Paper Citation Record · LEDGER

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover

As of 23 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.18099.

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pith.paper-citation-record.v1
2507.18099 v1

Coverage vector

measured 39 of 39 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T14:41:14.743267Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation f06e37ee-5eeb-4534-8eb2-7e4b0cf26143 · outbound

This paper cites Semi-supervised semantic segmentation in earth observation: The minifrance suite, dataset analysis and multi-task network study.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Semi-supervised semantic segmentation in earth observation: The minifrance suite, dataset analysis and multi-task network study

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.430978Z

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Observation aee42315-54a4-4fcd-9072-8a4f9008f664 · outbound

This paper cites Classification of imbalanced land-use/land-cover data using vari- ational semi-supervised learning.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Classification of imbalanced land-use/land-cover data using vari- ational semi-supervised learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.415428Z

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Observation 973cf90a-284c-4487-b882-408d1f6dbff2 · outbound

This paper cites Encoder- decoder with atrous separable convolution for se- mantic image segmentation.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Encoder- decoder with atrous separable convolution for se- mantic image segmentation

Reference 3

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Observation f3d85698-e592-486b-ad2d-fadfd1f358f7 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Semi-supervised semantic segmentation with cross pseudo supervision

Reference 4

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Observation 7679067f-fda6-4f33-9981-fdf1aaf69579 · outbound

This paper cites Tinycd: A (not so) deep learning model for change detection.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Tinycd: A (not so) deep learning model for change detection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.367728Z

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Observation f527d8d5-b188-43bd-9ca1-59449c76c1ad · outbound

This paper cites Xarray - https://docs.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Xarray - https://docs

Reference 6

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Observation 00352676-5f0f-4943-8c4b-45b1101215f3 · outbound

This paper cites Zarr - https://zarr.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Zarr - https://zarr

Reference 7

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Observation dc46413c-d6ac-498e-bb3a-2ac54f89ef6d · outbound

This paper cites Cross pseudo supervision framework for sparsely labelled geo-spatial images, 2024.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Cross pseudo supervision framework for sparsely labelled geo-spatial images, 2024

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.320716Z

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Observation a00f0a28-4b08-4467-8ecb-a8ad4aed4d61 · outbound

This paper cites Land cover classification of resources survey remote sensing images based on segmentation model.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Land cover classification of resources survey remote sensing images based on segmentation model

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.304536Z

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Observation 3436684c-5e8e-4762-8d3a-078eb28a7c61 · outbound

This paper cites Shapely: manipulation and anal- ysis of geometric objects, 2007–.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Shapely: manipulation and anal- ysis of geometric objects, 2007–

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.288297Z

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Observation 9af80b78-6015-4f80-b4f8-9367c002a87c · outbound

This paper cites Segmentation models py- torch.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Segmentation models py- torch

Reference 11

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Observation 60ae2372-3ad4-47c2-a7a0-ad44fa6d39da · outbound

This paper cites nnu-net: a self- configuring method for deep learning-based biomedi- cal image segmentation.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover nnu-net: a self- configuring method for deep learning-based biomedi- cal image segmentation

Reference 12

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Observation 21244941-ad4a-4f64-93dc-64547b6a4483 · outbound

This paper cites 2d semantic labeling contest - potsdam.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover 2d semantic labeling contest - potsdam

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.240775Z

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Observation 35c878b9-4532-45cc-94d3-35c1a7537ca8 · outbound

This paper cites A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery

Reference 14

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Observation e8c80e2a-25ee-420c-b1d8-304002dcf5ad · outbound

This paper cites Salcudean.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Salcudean

Reference 15

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Observation 1059009e-7d5f-4854-8de6-df492d026b10 · outbound

This paper cites Temporal Ensembling for Semi-Supervised Learning.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Temporal Ensembling for Semi-Supervised Learning

Reference 16

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Source-reported events for the cited work

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Observation 4188f0bd-c735-4330-bdf8-7feb492426a6 · outbound

This paper cites Monitoring earth surface dynamics with optical imagery.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Monitoring earth surface dynamics with optical imagery

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.209441Z

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Observation ebaa4d82-0efd-4246-93ab-7f1fbbb2b0c4 · outbound

This paper cites One model is enough: Toward multiclass weakly su- pervised remote sensing image semantic segmenta- tion.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover One model is enough: Toward multiclass weakly su- pervised remote sensing image semantic segmenta- tion

Reference 18

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Observation 91ea31ef-9205-47eb-a9df-ed498b5c14fb · outbound

This paper cites Simple and efficient: A semisupervised learn- ing framework for remote sensing image semantic segmentation.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Simple and efficient: A semisupervised learn- ing framework for remote sensing image semantic segmentation

Reference 19

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Observation 11072844-f79a-4362-bd7e-fcbb1e830621 · outbound

This paper cites An atmospheric correction in- tegrated lulc segmentation model for high-resolution satellite imagery, 2024.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover An atmospheric correction in- tegrated lulc segmentation model for high-resolution satellite imagery, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.159034Z

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Observation 8017b837-7e98-4c01-ad4a-2f4401fa6bca · outbound

