3DLCDM: Hybrid supervision for land cover discovery mapping of emerging urban structures in 3D remote sensing

Abstract

Urban environments are characterized by continuous transformation, with new buildings, innovative infrastructures, evolving landforms, and emerging vegetation constantly reshaping the urban fabric. These dynamic changes create previously unannotated land cover classes that modify surface albedo, alter drainage patterns, and influence carbon storage, thereby affecting local climates, resource flows, and ecosystem services. Therefore, traditional land cover mapping methods based on static semantic labels are inherently limited. Even the most meticulously annotated datasets cannot comprehensively account for the full spectrum of urban classes. As urban environments continue to evolve, these static methods fail to capture the continual appearance of previously unannotated classes. This limitation leads to maps that quickly become outdated, incomplete, and imprecise, thereby impeding accurate environmental monitoring. To address this critical challenge, we propose Land Cover Discovery Mapping (LCDM), which integrates novel class discovery with land cover mapping, and we present an innovative end-to-end hybrid supervision framework, 3DLCDM, to implement LCDM in 3D remote sensing. The system has been tested on two high-resolution 3D point cloud datasets: one acquired via airborne LiDAR in Canada and the other obtained primarily using UAV-based LiDAR in Germany. Experimental results reveal that our 3DLCDM framework increases the mIoU for novel classes by up to 16.95% on the DALES dataset and up to 24.43% on the H3D dataset compared to baseline methods, demonstrating effective discovery capabilities under evaluation conditions that are procedurally equivalent to encountering genuinely novel urban features in practice. The proposed 3DLCDM framework demonstrates the potential to enable the continuous generation of up-to-date land cover maps that capture dynamic changes in urban morphology, thereby significantly advancing land cover discovery mapping. Furthermore, strong generalization across multiple datasets and urban feature types demonstrates the robustness of the framework’s discovery mechanisms and its capability to deliver high-fidelity maps that scale across diverse urban environments.