The accurate characterization and geo-localization of objects using image data and LiDAR are important for forestry, agriculture, urban planning, infrastructure monitoring, and related geospatial applications. However, reliability is affected by uncertainty introduced during sensor acquisition, LiDAR-image projection, segmentation, object-parameter estimation, and final geo-localization. This paper presents a proof-of-concept and method prototype for an uncertainty-aware LiDAR-image workflow in a forestry setting. The novelty of the work does not lie in proposing a new segmentation architecture, but in integrating image-based segmentation, LiDAR-image projection, DBH-level geometric estimation, stage-wise uncertainty propagation, and uncertainty-aware reconciliation of alternative estimates within a single modular workflow. The experimental evaluation was conducted on a limited pilot dataset consisting of 12 individual trees, multiple LiDAR acquisition viewpoints, and 18 high-resolution photographs. The number of trees is the number of independent analyzed objects, whereas the scans and photographs represent acquisition observations. Dense LiDAR point clouds provide many object-level geometric measurements, but these points are not interpreted as independent biological samples. Under the tested acquisition and processing conditions, the uncertainty-aware reconciliation step reduced the estimated spatial uncertainty to approximately 2.5 ± 0.4 cm. This value should be interpreted as a pilot result for the analyzed dataset, not as a general performance guarantee across forest types, tree species, stand densities, lighting conditions, or occlusion patterns. The contribution of this study is therefore positioned as a modular engineering-oriented uncertainty propagation and reconciliation workflow for DBH-level forestry localization. Potential use in robotics, infrastructure monitoring, or other high-precision geospatial applications is discussed only as a future direction requiring separate validation, larger datasets, and real-time implementation work.
Wołk et al. (Mon,) studied this question.