Land type provides a fundamental basis for understanding the spatial heterogeneity of territorial space. The evolution of land type research is reviewed by examining the theoretical perspectives of different academic schools and synthesizing advances in land type classification, mapping, and application. Building on recent technological developments and practical demands, future research priorities are further discussed. Existing studies indicate that land type classification has evolved from expert knowledge-based approaches toward intelligent methods supported by multi-source geospatial data and machine learning, leading to substantial improvements in classification accuracy and efficiency. Meanwhile, land type mapping has progressed from traditional manual cartography to digital spatial information processing, enabling more effective visualization and representation of spatial information. The application of land type has also expanded beyond land resource surveys and agricultural development to territorial spatial planning, ecological restoration, environmental governance, and sustainable development. Future efforts should focus on improving multi-source geospatial data integration, developing multi-scale land type classification frameworks, advancing visualization techniques for digital land type mapping, and strengthening the application of land type in comprehensive territorial spatial optimization. These advances are expected to provide methodological support for multi-scale land type classification, digital mapping, and intelligent applications, while further promoting the development of both the theoretical foundation and practical implementation of land type research.