Paper Citation Record · LEDGER

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

As of 18 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 4 inbound Pith citation observations for arXiv:2502.00435.

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

pith.paper-citation-record.v1
2502.00435 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:03:25.883588Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:40:08.144755Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:30:07.463758Z

Reference resolution

23 of 23 outbound references displayed

verified exact1
verified fuzzy15
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 62ac218d-d9bc-4c60-a872-0e72414d84a5 · outbound

This paper cites SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.851173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0cd90f64-308d-4d92-9083-cb463fb18fbc · outbound

This paper cites Foundation Models for Generalist Geospatial Artificial Intelligence,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Foundation Models for Generalist Geospatial Artificial Intelligence,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.826236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b013c861-1152-4ee4-92f9-8f70fb787ed1 · outbound

This paper cites SpectralGPT: Spectral Remote Sensing Foundation Model,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models SpectralGPT: Spectral Remote Sensing Foundation Model,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T19:03:25.711667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97ae0741-0886-4a2f-93d7-6eaff5f1927b · outbound

This paper cites Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.781599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7bba644f-0e95-4d20-8952-f3b1ba7dade0 · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Masked Autoencoders Are Scalable Vision Learners,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.713263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b03d9a20-66a5-47cc-9809-c112e06d1f91 · outbound

This paper cites An Image Is Worth 16X16 Words: Transformers for Image Recognition At Scale,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models An Image Is Worth 16X16 Words: Transformers for Image Recognition At Scale,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.638563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 235cd4cd-3404-451f-950f-9d0d6ec41505 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T19:03:25.751273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96dfcd12-7d93-4847-ba73-e4b9799ad64f · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.565798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 98c49bde-852b-4588-bde6-97bc9cf9d488 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.532734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 71e69ae5-e13a-4e23-a7c9-76f86c2c80ba · outbound

This paper cites VMamba: Visual State Space Model.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models VMamba: Visual State Space Model

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T19:03:25.766370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baa1ee72-f9fb-4a36-b616-02a6810965c3 · outbound

This paper cites ChangeMamba: Remote Sensing Change Detection with Spatiotemporal State Space Model,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models ChangeMamba: Remote Sensing Change Detection with Spatiotemporal State Space Model,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.516287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ac0a7a7-3d47-4b27-98d6-094f8bdd0fac · outbound

This paper cites Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-09T19:03:26.201836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation af688298-8272-452c-bf30-02efccec52c3 · outbound

This paper cites PhilEO Bench: Evaluating Geo-Spatial Foundation Models,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models PhilEO Bench: Evaluating Geo-Spatial Foundation Models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.499184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 08561749-66db-40a8-9455-aa57c10a5d6c · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T19:03:25.791418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e34be80b-d29a-46a2-8b3b-d9a26009ab29 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models U-net: Convolutional networks for biomedical image segmentation,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T19:03:25.796343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 248698b9-0cef-4add-9872-16c350e598bb · outbound

This paper cites Segmentation Models Pytorch,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Segmentation Models Pytorch,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.470052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44f490cc-f96c-417b-a6ed-bfcb84ed155a · outbound

This paper cites Prithvi Pytorch,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Prithvi Pytorch,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.434202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4aa4ecba-0ae6-4dde-9efd-5fcf8087e5ed · outbound

This paper cites Creating xbd: A dataset for assessing building damage from satellite imagery,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Creating xbd: A dataset for assessing building damage from satellite imagery,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.416266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ad620ec0-1cad-4b8c-9a75-f580add4af26 · outbound

This paper cites Functional Map of the World,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Functional Map of the World,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.391381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5c5d8149-6c8f-4f46-939b-aff3ce61cbdd · outbound

This paper cites USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T19:03:25.832303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ebfc04b-a9fd-4e0a-923c-fcb5b5da3e27 · outbound

This paper cites OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Map- ping,.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Map- ping,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.366621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 78d559f6-fe32-4e5b-b2de-2372488e84f8 · outbound

This paper cites Available: https://github.com/qubvel/segmentation models.pytorch.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models Available: https://github.com/qubvel/segmentation models.pytorch

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T19:03:26.452038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7d9be533-1a5c-4a60-9121-9c382501e93d · outbound

This paper cites PhilEO Bench: Evaluating Geo-Spatial Foundation Models.

SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models PhilEO Bench: Evaluating Geo-Spatial Foundation Models

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T19:03:26.105866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 181db59b-57dc-4b0e-b812-c72f9755ae66 · inbound

Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook cites this paper.

Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

Reference 141

Resolution: unresolved; no resolver link, observed 2026-08-16T04:40:08.144755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 247554d9-6b69-464f-bfa0-65f0f4a54508 · inbound

Agentic AI for Remote Sensing: Technical Challenges and Research Directions cites this paper.

Agentic AI for Remote Sensing: Technical Challenges and Research Directions SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:46:28.026008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 069a86f2-2df3-4415-89ec-13d092a0d39b · inbound

Agentic AI for Remote Sensing: Technical Challenges and Research Directions cites this paper.

Agentic AI for Remote Sensing: Technical Challenges and Research Directions SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:59:27.642072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bcfce4db-c93f-48eb-a248-03730a0f1091 · inbound

State Space Models Meet Remote Sensing: A Survey cites this paper.

State Space Models Meet Remote Sensing: A Survey SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models

Reference 194

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:30:07.465170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.