Grounded open-vocabulary 3D LiDAR AI for natural resource surveying

Natural resource surveying underpins Earth system governance by providing the structural evidence required for understanding, monitoring, and managing forests, terrain systems, and inland waters. However, many survey-relevant processes are inherently three-dimensional, and their reliable characterization remains constrained by two-dimensional observations and by inconsistencies across sensing platforms, seasons, and deployment conditions. Although three-dimensional (3D) LiDAR point clouds increasingly serve as a primary source of structural information, translating these data into survey-grade, transferable evidence remains a central challenge. Recent advances in open-vocabulary and language-steerable artificial intelligence promise greater semantic flexibility, enabling long-tail and policy-driven survey targets to be queried beyond fixed taxonomies. Yet semantic openness alone does not guarantee evidentiary credibility. In real-world surveying, domain shifts, sensor heterogeneity, and ecological dynamics can cause models to rely on spurious correlations rather than on causal, transferable structure, thereby undermining cross-domain consistency and interpretability. Here, we propose a Grounded Open-Vocabulary perspective for 3D LiDAR point-cloud understanding in natural resource surveying. The core principle is that open-vocabulary inference should be constrained by measurable three-dimensional structure, spatial and environmental context, and explicit uncertainty, such that only semantic claims supported by robust structural evidence are accepted. We argue that grounding is not an auxiliary constraint on open-vocabulary AI, but an organizing evidentiary framework that aligns language-based querying with survey-grade standards of credibility and verification. By reframing open-vocabulary 3D understanding as an evidentiary practice rather than a recognition task, this perspective provides a pathway toward robust, transferable, and interpretable AI systems capable of supporting long-term, multi-platform natural resource surveying and Earth system governance.