Paper Citation Record · LEDGER

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images

As of 20 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 2 inbound Pith citation observations for arXiv:2507.11143.

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

pith.paper-citation-record.v1
2507.11143 v1

Coverage vector

measured 62 of 62 reference resolution

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T22:26:08.721263Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T14:05:46.184129Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 75ad6d5c-4884-410d-8aac-73a8af7899df · outbound

This paper cites Landslides,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslides,

Reference 1

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9d13023c-f176-4edd-a84b-4872d116c13e · outbound

This paper cites The impact of climate change on landslide hazard and risk,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images The impact of climate change on landslide hazard and risk,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:47.192950Z

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Observation 82ace477-19f1-498a-8f86-dad8cdb81927 · outbound

This paper cites The international emergency disasters database,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images The international emergency disasters database,

Reference 3

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Observation 1ca07f8e-0e63-4c89-8032-2b97e9d7d1a4 · outbound

This paper cites Crozier, The Nature of Landslide Hazard Impact, chapter 2, pp.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Crozier, The Nature of Landslide Hazard Impact, chapter 2, pp

Reference 4

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Observation fc5da8a4-6e7c-48b2-a5c9-db2dc264d9e3 · outbound

This paper cites Landslide in india,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide in india,

Reference 5

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Observation 3b3c4d54-da95-4169-be28-c744d8220acb · outbound

This paper cites Landslide in ethiopia,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide in ethiopia,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:46.388613Z

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Observation 56a41282-7090-44d2-8298-a55e434601ea · outbound

This paper cites Landslide inventory maps: New tools for an old problem,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide inventory maps: New tools for an old problem,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:46.269506Z

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Observation 337c2279-dd67-4a6c-a996-1de7a2a0e090 · outbound

This paper cites Landslide mapping from aerial photographs using change detection-based markov random field,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide mapping from aerial photographs using change detection-based markov random field,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:46.064868Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6fecb8a3-20c1-4837-9d28-dfbe36c128aa · outbound

This paper cites Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:45.957262Z

Source-reported events for the cited work

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Observation 79023604-93de-4ef1-8f7d-d558d85ba930 · outbound

This paper cites Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:45.837046Z

Source-reported events for the cited work

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Observation 7a862123-4a09-4be7-81d2-1e4dc9d34b2e · outbound

This paper cites Analysis of lidar-derived topo- graphic information for characterizing and differentiating landslide morphology and activity,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Analysis of lidar-derived topo- graphic information for characterizing and differentiating landslide morphology and activity,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:45.679468Z

Source-reported events for the cited work

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Observation d855e5ea-84c1-4f16-b360-8f51e81b913e · outbound

This paper cites Giant landslides, topography, and erosion,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Giant landslides, topography, and erosion,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:45.430664Z

Source-reported events for the cited work

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Observation 83b3eee9-617d-4fa6-b830-00854b37cfb1 · outbound

This paper cites Relation between land cover and landslide susceptibility in val d’aran, pyrenees (spain): Historical aspects, present situation and forward prediction,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Relation between land cover and landslide susceptibility in val d’aran, pyrenees (spain): Historical aspects, present situation and forward prediction,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:45.246592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a79f7bc-8d0e-4865-bdc3-88bef9ceb4bb · outbound

This paper cites Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:45.083407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1c1a334-3d1d-42f4-b5a6-8f649f466799 · outbound

This paper cites An overview of the applications of earth observation satellite data: impacts and future trends,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images An overview of the applications of earth observation satellite data: impacts and future trends,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:44.858343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9064f553-3209-4d57-abbb-ac94d99a7a1a · outbound

This paper cites Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review,

Reference 16

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Source-reported events for the cited work

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Observation 6e8e8a28-3994-4586-8df0-3de7dd88dd41 · outbound

This paper cites Uav & satellite synergies for optical remote sensing applications: A liter- ature review,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Uav & satellite synergies for optical remote sensing applications: A liter- ature review,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:44.424000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e7792b7f-c6ec-4436-a209-01b8c86a0939 · outbound

This paper cites Review on remote sensing methods for landslide detec- tion using machine and deep learning,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Review on remote sensing methods for landslide detec- tion using machine and deep learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:44.294759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9acf0e39-2565-476c-aad1-ea5c526397ba · outbound

This paper cites The outcome of the 2022 landslide4sense competi- tion: Advanced landslide detection from multisource satellite imagery,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images The outcome of the 2022 landslide4sense competi- tion: Advanced landslide detection from multisource satellite imagery,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:44.131261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e0a8112-9458-4b21-a4f1-f5a67b3fd8e8 · outbound

This paper cites Ma- chine learning techniques in landslide susceptibility mapping: a survey and a case study,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Ma- chine learning techniques in landslide susceptibility mapping: a survey and a case study,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:44.029345Z

Source-reported events for the cited work

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Observation 060a7416-4c32-4249-81cf-572aaafe97f8 · outbound

This paper cites Revamping land cov- erage analysis using aerial satellite image mapping,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Revamping land cov- erage analysis using aerial satellite image mapping,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:43.931739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2e55d0a-0321-4e30-901f-c2ea51c5f90c · outbound

