Paper Citation Record · LEDGER

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.07925.

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

pith.paper-citation-record.v1
2506.07925 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:24:12.194070Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

41 of 41 outbound references displayed

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verified fuzzy21
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85be90c5-e70b-48eb-9013-b465cf4b9714 · outbound

This paper cites A review on image segmentation techniques with remote sensing perspective,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images A review on image segmentation techniques with remote sensing perspective,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:20.649035Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd512584-4d57-400a-8468-ec26ac7e81d7 · outbound

This paper cites fully convolutional network.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images fully convolutional network

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:22.434956Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c0ec9327-ef18-4e4b-968f-ad9ee803d50b · outbound

This paper cites By utilizing knowledge from large dataset, U-Net variants can achieve high performance with less training data.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images By utilizing knowledge from large dataset, U-Net variants can achieve high performance with less training data

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:21.987523Z

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Observation 0218246d-0c96-452b-b0a7-0643ef278e4d · outbound

This paper cites These variants introduce modifications within the encoder path to enhance feature extraction for change detection tasks.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images These variants introduce modifications within the encoder path to enhance feature extraction for change detection tasks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:21.802894Z

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Observation 45e35615-3f5e-45af-84db-77094d156d12 · outbound

This paper cites For example, Optimised U-Net [29] ,introduces bottleneck structures within skip connections.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images For example, Optimised U-Net [29] ,introduces bottleneck structures within skip connections

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:21.508186Z

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Observation 84151d81-00c5-40f5-991a-36c274bb4b89 · outbound

This paper cites With 3 main components of U- Net, they are trying to modify and manipulate among those parts.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images With 3 main components of U- Net, they are trying to modify and manipulate among those parts

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:21.213233Z

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Observation 6687b2cf-a634-420c-909f-b18a844a7631 · outbound

This paper cites A Unified Approach to Change Detection Using an Adaptive Ensemble of Extreme 8 Learning Machines,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images A Unified Approach to Change Detection Using an Adaptive Ensemble of Extreme 8 Learning Machines,

Reference 7

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:24:16.023409Z

Source-reported events for the cited work

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Observation 0521bdcf-0332-4dfa-ae3c-9da281e3c6a4 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 8

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Observation 78d9bc4e-1740-46d1-a8a1-9208c797d4be · outbound

This paper cites Review Article Digital Change Detection Techniques Using Remotely-Sensed Data,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Review Article Digital Change Detection Techniques Using Remotely-Sensed Data,

Reference 9

Resolution: verified exact; doi, observed 2026-08-07T05:24:15.043217Z

Source-reported events for the cited work

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Observation 1f71baf2-77e7-4eb1-9340-3732180a28eb · outbound

This paper cites Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images,

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:24:16.450493Z

Source-reported events for the cited work

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Observation e595764a-a947-4a16-a7f4-3187e79e3714 · outbound

This paper cites Densely Connected Convolutional Networks,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Densely Connected Convolutional Networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:20.322035Z

Source-reported events for the cited work

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Observation cc67b174-1230-47cf-aaea-51a9c9c82bdc · outbound

This paper cites A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images,

Reference 12

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Observation 87c6f5eb-98b4-4d14-bab1-8db518d3bd12 · outbound

This paper cites Toward Generalized Change Detection on Planetary Surfaces With Convolutional Autoencoders and Transfer Learning,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Toward Generalized Change Detection on Planetary Surfaces With Convolutional Autoencoders and Transfer Learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:20.043982Z

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Observation b20cd2e8-d3ed-42ed-8e8c-247719c05a8f · outbound

This paper cites U-Net Transformer: Self and Cross Attention for Medical Image Segmentation.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images U-Net Transformer: Self and Cross Attention for Medical Image Segmentation

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:24:15.517822Z

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Observation ed89d6fd-3a10-40bb-a60b-f40bf64cef1e · outbound

This paper cites Going Deeper with Convolutions,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Going Deeper with Convolutions,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:19.505436Z

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Observation 604c76f5-6143-4953-af6d-70cf1b7a15f4 · outbound

This paper cites Deep Learning-Based Change Detection in Remote Sensing Images: A Review,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Deep Learning-Based Change Detection in Remote Sensing Images: A Review,

Reference 16

Resolution: verified exact; doi, observed 2026-08-07T05:24:14.777562Z

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Observation 2ea9362e-698d-4ff0-91b3-1482c929e46a · outbound

This paper cites HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images,

Reference 17

Resolution: verified exact; doi, observed 2026-08-07T05:24:14.467488Z

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Observation e36f3d4a-8f24-4a78-aca5-e0f7c7f5a20d · outbound

This paper cites SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,

Reference 18

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Observation c161276e-4f70-453a-b318-bde2174ad906 · outbound

This paper cites Dense-UNet: A novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Dense-UNet: A novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network,

Reference 19

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Observation 6e80e4ee-d421-4527-a263-b04852b3f168 · outbound

This paper cites Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:19.806830Z

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Observation a5fe4fa8-ac00-43c1-ab4c-8b69d79aa85e · outbound

This paper cites Recalibrating Fully Convolutional Networks with Spatial and Channel ‘Squeeze & Excitation’ Blocks,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Recalibrating Fully Convolutional Networks with Spatial and Channel ‘Squeeze & Excitation’ Blocks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:18.325598Z