This paper cites Weakly supervised semantic segmentation of satel- lite images.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Weakly supervised semantic segmentation of satel- lite images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.142937Z

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Observation e433d44a-f158-4c95-8e55-546ca9df76bf · outbound

This paper cites Planet dump re- trieved from https://planet.osm.org.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Planet dump re- trieved from https://planet.osm.org

Reference 22

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Source-reported events for the cited work

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Observation 091bc4eb-4873-45a9-9283-bb6b0131788a · outbound

This paper cites Fast building segmentation from satellite imagery and few local labels.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Fast building segmentation from satellite imagery and few local labels

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.109961Z

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Observation 4c73b346-531d-4514-89f9-b54f45e89b0d · outbound

This paper cites U-net: Convolutional networks for biomed- ical image segmentation.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover U-net: Convolutional networks for biomed- ical image segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.093596Z

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Observation e782bf1e-cb44-4628-91a2-809d1b53ece3 · outbound

This paper cites Self-supervised learning on small in-domain datasets can overcome supervised learning in remote sensing.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Self-supervised learning on small in-domain datasets can overcome supervised learning in remote sensing

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.077684Z

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Observation 8948019d-f79b-4f56-8c4c-6c5623e62fa4 · outbound

This paper cites Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T14:41:14.810918Z

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Observation 19bef5c9-85d7-4805-bd5b-819bd6d426bf · outbound

This paper cites Land use and land cover mapping using deep learning based segmentation ap- proaches and vhr worldview-3 images.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Land use and land cover mapping using deep learning based segmentation ap- proaches and vhr worldview-3 images

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.061320Z

Source-reported events for the cited work

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Observation 00158f74-1bb3-438e-aa0e-c7e2b0c94027 · outbound

This paper cites A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic wa- ters.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic wa- ters

Reference 28

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Observation 7eb3f1da-2534-4cf3-847a-11277365f750 · outbound

This paper cites Continental-Scale Building Detection from High Resolution Satellite Imagery.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Continental-Scale Building Detection from High Resolution Satellite Imagery

Reference 29

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Source-reported events for the cited work

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Observation fe42d078-f957-4089-a882-8e2e8f1b3f3e · outbound

This paper cites Vermote, D.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Vermote, D

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.027252Z

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Observation 6b512eeb-2658-4b37-bdba-67a92fe7c108 · outbound

This paper cites Dhc: Dual- debiased heterogeneous co-training framework for class-imbalanced semi-supervised medical image seg- mentation.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Dhc: Dual- debiased heterogeneous co-training framework for class-imbalanced semi-supervised medical image seg- mentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:15.009579Z

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Observation c2697309-b4ce-42bd-b5fa-14b91edb9f76 · outbound

This paper cites Towards generic semi-supervised framework for volumetric medical image segmentation.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Towards generic semi-supervised framework for volumetric medical image segmentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:14.990866Z

Source-reported events for the cited work

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Observation 71ce6329-5426-47bb-86cb-beaf5da9a273 · outbound

This paper cites Sd- cdnet: A semi-dual change detection network frame- work with super-weak label for remote sensing im- age.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Sd- cdnet: A semi-dual change detection network frame- work with super-weak label for remote sensing im- age

Reference 33

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Source-reported events for the cited work

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Observation 1c200b35-3597-41a3-a348-f11c1075e758 · outbound

This paper cites Semi-supervised semantic seg- mentation of remote sensing images with iterative contrastive network.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Semi-supervised semantic seg- mentation of remote sensing images with iterative contrastive network

Reference 34

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Source-reported events for the cited work

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Observation daf09b98-f5a9-4e85-8f37-9019a8637911 · outbound

This paper cites Self- supervised learning in remote sensing: A review.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Self- supervised learning in remote sensing: A review

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:14.941396Z

Source-reported events for the cited work

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Observation 5486c678-154b-4c63-bc59-0ff7570530b2 · outbound

This paper cites Mask deeplab: End-to-end image segmentation for change detection in high- resolution remote sensing images.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Mask deeplab: End-to-end image segmentation for change detection in high- resolution remote sensing images

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:14.923300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 888e08e6-7878-4061-a091-141f9f17ff16 · outbound

This paper cites A seman- tic segmentation method with category boundary for land use and land cover (lulc) mapping of very-high resolution (vhr) remote sensing image.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover A seman- tic segmentation method with category boundary for land use and land cover (lulc) mapping of very-high resolution (vhr) remote sensing image

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:14.906574Z

Source-reported events for the cited work

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Observation c0ff9581-cd8d-413e-9ee7-7d4cba525793 · outbound

This paper cites Semi-supervised semantic segmen- tation network via learning consistency for remote sensing land-cover classification.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover Semi-supervised semantic segmen- tation network via learning consistency for remote sensing land-cover classification

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:14.888627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aee29b85-fbb1-4f52-a603-068c55482dd5 · outbound

This paper cites A sur- vey of weakly-supervised semantic segmentation.

Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover A sur- vey of weakly-supervised semantic segmentation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:41:14.869733Z

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