Paper Citation Record · LEDGER

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models

As of 7 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 6 inbound Pith citation observations for arXiv:2506.01277.

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

pith.paper-citation-record.v1
2506.01277 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:52:10.218789Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:12:10.857306Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:06:20.239381Z

Reference resolution

39 of 39 outbound references displayed

verified exact4
verified fuzzy20
unresolved14
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b870ec5-2ecf-4f1f-9453-a203fb34e9ec · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Learning Transferable Visual Models From Natural Language Supervision

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:08.326032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a14bccb-dcf5-45f6-b2a3-7a98fa9acb10 · outbound

This paper cites an unresolved cited work.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:52:13.349011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1141b9f7-2500-4a76-8e86-177824a35b62 · outbound

This paper cites PlaNet - photo geolocation with con- volutional neural networks.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models PlaNet - photo geolocation with con- volutional neural networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:08.387126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 148ace07-28fb-49c5-85a7-6ba3ad394ec2 · outbound

This paper cites PIGEON: Predicting Image Geolocations.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models PIGEON: Predicting Image Geolocations

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:52:10.869770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fee7286f-50b1-4b48-a43b-13fa9eb5c07f · outbound

This paper cites Image-Based Geolocation Using Large Vision-Language Models.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Image-Based Geolocation Using Large Vision-Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:08.551981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2dc9744-dbad-4f52-852a-7bfcb6c5134f · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:08.601226Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b13da83-2661-40bd-b0f2-60b00ee1c4f7 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:08.630800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30c03c75-74da-45cd-a91b-d1218652f3af · outbound

This paper cites Neural network ensembles.IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10):993–1001, 1990.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Neural network ensembles.IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10):993–1001, 1990

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:13.244819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a64f0ec5-2882-4c39-8759-e00d3c24ef5a · outbound

This paper cites Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:08.724712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dcf04f6-b1eb-42bb-9bd8-6608e1952def · outbound

This paper cites Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:52:10.616398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eb76507a-c259-486b-a3ce-9e3b118146cc · outbound

This paper cites The Mapil- lary Vistas dataset for semantic understanding of street scenes.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models The Mapil- lary Vistas dataset for semantic understanding of street scenes

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:13.100674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 25452abf-1357-45ad-88ff-b01d1e19727f · outbound

This paper cites GeoNames geographical database.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models GeoNames geographical database

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.947008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5169bda4-4f7d-42c3-a9a2-4dcc23558192 · outbound

This paper cites Qwen2.5-VL Technical Report.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Qwen2.5-VL Technical Report

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:09.047711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54ddb3a1-3421-4792-a162-fd35c6d2a87d · outbound

This paper cites Decoupled Weight Decay Regularization.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Decoupled Weight Decay Regularization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:09.081528Z

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Unavailable: canonical work link unavailable.

Observation 9ad4f2a6-8671-4596-87c9-e9dc77b2b336 · outbound

This paper cites OpenStreetView-5M: The Many Roads to Global Visual Geolocation.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models OpenStreetView-5M: The Many Roads to Global Visual Geolocation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:09.120531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 193b1e22-a3e6-49ce-8702-3dc16a604b34 · outbound

This paper cites The Claude 3 model family: Opus, Sonnet, Haiku.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models The Claude 3 model family: Opus, Sonnet, Haiku

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.868679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7631d502-cc44-4fdb-9cae-f977578e16e5 · outbound

This paper cites Visual Instruction Tuning.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Visual Instruction Tuning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:09.261284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8c927a4-7674-4873-acef-447b02dfb975 · outbound

This paper cites Mistral-Small-3.1-24B-Instruct-2503.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Mistral-Small-3.1-24B-Instruct-2503

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.768597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 00ff3373-43a2-4640-9a60-16d45423daa3 · outbound

This paper cites Revisiting IM2GPS in the Deep Learning Era.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Revisiting IM2GPS in the Deep Learning Era

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:09.371329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe163f24-9bf9-4a1e-9068-23c0b9512cd1 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:09.515725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c703f5e-d585-4cf8-9785-e4d83a4ec20f · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 24

Resolution: malformed identifier; no resolver link, observed 2026-08-07T11:52:09.555930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e70fda6e-38f2-403b-a5b8-43f8a2764ae7 · outbound

This paper cites Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:52:10.415712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7dbbf803-2ed1-4664-ac0f-00481042e4bb · outbound

This paper cites Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.675504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 947e2810-6378-40e2-b3cf-419d7b1409a3 · outbound

