Paper Citation Record · LEDGER

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence

As of 21 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2607.14127.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.14127 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T10:39:58.836824Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

11 of 11 outbound references displayed

verified exact0
verified fuzzy0
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a824f110-24b6-4115-bdcf-1c3897915289 · outbound

This paper cites Recommendation itu-r p.452-18: Prediction procedure for the evaluation of interference between stations on the surface of the earth at frequencies above about 100 mhz,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence Recommendation itu-r p.452-18: Prediction procedure for the evaluation of interference between stations on the surface of the earth at frequencies above about 100 mhz,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:57.270040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80d8ae40-e58c-45ec-9ae1-b842dfb694e1 · outbound

This paper cites Recommendation itu-r p.2108-1: Prediction of clutter loss,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence Recommendation itu-r p.2108-1: Prediction of clutter loss,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:57.352041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1a5c832-584f-487f-b3c5-49023b613378 · outbound

This paper cites Global 3d building pattern prediction using random forests and open geospatial data,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence Global 3d building pattern prediction using random forests and open geospatial data,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:57.472077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d51dddca-b1a2-43a4-931a-41de633c3a05 · outbound

This paper cites Estimating global building heights from footprint morphology using interpretable machine learning,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence Estimating global building heights from footprint morphology using interpretable machine learning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:57.608118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5496bf11-6698-4c98-a2cf-3cd9b86b8816 · outbound

This paper cites Mapping global forest canopy height through integration of gedi and landsat data,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence Mapping global forest canopy height through integration of gedi and landsat data,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:57.731346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e1b53c2-53ec-4af1-a8a7-1f44e15dc9de · outbound

This paper cites National-scale mapping of building height using sentinel-1 and sentinel-2 time series data,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence National-scale mapping of building height using sentinel-1 and sentinel-2 time series data,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:57.917639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 875b23d8-5ecc-472c-adcb-9699057dce83 · outbound

This paper cites 3d-globfp: A global building footprint height product from sentinel-1, sentinel-2, and openstreetmap,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence 3d-globfp: A global building footprint height product from sentinel-1, sentinel-2, and openstreetmap,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:58.091158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ed11497-6177-4e60-ba2a-320747e30f0c · outbound

This paper cites 3d elevation program (3dep),.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence 3d elevation program (3dep),

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:58.315726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69a24402-db71-4fe1-ba44-5cf73207f9a9 · outbound

This paper cites Pdal: Point data abstraction library,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence Pdal: Point data abstraction library,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:58.496687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43a29691-700b-41fd-9e5d-a951b3e31c03 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence Lightgbm: A highly efficient gradient boosting decision tree,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:58.655819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e21d68dd-d9e7-43ea-8af1-61be3ddc564e · outbound

This paper cites A unified approach to interpreting model predictions,.

Explainable Geospatial AI for Satellite Ground Station Siting Using LiDAR-Derived Terrain Intelligence A unified approach to interpreting model predictions,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T10:39:58.836824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.