The use of deep learning techniques for semantic segmentation in remote sensing has been increasingly prevalent. Effectively modeling remote contextual information and integrating high-level abstract features with low-level spatial features are critical challenges for semantic segmentation tasks. This article addresses these challenges by constructing a graph space reasoning (GSR) module and a dual-channel cross-attention upsampling (DCAU) module. Meanwhile, a new domain-incremental learning (DIL) framework is designed to alleviate catastrophic forgetting when the deep learning model is used in cross-domain. This framework makes a balance between retaining prior knowledge and acquiring new information through the use of frozen feature layers and multifeature joint loss optimization. Based on this, a new DIL of remote sensing semantic segmentation with multifeature constraints in graph space (GSMF-RS-DIL) framework is proposed. Extensive experiments, including ablation experiments on the ISPRS and LoveDA datasets, demonstrate that the proposed method achieves superior performance and optimal computational efficiency in both single-domain and cross-domain tasks. The code is publicly available at https://github.com/Huang WBill/GSMF-RS-DIL.