Paper Citation Record · LEDGER

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2304.14065.

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pith.paper-citation-record.v1
2304.14065 v4

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measured 21 of 21 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:51:15.915179Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T07:26:54.517011Z

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Outbound references

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Pith citing papers

Observation d5cdcf32-ddec-4262-b7ea-d5d73cd7e1e3 · inbound

On the Generalizability of Foundation Models for Crop Type Mapping cites this paper.

On the Generalizability of Foundation Models for Crop Type Mapping Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:03:26.636563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 533f9222-805f-4fe3-a007-0f11b95ba492 · inbound

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities cites this paper.

Galileo: Learning Global & Local Features of Many Remote Sensing Modalities Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T21:51:15.915179Z

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Observation 212a9cf1-50d9-48f8-bbad-7a0f8af8487d · inbound

Frame-Level Captions for Long Video Generation with Complex Multi Scenes cites this paper.

Frame-Level Captions for Long Video Generation with Complex Multi Scenes Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:52:23.239488Z

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Observation 54272e7d-4105-4502-9eb6-aaf8ce49499f · inbound

Position Prediction Self-Supervised Learning for Multimodal Satellite Imagery Semantic Segmentation cites this paper.

Position Prediction Self-Supervised Learning for Multimodal Satellite Imagery Semantic Segmentation Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:38.444967Z

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Observation cb0398f7-8968-46fb-9b04-c20ef560112a · inbound

High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data cites this paper.

High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:59:41.611983Z

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Observation 46e429c1-f6c3-4e81-9fe5-96a1c3b9fb89 · inbound

TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis cites this paper.

TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-19T07:32:59.942671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9ee03fc4-afbf-4f09-8c68-9bc1c87dd9c7 · inbound

Farm-Level, In-Season Crop Identification for India cites this paper.

Farm-Level, In-Season Crop Identification for India Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T21:33:05.515779Z

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Observation 2bc17d79-eb13-4f22-bd11-464c45d20aed · inbound

Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal cites this paper.

Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T16:54:18.119238Z

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Observation a7205f70-eb23-449a-96e2-d6bdbf69c1ea · inbound

Invariant Features for Global Crop Type Classification cites this paper.

Invariant Features for Global Crop Type Classification Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:06:45.840499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4e6b3e1c-7be6-41e9-b667-facc05fca380 · inbound

Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications cites this paper.

Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 164

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:45:54.698268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d7fb664-96e8-4f14-89f1-da6855397176 · inbound

MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications cites this paper.

MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:48:15.124110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6da891e2-7e6b-43d6-8f1e-22c370bca2eb · inbound

Better Together: Evaluating the Complementarity of Earth Embedding Models cites this paper.

Better Together: Evaluating the Complementarity of Earth Embedding Models Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T11:53:14.853387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6835f29c-dcce-4fc6-a288-b79d7ed1acce · inbound

OlmoEarth v1.2: A more efficient family of OlmoEarth models cites this paper.

OlmoEarth v1.2: A more efficient family of OlmoEarth models Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:39:40.984739Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3585198f-a056-4ded-87cd-e2acaa518da3 · inbound

OlmoEarth v1.2: A more efficient family of OlmoEarth models cites this paper.

OlmoEarth v1.2: A more efficient family of OlmoEarth models Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T08:05:31.209847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 16ef0c53-9323-4743-b135-15a9f1a054ad · inbound

UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation cites this paper.

UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:44.846025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7e646c65-2fa6-443c-8b2c-914af4590790 · inbound

TESSERA v2: Scaling Pixel-wise Earth Foundation Models cites this paper.

TESSERA v2: Scaling Pixel-wise Earth Foundation Models Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-11T22:49:02.844739Z

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Unavailable: canonical work link unavailable.

Observation a4b5ae0d-af36-467c-a68d-df176fe544f8 · inbound

Predicting Viticulture Potential through an Ensemble of U-Net and a Geospatial Foundation Model cites this paper.

Predicting Viticulture Potential through an Ensemble of U-Net and a Geospatial Foundation Model Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-10T07:26:54.518840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d53a4718-35da-491c-97b7-7b1299c4124d · inbound

STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series cites this paper.

STS-NET: Spatio-Temporal Stress Network for Self-Supervised Crop Stress Detection using Satellite Image Time Series Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T14:24:48.970965Z

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Observation ca3a2fd9-7177-4fdd-a491-3ccf63732ab1 · inbound

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets cites this paper.

Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-31T23:35:24.993661Z

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Unavailable: canonical work link unavailable.

Observation de877398-cc37-4462-b648-78df83c0290d · inbound

How Usable Are Geospatial Foundation Models? A Systematic Evaluation of 89 Models cites this paper.

How Usable Are Geospatial Foundation Models? A Systematic Evaluation of 89 Models Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T12:09:33.073225Z

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Unavailable: canonical work link unavailable.

Observation 56df8de5-2567-4fdc-a001-39a85a0eabed · inbound

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series cites this paper.

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 139

Resolution: unresolved; no resolver link, observed 2026-08-06T18:32:22.577448Z

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Unavailable: canonical work link unavailable.