and 86.47%, showing enhancements of 1.47% and 7.83%, respectively. The F1 index reaches 84.29% and 77.04%, increasing by 3.86% and 13.46%, respectively. The improved DeepLabV3+ model can better solve the problems of loss of detail information and class imbalance, which improves the performance and accuracy of remote sensing image feature segmentation.