This paper cites Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the inje area, korea,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the inje area, korea,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:43.824868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation efdb23e7-9f0c-4ef3-a0cd-feedf9feb907 · outbound

This paper cites Object-oriented landslide mapping using zy-3 satellite imagery, random forest and mathematical morphology, for the three-gorges reservoir, china,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Object-oriented landslide mapping using zy-3 satellite imagery, random forest and mathematical morphology, for the three-gorges reservoir, china,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:43.633983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 18027c25-e979-45c9-88b9-ea65fe63f40b · outbound

This paper cites Reg- sa–unet++: A lightweight landslide detection network based on single- temporal images captured postlandslide,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Reg- sa–unet++: A lightweight landslide detection network based on single- temporal images captured postlandslide,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:43.485602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a9e30394-9409-4924-98f1-fdeae0187c77 · outbound

This paper cites A landslide extraction method of channel attention mechanism u-net network based on sentinel-2a remote sensing images,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images A landslide extraction method of channel attention mechanism u-net network based on sentinel-2a remote sensing images,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:43.341613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d58cb50a-7f56-44cf-b57b-f341ff2c3186 · outbound

This paper cites Drs-unet: A deep semantic segmentation network for the recognition of active landslides from insar imagery in the three rivers region of the qinghai–tibet plateau,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Drs-unet: A deep semantic segmentation network for the recognition of active landslides from insar imagery in the three rivers region of the qinghai–tibet plateau,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:43.224287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2236b70a-a5e5-4265-ab3f-d6688a627ae7 · outbound

This paper cites Landslide susceptibility prediction based on image semantic segmentation,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide susceptibility prediction based on image semantic segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:43.112006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ac24c242-7fd7-430f-a321-e68cd17c1805 · outbound

This paper cites Landslide recognition from multi- feature remote sensing data based on improved transformers,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide recognition from multi- feature remote sensing data based on improved transformers,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:42.984637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8f8a8acb-bb03-4ba7-adea-4630b96ee1ad · outbound

This paper cites An improved segmentation method for automatic mapping of cone karst from remote sensing data based on deeplab v3+ model,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images An improved segmentation method for automatic mapping of cone karst from remote sensing data based on deeplab v3+ model,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:42.852277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6b61737-5280-470c-bc6a-87abc1028c51 · outbound

This paper cites Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:42.725831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 409231d6-96c4-4990-9c6e-eeda7f7355f8 · outbound

This paper cites Relict landslide detection using deep- learning architectures for image segmentation in rainforest areas: a new framework,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Relict landslide detection using deep- learning architectures for image segmentation in rainforest areas: a new framework,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:42.617079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23fee5a2-74ea-41fb-9fd0-27148dd2fbef · outbound

This paper cites Deep learning for landslide recognition in satellite architecture,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Deep learning for landslide recognition in satellite architecture,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:42.464406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d2b108e-3ca1-4df7-a49a-5b23c84a190a · outbound

This paper cites Landslide detection using multi-scale image segmentation and different machine learning models in the higher himalayas,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide detection using multi-scale image segmentation and different machine learning models in the higher himalayas,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:42.363053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f044d6ee-68e0-4909-a739-d4ba331b18bc · outbound

This paper cites SAR-based landslide classification pretraining leads to better segmentation.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images SAR-based landslide classification pretraining leads to better segmentation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:21:38.450534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0632a352-7ab4-4022-b6c6-0c2ebfc35721 · outbound

This paper cites Landslide identification from post-earthquake high-resolution remote sensing images based on resunet–bfa,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide identification from post-earthquake high-resolution remote sensing images based on resunet–bfa,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:42.233373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dbd79020-288b-407c-bdc2-6d8478bb17ca · outbound

This paper cites Enhanced u-net++ for improved semantic segmentation in landslide detection,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Enhanced u-net++ for improved semantic segmentation in landslide detection,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:42.130228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d9bdb1e-81b2-4e95-9377-01f39bbb3fd0 · outbound

This paper cites Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:41.996444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cb880342-1014-438a-ab09-8859fcce1c7c · outbound

This paper cites Convolutional neural networks applied to semantic segmentation of landslide scars,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Convolutional neural networks applied to semantic segmentation of landslide scars,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:41.868499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ebf7ca96-defa-4c00-b868-ebabd58e2bc1 · outbound

This paper cites A practical trial of landslide detection from single-temporal landsat8 images using contour-based proposals and random forest: A case study of national nepal,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images A practical trial of landslide detection from single-temporal landsat8 images using contour-based proposals and random forest: A case study of national nepal,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:41.759079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4a759e78-6cad-4fcb-8f90-0285e34700bc · outbound

This paper cites Landslide detection based on contour-based deep learning framework in case of national scale of nepal in 2015,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide detection based on contour-based deep learning framework in case of national scale of nepal in 2015,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:41.618591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a52ebdb-7d8a-46dc-b5fe-2f514865bcae · outbound

