Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:11:23.400903Z
Paper Citation Record · LEDGER
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification
As of 14 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2411.14560.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
15 of 15 outbound references displayed
- verified exact1
- verified fuzzy9
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b0653a27-dacb-43e8-b7c4-126b4bb1e469 · outbound
This paper cites GeoAI: Where machine learning and big data converge in GIScience,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification GeoAI: Where machine learning and big data converge in GIScience,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.840545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 009e68a3-4cc7-4713-a6df-444a3b8e2f9b · outbound
This paper cites an unresolved cited work.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T15:11:23.830853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation daffd5b1-0001-4f02-9d26-7743b86a1fa7 · outbound
This paper cites A review of location encoding for GeoAI: methods and applications,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification A review of location encoding for GeoAI: methods and applications,
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:11:23.360037Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 170a42d6-9a42-4452-b751-e0033d50cce2 · outbound
This paper cites Presence-only geographical priors for fine-grained image classification,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Presence-only geographical priors for fine-grained image classification,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.820395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7bf71b71-98cc-4701-aab2-795107c7d3ed · outbound
This paper cites Geography-aware self-supervised learning,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Geography-aware self-supervised learning,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.809275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 5c3b7aed-bb46-4614-aadb-cb5fdc538b3e · outbound
This paper cites GPS2Vec: Towards Generating Worldwide GPS Embeddings,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification GPS2Vec: Towards Generating Worldwide GPS Embeddings,
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:11:23.370354Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3433dec2-0b80-40fa-a2ea-947fae3d52b9 · outbound
This paper cites Sphere2Vec: A general-purpose location representation learning over a spherical surface for large -scale geospatial predictions,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Sphere2Vec: A general-purpose location representation learning over a spherical surface for large -scale geospatial predictions,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.797619Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 4bd4274b-96a8-4893-852f-5eddaad7956d · outbound
This paper cites Csp: Self-supervised contrastive spatial pre -training for geospatial-visual representations,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Csp: Self-supervised contrastive spatial pre -training for geospatial-visual representations,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.784155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation edd9208a-c402-4644-b919-1837e141b63d · outbound
This paper cites SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:11:23.379809Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3e477d9-eddb-4b94-b59d-edc6f40bfb98 · outbound
This paper cites Geoclip: Clip-inspired alignment between locations and images for effective worldwide geo- localization,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Geoclip: Clip-inspired alignment between locations and images for effective worldwide geo- localization,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.773522Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 77fbf8ff-2a04-4a46-9e4b-a5052084703c · outbound
This paper cites Rings, circles, and null-models for point pattern analysis in ecology,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Rings, circles, and null-models for point pattern analysis in ecology,
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:11:23.390968Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 33c8202c-8168-4d80-b81d-afc622496470 · outbound
This paper cites Local Indicator of Colocation Quotient with a Statistical Significance Test: Examining Spatial Association of Crime and Facilities,.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Local Indicator of Colocation Quotient with a Statistical Significance Test: Examining Spatial Association of Crime and Facilities,
Reference 12
- Resolution
- verified exact
- raw_fallback, observed 2026-08-12T15:11:23.503720Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation bb607174-4cb0-46ce-b4fe-8781830e1847 · outbound
This paper cites GeoImageNet: a multi -source natural feature benchmark dataset for GeoAI and supervised machine learning.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification GeoImageNet: a multi -source natural feature benchmark dataset for GeoAI and supervised machine learning
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.752583Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 3d150bd5-1bc1-4f45-b4ea-9bceeb024c04 · outbound
This paper cites Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi’s domain adaptability.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi’s domain adaptability
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.741778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation ea4f4b8a-4166-4fe9-bceb-64b1a7d4ac97 · outbound
This paper cites Available: https://proceedings.neurips.cc/paper_files/paper/2023/hash/1b57aaddf85ab01a 2445a79c9edc1f4b-Abstract-Conference.html.
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification Available: https://proceedings.neurips.cc/paper_files/paper/2023/hash/1b57aaddf85ab01a 2445a79c9edc1f4b-Abstract-Conference.html
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T15:11:23.763791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Pith citing papers
No inbound Pith citation observations are available.