Paper Citation Record · LEDGER

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features

As of 11 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2501.10144.

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

pith.paper-citation-record.v1
2501.10144 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:26:44.966467Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

30 of 30 outbound references displayed

verified exact0
verified fuzzy17
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c1838a2c-9fe3-4eb5-bfcd-0e56e5146ecd · outbound

This paper cites Canopy averaged chlorophyll content pre- diction using convolutional autoencoder on hyperspectral data,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Canopy averaged chlorophyll content pre- diction using convolutional autoencoder on hyperspectral data,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.545801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1694854d-5b7e-4220-8dee-6b573e39c490 · outbound

This paper cites Enhancing deforestation monitoring in the brazilian amazon: A semi-automatic approach leveraging uncer- tainty estimation,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Enhancing deforestation monitoring in the brazilian amazon: A semi-automatic approach leveraging uncer- tainty estimation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.532352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bc850173-9504-4c33-9c38-0a9ff8a53add · outbound

This paper cites Sen1floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Sen1floods11: a georeferenced dataset to train and test deep learning flood algorithms for sentinel-1,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.517400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dbe34249-0774-4a29-bdf4-dcb65b466d23 · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Improved Baselines with Visual Instruction Tuning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.861667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cf2e9f3-cdc3-4d4f-a818-d66cdceaaecd · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Llava-next: Improved reasoning, ocr, and world knowledge,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.866767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b4ef0d6-8697-41fc-9267-72625460c83d · outbound

This paper cites Visual instruction tuning,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Visual instruction tuning,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.870831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb33002a-ee4f-46f5-ad82-f8456ef1c552 · outbound

This paper cites Blip- 3: A family of open large multimodal models,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Blip- 3: A family of open large multimodal models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.875200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2af9673b-755f-4fd6-ba7a-cd1e8b138c42 · outbound

This paper cites Geochat: Grounded large vision- language model for remote sensing,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Geochat: Grounded large vision- language model for remote sensing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.488360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e99ded90-b835-4b0e-adb9-5f79cfd646c5 · outbound

This paper cites Geollava: Efficient vision-language models for temporal change detection in remote sensing,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Geollava: Efficient vision-language models for temporal change detection in remote sensing,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.476126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca4e9245-c9bb-4459-b843-cc1052393104 · outbound

This paper cites SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.887132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd2f19f5-4f7e-45d9-8907-80706657e817 · outbound

This paper cites reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.891341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4863534a-e817-46a7-9594-825c2c60a759 · outbound

This paper cites First principles residual resistivity using locally self-consistent multiple scattering method.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features First principles residual resistivity using locally self-consistent multiple scattering method

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T19:26:45.085727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 58760e58-4351-44e1-a8ab-6582f79edd20 · outbound

This paper cites Spectral reconstruction from satellite multispectral imagery using convolution and transformer joint network,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Spectral reconstruction from satellite multispectral imagery using convolution and transformer joint network,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.463752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 061acac1-f5ce-41fb-b4cb-fef4bbe9b770 · outbound

This paper cites Ringmo: A remote sensing foundation model with masked image modeling,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Ringmo: A remote sensing foundation model with masked image modeling,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.451307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2cbe90f8-8d2f-4d07-94e2-66cf27b0fd8a · outbound

This paper cites Masked autoencoders are scalable vision learners,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Masked autoencoders are scalable vision learners,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.438591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bda0b774-b894-48e7-aaed-7e210aad771b · outbound

This paper cites Late-time Evolution and Instabilities of Tidal Disruption Disks.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Late-time Evolution and Instabilities of Tidal Disruption Disks

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T19:26:45.067195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 41ff7c79-c082-4b5c-9f4e-158b0e998fa9 · outbound

This paper cites Spectralgpt: Spectral remote sensing foundation model,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Spectralgpt: Spectral remote sensing foundation model,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.425343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 21f93b25-e620-4a28-beb6-81179c3601da · outbound

This paper cites Rsgpt: A remote sensing vision-language model and benchmark,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Rsgpt: A remote sensing vision-language model and benchmark,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.412442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1937ae6e-5625-4d8a-b76b-53a8f2514bc6 · outbound

This paper cites Earthmarker: A visual prompting multi-modal large language model for remote sensing,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Earthmarker: A visual prompting multi-modal large language model for remote sensing,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.400090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation afed98c1-59ec-4a35-877e-4be148390dd5 · outbound

This paper cites Combinatorics of generalized orthogonal polynomials of type $R_{II}$.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Combinatorics of generalized orthogonal polynomials of type $R_{II}$

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.925055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb283d58-5694-487e-bbb6-c1bfaa93ed9c · outbound

This paper cites LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.930836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 107ffc3c-f686-4b15-b1a2-65da33d38e76 · outbound

This paper cites GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.935014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d72f046e-e008-4199-937e-5e13bb5abb3f · outbound

This paper cites Skyeyegpt: Unifying remote sensing vision-language tasks via instruction tun- ing with large language model,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Skyeyegpt: Unifying remote sensing vision-language tasks via instruction tun- ing with large language model,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.281077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d51d1951-6a09-4315-942c-46bc30b80357 · outbound

This paper cites Ringmogpt: A unified remote sensing foundation model for vision, language, and grounded tasks,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Ringmogpt: A unified remote sensing foundation model for vision, language, and grounded tasks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.266758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f2d47d54-df15-40e7-9b89-6f41eef3f49c · outbound

This paper cites Teochat: A vision-language assistant for earth observation data,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Teochat: A vision-language assistant for earth observation data,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.252934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b576194a-f80a-4478-a2ef-8059ee3c040e · outbound

This paper cites VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.950567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeac5fb0-3a0c-4606-993f-0df4e692bf1e · outbound

This paper cites Functional map of the world,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Functional map of the world,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.239515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5901d53d-5e2b-4278-b5c2-7c087f02401d · outbound

This paper cites BigEarthNet: A large-scale benchmark archive for re- mote sensing image understanding,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features BigEarthNet: A large-scale benchmark archive for re- mote sensing image understanding,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.227417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1d9f7c85-4c4c-4afb-8b45-e0d0ccc77b26 · outbound

This paper cites Distributionally Robust Receive Combining.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Distributionally Robust Receive Combining

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T19:26:44.962285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8df9fe71-2f7c-403c-83cb-3bf198841104 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,.

A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:26:45.214254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.