UCRG: Reliability-Gated Multi-Source Prediction Integration for Forest Point Cloud Semantic Segmentation Across Sensing Platforms

Unmanned aerial vehicle (UAV) scanning and mobile laser scanning provide detailed three-dimensional observations for forest inventory and structural monitoring for which point-wise separation of ground, understory, woody components, and foliage is fundamental. Modern segmentation pipelines can produce multiple predictions from different feature configurations, training objectives, random seeds, or network architectures. However, equal and fixed-weight fusion implicitly assume stable source quality, although the relative reliability of these predictions can vary across semantic classes and sensing conditions, leading to error propagation and negative transfer. We therefore formulate multi-source prediction integration as a calibration-supported class-wise reliability decision problem and propose the unified class-wise reliability gate (UCRG). The UCRG measures global and class-wise source reliability from labeled calibration data, uses shrinkage to stabilize class-specific estimates when labels are scarce, and conditionally performs soft fusion or hard source selection according to the reliability gap. An optional calibration-guided confusion-pair refiner further targets residual ambiguity between semantically similar classes. All the reliability estimation and parameter selection processes are performed without query labels. Experiments on FOR-instance, EvoMLS, and Lin3D cover UAV and mobile laser scanning together with cross-dataset transfer. Using one shared parameterization across the three primary protocols, the UCRG obtains 70.68% mIoU and records two negative-transfer episodes across the six evaluated episodes compared with six for fixed-weight fusion. When the gate parameters are selected solely from the available calibration support in each deployment, the UCRG reaches 59.20% mean mIoU in the PTv3-augmented Lin3D candidate pool. These results show that reliability-guided decisions can exploit class-dependent complementarity across heterogeneous frozen predictors and sensing conditions.