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Paper Citation Record · LEDGER

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification

As of 18 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2506.19561.

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

pith.paper-citation-record.v1
2506.19561 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:11:45.553123Z

measured 16 of 16 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

16 of 16 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4e9be47-22da-4a67-a50f-fc8443c01721 · outbound

This paper cites Machine learning in modelling land-use and land cover-change (lulcc): Current status, challenges and prospects.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Machine learning in modelling land-use and land cover-change (lulcc): Current status, challenges and prospects

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:47.337235Z

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.

source=arxiv_source observed=2026-08-06T23:11:43.993288Z digest=sha256:a1c16c6a357de9630f174d2be6437e6f347434e1c79e395b013b55d215eeb46c

Observation 1e115694-0346-4482-a07e-b664e3d8d0df · outbound

This paper cites Pure data correction enhancing remote sensing image classification with a lightweight ensemble model.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Pure data correction enhancing remote sensing image classification with a lightweight ensemble model

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:47.166699Z

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.

source=arxiv_source observed=2026-08-06T23:11:44.108436Z digest=sha256:340384a1d12c9a3cc4168ce6661dfa486e08d75bca18d75fdc76bd493c544546

Observation 32e6d1e2-df92-4182-953f-74f63051757b · outbound

This paper cites Combining kan with cnn: Konvnext’s performance in remote sensing and patent insights.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Combining kan with cnn: Konvnext’s performance in remote sensing and patent insights

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:47.005684Z

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.

source=arxiv_source observed=2026-08-06T23:11:44.231581Z digest=sha256:e4d66607e954671910c179357a2a805e866beb8e296e604e53a4c96ac6ed6558

Observation 5e4b8f42-d9a3-4c71-8658-08c7d65a2d3a · outbound

This paper cites Rsmamba: Remote sensing image classification with state space model.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Rsmamba: Remote sensing image classification with state space model

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:46.861745Z

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.

source=arxiv_source observed=2026-08-06T23:11:44.322321Z digest=sha256:220366d4216cf6e91a66d22079e8cc46cb190b513d3d65c99b5bb390b7b600a3

Observation 61cfdd02-5c49-443a-83fb-5995d7cee5ca · outbound

This paper cites Remote sensing image classification using deep learning.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Remote sensing image classification using deep learning

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:46.730589Z

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.

source=arxiv_source observed=2026-08-06T23:11:44.442846Z digest=sha256:208c6529513f08916cc0ca0e1de69c2100a8222e2fc1097380002f0a6d06e19a

Observation e44b3770-db65-4094-9b4a-c9362a3a536f · outbound

This paper cites Review of vision transformer models for remote sensing image scene classification.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Review of vision transformer models for remote sensing image scene classification

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:46.610057Z

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.

source=arxiv_source observed=2026-08-06T23:11:44.524456Z digest=sha256:c4075b844efdc17b2c2c79c3f46c0b0d45c79c6430536b0715a8bf4adea7a996

Observation c0375b5a-1f71-4d32-963b-47ca3cc48314 · outbound

This paper cites Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:44.633339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:11:44.633339Z digest=sha256:c7dc9d76c7e37e0056a4a063ebc14b1b5babc8d215ee9b82b5092d2b2e712f79

Observation 3d0208ae-d728-45f3-89a8-ade8dda22a21 · outbound

This paper cites Vivit-prob: A radar echo extrapolation model based on video vision transformer and spatiotemporal sparse attention.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Vivit-prob: A radar echo extrapolation model based on video vision transformer and spatiotemporal sparse attention

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:46.468317Z

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.

source=arxiv_source observed=2026-08-06T23:11:44.749656Z digest=sha256:3e57613e3ad8d4514098ded230391b407b9a958e66c1afe90bbf5cb84f972cef

Observation db30c953-3490-408e-9f6e-42856db1ec76 · outbound

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

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:44.868546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:11:44.868546Z digest=sha256:696dbb1cde8ea9bec83c69aa010e4c300b28775ddf7e5390803db0b513dc77f6

Observation cb973502-4494-47ef-b70a-27c6de870c28 · outbound

This paper cites Vision Mamba: A Comprehensive Survey and Taxonomy.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Vision Mamba: A Comprehensive Survey and Taxonomy

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:45.025725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:11:45.025725Z digest=sha256:b9ac85a011f2fcae81c20eef520a3867561bc70c72dd72b152f3efc495687ad1

Observation b8505967-42ad-4a2b-b49f-69bb56885feb · outbound

This paper cites Mambavision: A hybrid mamba-transformer vision backbone.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Mambavision: A hybrid mamba-transformer vision backbone

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:46.315230Z

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.

source=arxiv_source observed=2026-08-06T23:11:45.148407Z digest=sha256:413e3b8ae6549fb5de78727560475807c3184a6ecdfd8a6932422e64d4b21be0

Observation 7c68e015-0a02-45c5-b168-c3d2ff3094ed · outbound

This paper cites Mambaout: Do we really need mamba for vision? In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 4484--4496, 2025.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Mambaout: Do we really need mamba for vision? In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 4484--4496, 2025

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:46.184598Z

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.

source=arxiv_source observed=2026-08-06T23:11:45.248768Z digest=sha256:a831437efdb99129ef68c6d051660309adeb2eed729c70ec95795a10a7116387

Observation cf11985b-8b6c-4ae0-84c6-f5fb47d86dee · outbound

This paper cites Aid: A benchmark data set for performance evaluation of aerial scene classification.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Aid: A benchmark data set for performance evaluation of aerial scene classification

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:46.011420Z

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.

source=arxiv_source observed=2026-08-06T23:11:45.329253Z digest=sha256:1d11f9fb2ae8f0ad3bea6057add87e5bafaae445cc289e00f70e06d747367270

Observation 1da43456-3714-4ebd-9728-9203fdb95a8e · outbound

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

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T23:11:45.441646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:11:45.441646Z digest=sha256:b4af0bb807875ba014c78680a7ef49f47294b0687c3d7255cba666fae9631673

Observation e55cc239-fe55-49f4-8176-c54d1fbddc2e · outbound

This paper cites Remote sensing image scene classification: Benchmark and state of the art.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Remote sensing image scene classification: Benchmark and state of the art

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:45.881634Z

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.

source=arxiv_source observed=2026-08-06T23:11:45.507357Z digest=sha256:797a8a850505d9cb2f12fdcb7ddbb6f03e06b15ca197c207e22fe70226fdbe12

Observation 92cfd410-de22-43aa-b927-4c89015c0d53 · outbound

This paper cites Bag-of-visual-words and spatial extensions for land-use classification.

MambaOutRS: A Hybrid CNN-Fourier Architecture for Remote Sensing Image Classification Bag-of-visual-words and spatial extensions for land-use classification

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:11:45.713787Z

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.

source=arxiv_source observed=2026-08-06T23:11:45.553123Z digest=sha256:5cd682b7211c552ad00973b661f856805389415d05eb073a1aeeca7112504e7f

Pith citing papers

No inbound Pith citation observations are available.