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Paper Citation Record · LEDGER

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring

As of 13 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2607.13651.

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

pith.paper-citation-record.v1
2607.13651 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T04:37:52.926640Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

18 of 18 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d048673c-4c86-45dc-a455-763590a1fed0 · outbound

This paper cites EarthNet2021: A large-scale dataset and challenge for earth surface forecasting as a guided video prediction task.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring EarthNet2021: A large-scale dataset and challenge for earth surface forecasting as a guided video prediction task

Reference 1

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no resolver link, observed 2026-08-02T04:37:50.767016Z

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Observation 603e72b5-3438-4dc0-abd1-b69807eea852 · outbound

This paper cites LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

Reference 2

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no resolver link, observed 2026-08-02T04:37:50.911660Z

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Observation 539d9db6-cf82-42ef-a31a-c3326a1d499c · outbound

This paper cites EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts

Reference 3

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no resolver link, observed 2026-08-02T04:37:51.032599Z

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Observation d81aff13-a292-4f6d-bfd5-4eb018912cab · outbound

This paper cites Earthformer: Exploring space-time transformers for earth system forecasting.Advances in Neural Information Processing Systems, 35:25390–25403, 2022.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Earthformer: Exploring space-time transformers for earth system forecasting.Advances in Neural Information Processing Systems, 35:25390–25403, 2022

Reference 4

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no resolver link, observed 2026-08-02T04:37:51.120404Z

Source-reported events for the cited work

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Observation 4f13c5ca-6c9f-4429-b02f-ddc645d009a8 · outbound

This paper cites EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting

Reference 5

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no resolver link, observed 2026-08-02T04:37:51.193266Z

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Observation 7df08347-7f71-497c-a9a3-5289617545c6 · outbound

This paper cites SEN12MS-CR-TS: A remote sensing data set for multi-modal multi-temporal cloud removal.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring SEN12MS-CR-TS: A remote sensing data set for multi-modal multi-temporal cloud removal.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14, 2022

Reference 6

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no resolver link, observed 2026-08-02T04:37:51.336233Z

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Observation 97b3adc6-d619-41fd-b581-ecc8c611326f · outbound

This paper cites UnCRtainTS: Uncertainty quantification for cloud removal in optical satellite time series.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring UnCRtainTS: Uncertainty quantification for cloud removal in optical satellite time series

Reference 7

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no resolver link, observed 2026-08-02T04:37:51.480786Z

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Observation 0d114133-ef19-4bd2-9ff4-7f05d7eb26b1 · outbound

This paper cites an unresolved cited work.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-02T04:37:51.606313Z digest=sha256:fee12838ecb2c2a4835a21b7221b287fb89b2e80ec78271d46d56c1c23c2b744

Observation 839ee5f1-7b8d-4fca-b2d4-9f3fc0028f3c · outbound

This paper cites Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model

Reference 9

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Observation 90e30528-6aa5-4489-8130-17c3aa69839c · outbound

This paper cites Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery.Advances in Neural Information Processing Systems, 35:197–211, 2022.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery.Advances in Neural Information Processing Systems, 35:197–211, 2022

Reference 10

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no resolver link, observed 2026-08-02T04:37:51.867031Z

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Observation b7976990-c8f2-4f32-a26a-bbd255a3b23f · outbound

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

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Scale-MAE: A scale-aware masked autoencoder for multiscale geospatial representation learning

Reference 11

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Observation d4473dda-7be9-4130-8b75-75a643fbc67b · outbound

This paper cites Foundation Models for Generalist Geospatial Artificial Intelligence.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Foundation Models for Generalist Geospatial Artificial Intelligence

Reference 12

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Observation 42c2dd0c-c448-4112-95e1-7bce21180a80 · outbound

This paper cites Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation

Reference 13

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Observation 7d977f46-0b87-4c7b-a8ff-5a30bda450e0 · outbound

This paper cites World Models.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring World Models

Reference 14

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Observation 75647aa9-105f-4066-b8f8-d93c453f55b6 · outbound

This paper cites Learning latent dynamics for planning from pixels.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Learning latent dynamics for planning from pixels

Reference 15

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no resolver link, observed 2026-08-02T04:37:52.587850Z

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Observation b30c32d6-54da-4e8d-9e85-34cf7becc861 · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Dream to Control: Learning Behaviors by Latent Imagination

Reference 16

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Observation a6d3ce0c-a988-450c-86ad-3fab4cce8404 · outbound

This paper cites Self-supervised learning from images with a Joint-Embedding Predictive architecture.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring Self-supervised learning from images with a Joint-Embedding Predictive architecture

Reference 17

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Observation 7dd992c2-250e-4e82-bad8-b5d1111c7200 · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

From Surface Forecasting to Observability Forecasting: A Latent World Model for Cloud-Aware EO Monitoring V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 18

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Pith citing papers

No inbound Pith citation observations are available.