Vertical-Driven Architectures
Collection
Physical AI system designs by CodeNinja, reasoned on Praxis, made living with Hyper Ontology. • 27 items • Updated
choice stringclasses 7
values | picked stringclasses 7
values | why stringclasses 7
values |
|---|---|---|
Learned model | None; the model register is empty by decision | No agent or model layer was wanted; vegetation analysis stays with the operator's own staff in its own tools |
License position | No model license exists to hold or trigger | With zero weights, ownership runs through the professional services agreement, not through a license |
Positioning class | GNSS guidance and correction service, with network RTK or an owned base station | Quality Level 1 accuracy is decided by this chain, so it is specified by baselines, correction source and test evidence |
Acquisition platform | Manned aircraft with a 4-band red, green, blue and near-infrared large-format camera | Unmanned data will not be considered, so the acquisition class is fixed by the requirement |
LiDAR sensing | Quality Level 1 sensor at a minimum 8 pulses per square meter | The 16 pulses per square meter option is priced separately and exercisable at the operator's option |
Pattern the design stands on | System of Context, with the ontology as a projection over the systems of record | Deliverables carry flight mission, sensor and processing provenance, so analysis joins to units, missions and seasons rather than to tiles |
Ground it runs on | The operator's ArcGIS Enterprise environment in the United States | Files are publish-ready, and publication stays with the operator's own GIS staff inside its own boundary |
Orthoimagery and LiDAR vegetation mapping ontology and register for an earth observation site
The object model and the model and equipment register from Baseline: One Flight of 4 Band Orthoimagery and LiDAR for Vegetation Mapping, an open reference architecture by CodeNinja Atoms for United States. Part of the Vertical-Driven Architectures series; every design in the series is also a row in the cumulative dataset https://huggingface.co/datasets/CodeNinjatools/vertical-driven-architectures.
- Read the paper: https://codeatoms.ai/vegetation-mapping-lidar-us/
- DOI: https://doi.org/10.5281/zenodo.23186673
- Source files and PDF: https://github.com/muhammadumar89/codeninja-research
- Live view: https://huggingface.co/spaces/CodeNinjatools/vegetation-mapping-lidar-us
Files
| File | What it holds |
|---|---|
objects.json |
16 typed objects (The site, The unit, Watercourse, Dam, Flight Mission, 4-Band Aerial Camera System, QL1 LiDAR Sensor System, GNSS/IMU Georeferencing Chain, Multispectral Orthoimagery Product, Classified LAS Point Cloud, Bare-Earth and Highest-Hit DEMs, QA/QC Accuracy Report, Professional Services Agreement, Monthly Itemized Invoice, Contractor Project Manager, Operator Project Manager), each with its anchor system, properties, status vocabulary and 14 typed links. Format hyper-ontology/1: designed with Praxis, implemented with Hyper Ontology. |
models.csv |
The model and equipment register from the paper's Table 4, with the reason for each choice. |
How to use it
import json
from huggingface_hub import hf_hub_download
p = hf_hub_download("CodeNinjatools/vegetation-mapping-lidar-us-ontology", "objects.json", repo_type="dataset")
objects = json.load(open(p))["objects"]
print([o["label"] for o in objects])
Made with
Designed on Praxis, CodeNinja's platform for designing physical AI systems. The object model imports into Hyper Ontology, which turns it into a living system. Both are in beta; join at https://codeatoms.ai.
Citation
CodeNinja Engineering Team and Umar Bilal. 2026. Baseline: One Flight of 4 Band Orthoimagery and LiDAR for Vegetation Mapping. CodeNinja. https://doi.org/10.5281/zenodo.23186673. CC BY 4.0.
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