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Observation 12b80c37-b5a0-4ae1-b097-12de92f90f70 · outbound

This paper cites Road Extraction by Deep Residual U-Net,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Road Extraction by Deep Residual U-Net,

Reference 22

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Observation 0dcaf33f-5a05-4250-9842-2969834fee9c · outbound

This paper cites Recurrent residual U-Net for medical image segmentation,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Recurrent residual U-Net for medical image segmentation,

Reference 23

Resolution: verified exact; doi, observed 2026-08-07T05:24:13.934190Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4fe65818-364b-4b68-9330-26d12e5bc384 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Attention U-Net: Learning Where to Look for the Pancreas,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:19.144972Z

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Observation 1ec19f3e-48d0-4c62-ba29-82ba6b5126b4 · outbound

This paper cites Based on the calculated similarity score, the attention module generates weight map.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Based on the calculated similarity score, the attention module generates weight map

Reference 25

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Observation 9435adfd-f71b-4264-a488-bfe4ad28b648 · outbound

This paper cites UNet++: A Nested U-Net Architecture for Medical Image Segmentation,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images UNet++: A Nested U-Net Architecture for Medical Image Segmentation,

Reference 26

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Observation ec79ed82-93d5-405c-9cba-1d0820a533d8 · outbound

This paper cites It allows the network to focus on informative features.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images It allows the network to focus on informative features

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:20.964983Z

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Observation f9e08d66-23f3-456c-8de5-745fc0dee6ac · outbound

This paper cites USE-Net: Incorporating Squeeze- and-Excitation Blocks into U-Net for Prostate Zonal Segmentation of Multi-Institutional MRI Datasets,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images USE-Net: Incorporating Squeeze- and-Excitation Blocks into U-Net for Prostate Zonal Segmentation of Multi-Institutional MRI Datasets,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:18.889834Z

Source-reported events for the cited work

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Observation c14cd6ae-2fa8-49ff-bcf8-e92de45600f3 · outbound

This paper cites Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks,

Reference 29

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Observation fdfb7b2f-b251-4fec-b43c-f79b4975d255 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Deep Residual Learning for Image Recognition,

Reference 30

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Observation 23097c9d-8595-46ef-b9e8-4ba9d55e0ab5 · outbound

This paper cites Multi-Class U- Net for Segmentation of Non-Biometric Identifiers,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Multi-Class U- Net for Segmentation of Non-Biometric Identifiers,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:17.986703Z

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Observation bc153500-b012-4d4d-9165-2603370ee1b7 · outbound

This paper cites SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:17.713402Z

Source-reported events for the cited work

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Observation 4d7d344c-026d-4bed-9c7c-37ebcf4799ab · outbound

This paper cites A Siamese Swin-Unet for Image Change Detection,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images A Siamese Swin-Unet for Image Change Detection,

Reference 33

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Source-reported events for the cited work

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Observation 0b370e28-dc91-4814-b904-f6980a8983e9 · outbound

This paper cites Bilateral Attention U-Net with Dissimilarity Attention Gate for Change Detection on Remote Sensing Imageries,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Bilateral Attention U-Net with Dissimilarity Attention Gate for Change Detection on Remote Sensing Imageries,

Reference 34

Resolution: verified exact; doi, observed 2026-08-07T05:24:13.581446Z

Source-reported events for the cited work

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Observation 47bcb367-2e4c-454a-a287-3c47db6881b0 · outbound

This paper cites STCD- EffV2T U-Net: Semi Transfer Learning EfficientNetV2 T-U- Net Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images STCD- EffV2T U-Net: Semi Transfer Learning EfficientNetV2 T-U- Net Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images,

Reference 35

Resolution: verified exact; doi, observed 2026-08-07T05:24:13.228776Z

Source-reported events for the cited work

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Observation 73f6712c-7525-4d04-ac7e-1bd42c7657ae · outbound

This paper cites Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography,

Reference 36

Resolution: verified exact; doi, observed 2026-08-07T05:24:12.912783Z

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Observation 570cdbf7-14e9-491f-9ed6-a5d328ba55ee · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:17.403978Z

Source-reported events for the cited work

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Observation 78886f13-e8aa-43c3-9183-952f4be875ff · outbound

This paper cites Adam: A Method for Stochastic Optimization,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Adam: A Method for Stochastic Optimization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:17.087213Z

Source-reported events for the cited work

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Observation c582e210-399e-46da-8592-bb668468c0d3 · outbound

This paper cites NDR-UNet: Segmentation of Water Bodies in Remote Sensing using Nested Dense Residual U-Net,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images NDR-UNet: Segmentation of Water Bodies in Remote Sensing using Nested Dense Residual U-Net,

Reference 39

Resolution: malformed identifier; no resolver link, observed 2026-08-07T05:24:11.871293Z

Source-reported events for the cited work

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Observation b597d03d-5138-4770-9e23-812a56179439 · outbound

This paper cites Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:24:16.745117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0b316003-dae0-46d3-b5df-c64c230afdbf · outbound

This paper cites Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review Through Various Learning Paradigms,.

A Comparative Study of U-Net Architectures for Change Detection in Satellite Images Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review Through Various Learning Paradigms,

Reference 41

Resolution: verified exact; doi, observed 2026-08-07T05:24:12.549892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

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