3DLCDM: Hybrid supervision for land cover discovery mapping of emerging urban structures in 3D remote sensing
Bibliographic record
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 This limitation leads to maps that quickly become outdated, incomplete, and imprecise, thereby impeding accurate environmental monitoring.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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".