This paper cites Guidelines: • The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Guidelines: • The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.601524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9bcd7b9a-f880-435d-aec0-4ad3a7a11462 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include theoretical results.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.486517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 62811774-990e-4f8c-9518-4fa0e1845b78 · outbound

This paper cites The appendices include hyperparameters for SFT and information about computational resources used.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models The appendices include hyperparameters for SFT and information about computational resources used

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.393269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7dc63ab1-963b-4bcd-b217-e92994f77b42 · outbound

This paper cites Our appendices provide detailed instructions regarding implementation, hyperparameters, and experimental setup to facilitate reproduction.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Our appendices provide detailed instructions regarding implementation, hyperparameters, and experimental setup to facilitate reproduction

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.301578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1dba0e3d-4065-44e2-a2ff-01d772224858 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.220821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5cc46a9c-3c83-45bd-a47d-a8079eb9fd12 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not include experiments.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Guidelines: • The answer NA means that the paper does not include experiments

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:12.110456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 180e1af7-572b-4d03-8655-1beb3d7b8761 · outbound

This paper cites We also report model sizes and memory requirements.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models We also report model sizes and memory requirements

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:11.988746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13c45ece-fa9c-4e7a-836b-b714c48619b8 · outbound

This paper cites We use publicly available datasets, acknowledge relevant prior work, and are transparent about our methodologies.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models We use publicly available datasets, acknowledge relevant prior work, and are transparent about our methodologies

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:11.886563Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54276147-772a-4883-9c90-95322fe4ce64 · outbound

This paper cites Guidelines: • The answer NA means that there is no societal impact of the work performed.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Guidelines: • The answer NA means that there is no societal impact of the work performed

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:11.755852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bbf48e5d-e190-40dd-9d6b-1ed9331aa26a · outbound

This paper cites 27 Guidelines: • The answer NA means that the paper poses no such risks.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models 27 Guidelines: • The answer NA means that the paper poses no such risks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:11.621881Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3ef73617-d963-4be5-98a5-33ed5ad543e8 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not use existing assets.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Guidelines: • The answer NA means that the paper does not use existing assets

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:11.507759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8125e676-e808-4062-8bd4-3cebc78c0535 · outbound

This paper cites This documentation will be released alongside the dataset.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models This documentation will be released alongside the dataset

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:11.310523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04d645b3-ad62-46d9-a66e-d21e141be845 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:11.158826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c14f16c0-6101-481f-82fd-6053b2217ae5 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:10.997861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2183609d-e1a8-486a-821c-40d15e3a9070 · outbound

This paper cites G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:52:10.788245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b42b2fe3-b71a-4bb4-a522-2881740e889e · outbound

This paper cites Gemma 3 Technical Report.

GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models Gemma 3 Technical Report

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:08.967423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 5da9a71f-b019-499b-aeee-179f85ab3b52 · inbound

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation cites this paper.

A Global-Local Cross-Attention Network for Ultra-high Resolution Remote Sensing Image Semantic Segmentation GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:10.857306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1b68c6f-60f2-4d40-b263-be1b4624e2e8 · inbound

Building Lightweight Semantic Segmentation Models for Aerial Images Using Dual Relation Distillation cites this paper.

Building Lightweight Semantic Segmentation Models for Aerial Images Using Dual Relation Distillation GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T23:00:29.112400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a34a1ef-010f-4b43-8de7-cf7d9ee70961 · inbound

GDGS: 3D Gaussian Splatting Via Geometry-Guided Initialization And Dynamic Density Control cites this paper.

GDGS: 3D Gaussian Splatting Via Geometry-Guided Initialization And Dynamic Density Control GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:31.913301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4aab417b-4cd4-4650-9ded-b40c41c22580 · inbound

From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models cites this paper.

From Pixels to Places: A Systematic Benchmark for Evaluating Image Geolocalization Ability in Large Language Models GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:50:47.271597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 556d334e-529f-4853-a0ff-ca369f038b59 · inbound

Do VLMs See What Sensors Feel? A Scalable Expert-Guided Design for Wheelchair Accessibility Assessment from Street View cites this paper.

Do VLMs See What Sensors Feel? A Scalable Expert-Guided Design for Wheelchair Accessibility Assessment from Street View GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:06:20.241225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 33241b1a-4fed-42af-80bf-13ec998b5cfb · inbound

DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization cites this paper.

DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-07-30T22:45:19.083071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.