Paper Citation Record · LEDGER

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing

As of 18 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.18587.

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

pith.paper-citation-record.v1
2506.18587 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:50:30.279482Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy8
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1628c24c-8e1c-4aa7-adf1-43b87f1f57de · outbound

This paper cites write newline.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.042414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f75a0b93-4f2d-46bd-bad5-bd04e63222db · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.050943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9483b9c-f9ee-456d-b10e-98b198031c54 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Unsupervised learning of visual features by contrasting cluster assignments

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.058242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0c31ff6-4857-46c6-95de-333d67ae8b58 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing A simple framework for contrastive learning of visual representations

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.067523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15046720-289f-4871-865e-84c5f0673f9d · outbound

This paper cites an unresolved cited work.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.077527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f53b93c4-0a26-46f8-9676-9dbfc419c644 · outbound

This paper cites and He, K.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing and He, K

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.088359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13dfcd35-7129-4304-9a21-e73a0d6572e2 · outbound

This paper cites Timemae: Self-supervised representations of time series with decoupled masked autoencoders.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Timemae: Self-supervised representations of time series with decoupled masked autoencoders

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.099156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bf9022d-b84c-4527-b7b2-5fcf2ba11879 · outbound

This paper cites Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:30.946025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eaf5c26a-2772-41b4-a33d-ee969e9a6815 · outbound

This paper cites Sentinel-2: Esa's optical high-resolution mission for gmes operational services.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Sentinel-2: Esa's optical high-resolution mission for gmes operational services

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.116598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b44906cf-2844-4a5e-97bb-0f6e670abb33 · outbound

This paper cites an unresolved cited work.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:50:30.904606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cf656bec-cd19-4c91-b1f1-422dc16d6618 · outbound

This paper cites an unresolved cited work.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:50:30.880310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ff0eb9e0-33c8-46f5-829d-379d630d35b4 · outbound

This paper cites Copernicus sentinel-2a calibration and products validation status.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Copernicus sentinel-2a calibration and products validation status

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:30.856786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8694935f-eb9d-45ea-9542-94f64906cbff · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Bootstrap your own latent-a new approach to self-supervised learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.150802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70305270-d815-41d6-8c27-ef66a6d5e1b7 · outbound

This paper cites Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:30.808175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 686514b7-5f91-472d-9f27-903c93d9792a · outbound

This paper cites Momentum Contrast for Unsupervised Visual Representation Learning.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Momentum Contrast for Unsupervised Visual Representation Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.165163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8a512ef-4330-426c-8fb0-ec54478b58e6 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Masked autoencoders are scalable vision learners

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.171797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6dc54b4b-4b24-42cc-af42-b8e56042cfad · outbound

This paper cites Data-efficient image recognition with contrastive predictive coding.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Data-efficient image recognition with contrastive predictive coding

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.184254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4300c89-c321-48c6-a502-93f14e7e6197 · outbound

This paper cites Foundation models for generalist geospatial artificial intelligence.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Foundation models for generalist geospatial artificial intelligence

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:30.746430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 95fbfd65-a184-44c9-94bc-3ee136b7e399 · outbound

This paper cites Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.198236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50167d7e-5b27-44de-b43e-9815d79680ab · outbound

This paper cites Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:30.725008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 35b2a53c-b6f5-43fd-8477-e7805235c39b · outbound

This paper cites Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:30.697364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 03b8ccaf-9a4b-4199-8a65-b9fa80bf08e3 · outbound

This paper cites Learning from few labeled time series with segment-based self-supervised learning: application to remote-sensing.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Learning from few labeled time series with segment-based self-supervised learning: application to remote-sensing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:30.665367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6f28e99-1ce4-452b-887f-b1e81eed1c4e · outbound

This paper cites Lightweight, Pre-trained Transformers for Remote Sensing Timeseries.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.232961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45d3c1c3-a60e-4a55-946a-0078bb0174c0 · outbound

This paper cites SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.250258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6615130-2bcf-466d-a643-90cdab6bb5c1 · outbound

This paper cites Decoupling Common and Unique Representations for Multimodal Self-supervised Learning.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Decoupling Common and Unique Representations for Multimodal Self-supervised Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.257028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5516d5e2-1db0-4a70-90fc-6943f3fa437c · outbound

This paper cites Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.262770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f136689d-f938-45cd-88bf-65de8f790e92 · outbound

This paper cites Sits-former: A pre-trained spatio-spectral-temporal representation model for sentinel-2 time series classification.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Sits-former: A pre-trained spatio-spectral-temporal representation model for sentinel-2 time series classification

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:50:30.633836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fe27c0f8-dfbf-4a30-b15e-73e969335c8d · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing Barlow twins: Self-supervised learning via redundancy reduction

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T18:50:30.279482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.