This paper cites Landslide detection from open satellite imagery using distant domain transfer learning,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide detection from open satellite imagery using distant domain transfer learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:41.476367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b868ec46-faa0-4596-865d-f91ade66597e · outbound

This paper cites Landslide detection for remote sensing images using a multi-label classification network based on bijie landslide dataset,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide detection for remote sensing images using a multi-label classification network based on bijie landslide dataset,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:41.320372Z

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Observation b669d786-fc37-4bde-bd5e-8301cadf0dc6 · outbound

This paper cites Landslide4sense: Reference benchmark data and deep learning models for landslide detection,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide4sense: Reference benchmark data and deep learning models for landslide detection,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:41.166143Z

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Observation 6c4368df-3f23-4cd8-a317-91926624faba · outbound

This paper cites Adam: A Method for Stochastic Optimization.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Adam: A Method for Stochastic Optimization

Reference 44

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Observation de0d9ca6-758a-491b-a422-882d5e15cbcd · outbound

This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Cutmix: Regularization strategy to train strong classifiers with localizable features,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:40.999328Z

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Observation 2a3ff4fc-1264-43c6-8c32-00e38b04fc18 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:40.854616Z

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Observation fedd7158-96f5-4d9c-a94e-722960fb5f57 · outbound

This paper cites Rectifier nonlinearities improve neural network acoustic models,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Rectifier nonlinearities improve neural network acoustic models,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:40.718402Z

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Observation 1a3a8b84-72ba-4a6b-92a0-15f0d149bec7 · outbound

This paper cites Focal loss for dense object detection,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Focal loss for dense object detection,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:40.590170Z

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Observation 492ba174-50fb-4850-b052-877118ba9583 · outbound

This paper cites A comprehensive survey of loss functions in machine learning,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images A comprehensive survey of loss functions in machine learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:40.455220Z

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Observation 64cf2c2a-366f-48ff-99ef-e52093925dfd · outbound

This paper cites Generalized intersection over union: A metric and a loss for bounding box regression,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Generalized intersection over union: A metric and a loss for bounding box regression,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:40.321939Z

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Observation 98e619cf-5b60-408f-a47d-54f310b57868 · outbound

This paper cites Tversky loss function for image segmentation using 3d fully convolu- tional deep networks,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Tversky loss function for image segmentation using 3d fully convolu- tional deep networks,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:40.213274Z

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Observation b8f048bd-69e3-4c73-86bf-47da6c6a0c5e · outbound

This paper cites The lov ´asz hinge: A novel convex surrogate for submodular losses,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images The lov ´asz hinge: A novel convex surrogate for submodular losses,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:40.089414Z

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Observation 9de0db00-d32d-4c28-a295-632312c68e97 · outbound

This paper cites Boundary loss for remote sensing imagery semantic segmentation,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Boundary loss for remote sensing imagery semantic segmentation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:39.924092Z

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Observation c409a2dc-d661-4f9a-8c6a-8b302481c28f · outbound

This paper cites A compre- hensive study on center loss for deep face recognition,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images A compre- hensive study on center loss for deep face recognition,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:39.735957Z

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Observation b981aba4-22db-4bcd-81ad-9cdbbbb1dc96 · outbound

This paper cites A simple method to improve the quality of ndvi time-series data by integrating spatiotemporal information with the savitzky-golay filter,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images A simple method to improve the quality of ndvi time-series data by integrating spatiotemporal information with the savitzky-golay filter,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:39.546626Z

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Observation 7470b6b5-192a-4a36-94ee-24a23eb51a68 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 56

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Observation 301d35e0-0220-4203-9be4-f980fe2b3200 · outbound

This paper cites Mobilenetv3 for image classification,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Mobilenetv3 for image classification,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:39.339572Z

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Observation bef5079a-5d6d-48ed-9f71-463e4b1bfbe2 · outbound

This paper cites Efficientnet,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Efficientnet,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:39.189120Z

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Observation 0d8a8c01-25af-4ea3-b406-3b5026511001 · outbound

This paper cites Squeeze-and-excitation networks,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Squeeze-and-excitation networks,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:38.974775Z

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Observation 2d3dd40a-c838-465e-8f1d-f69a7f8999a1 · outbound

This paper cites Cbam: Convolutional block attention module,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Cbam: Convolutional block attention module,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:38.778275Z

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Observation e061b10c-92b1-4f8f-8c41-be74dbe9524f · outbound

This paper cites Landslide detection and segmentation using remote sensing images and deep neural networks,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images Landslide detection and segmentation using remote sensing images and deep neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:21:38.589141Z

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Observation 77231ced-5be0-49f0-a0ee-42133b422541 · outbound

This paper cites At- tention is all you need,.

RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images At- tention is all you need,

Reference 62

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Pith citing papers

Observation ed2ebc89-f97d-4503-9efe-c68c1a5e4beb · inbound

Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data cites this paper.

Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:31:26.193934Z

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Observation dc521dbb-84e3-42ee-974a-ff46352f4fcb · inbound

Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data cites this paper.

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:46.185974Z

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