ecoSPLAT: A Specification for Distributed Ecological Structure Monitoring Using 360° Imagery and AI-Augmented Analysis

@manual{cnl2026ecosplat, author = {}, title = {ecoSPLAT: A Specification for Distributed Ecological Structure Monitoring Using 360° Imagery and AI-Augmented Analysis}, institution = {Canemah Nature Laboratory}, year = {2026}, number = {CNL-SP-2026-013}, month = {january}, url = {https://canemah.org/archive/document.php?id=CNL-SP-2026-013}, abstract = {Ecological science faces a persistent measurement gap: the three-dimensional structure of habitat fundamentally shapes species occurrence and community composition, yet quantifying this structure at relevant scales remains expensive, expertise-intensive, and temporally sparse. Meanwhile, citizen science platforms like iNaturalist have demonstrated that distributed observer networks can generate biodiversity data at unprecedented scales—but these observations document species presence without habitat context. This specification proposes ecoSPLAT, a methodology for coupling biodiversity observation with automated habitat structure documentation. When a naturalist photographs an organism for species identification, they simultaneously capture a 360° panorama of the surrounding habitat. Post-processing decomposes the spherical image into a systematic grid of perspective views (“terrariums”), each processed through single-image 3D reconstruction to extract structural metrics. Building on experimental work demonstrating that individual perspective extractions produce valid, analyzable 3D frustums despite limitations in cross-view geometric fusion, the approach adopts gradient analysis as its conceptual framework—characterizing how structure changes across space and time rather than inventorying absolute values at discrete points. This framing sidesteps depth calibration limitations while capturing ecologically meaningful variation. The specification details capture protocols, processing pipeline architecture, structural metric extraction, database integration, and validation strategies using existing seasonal 360° video archives from 25 biological field stations.} }