Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction | Paper Citation Record

Observation c05994bf-7621-47e0-9260-3492510999c3 · outbound

This paper cites Google earth engine: Planetary-scale geospatial analysis for everyone,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Google earth engine: Planetary-scale geospatial analysis for everyone,

Reference 1

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Observation a95ba615-1d59-4a3a-98e0-90559bc11339 · outbound

This paper cites Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics,

Reference 3

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This paper cites A semi-supervised deep rule-based approach for complex satellite sensor image analy- sis,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A semi-supervised deep rule-based approach for complex satellite sensor image analy- sis,

Reference 4

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This paper cites Global trends in satellite-based emergency mapping,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Global trends in satellite-based emergency mapping,

Reference 5

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Observation 70f9d67e-3ad0-4a13-9fc1-9bfe6488a5bb · outbound

This paper cites Mapping paddy rice planting area in northeastern asia with landsat 8 images, phenology-based algorithm and google earth engine,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Mapping paddy rice planting area in northeastern asia with landsat 8 images, phenology-based algorithm and google earth engine,

Reference 6

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Observation 5b61ff5b-8ab0-4165-8075-0d6ee52cfb4e · outbound

This paper cites Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application,

Reference 7

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Observation 6d6b53be-705b-4fe9-a842-644eccde06e6 · outbound

This paper cites Deep learning and process understanding for data-driven earth system science,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Deep learning and process understanding for data-driven earth system science,

Reference 8

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Observation a9eaa307-8911-4d5c-a619-bf1da04d0d30 · outbound

This paper cites Deep learning in remote sensing: A comprehensive review and list of resources,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Deep learning in remote sensing: A comprehensive review and list of resources,

Reference 9

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Observation c622b160-f880-4460-a790-680be07f4fdd · outbound

This paper cites Fully convolutional siamese networks for change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Fully convolutional siamese networks for change detection,

Reference 10

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Observation 2ce8267d-b540-408e-95f4-b8adcca288ae · outbound

This paper cites Multitask learning for large-scale semantic change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Multitask learning for large-scale semantic change detection,

Reference 11

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Observation 4c6175d4-4949-4e32-8a51-5ce4d1b74a64 · outbound

This paper cites Exchanging dual- encoder–decoder: A new strategy for change detection with semantic guidance and spatial localization,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Exchanging dual- encoder–decoder: A new strategy for change detection with semantic guidance and spatial localization,

Reference 12

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Observation 9abfaba7-e697-416b-a999-ca5273ccfa98 · outbound

This paper cites Fccdn: Feature constraint network for vhr image change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Fccdn: Feature constraint network for vhr image change detection,

Reference 13

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Observation 18120498-bb49-49c3-b26f-0882dfa72a1c · outbound

This paper cites Rs-mamba for large remote sensing image dense prediction,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Rs-mamba for large remote sensing image dense prediction,

Reference 14

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Observation 896310d3-d845-4869-85be-6f9fc0d78047 · outbound

This paper cites Remote sensing image change detection with transformers,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Remote sensing image change detection with transformers,

Reference 15

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Observation f4986b02-4e48-4c7b-a2a7-c3dc9eff784d · outbound

This paper cites Remote sensing in forestry: current challenges, considerations and directions,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Remote sensing in forestry: current challenges, considerations and directions,

Reference 16

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Observation f610f470-9b00-4396-922e-a49e3dbd8fc3 · outbound

This paper cites Learning building extraction in aerial scenes with convolu- tional networks,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Learning building extraction in aerial scenes with convolu- tional networks,

Reference 17

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Observation a837513e-c1ea-459e-a12c-b2952e686b13 · outbound

This paper cites Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017,

Reference 18

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Observation 499a1450-f5db-4fb5-a41a-6a0b5916f15b · outbound

This paper cites The moderate resolution imaging spectroradiometer (modis): land remote 17 sensing for global change research,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction The moderate resolution imaging spectroradiometer (modis): land remote 17 sensing for global change research,

Reference 19

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Observation dacebb6d-fe19-4ed7-a205-5b27141c23a8 · outbound

This paper cites Gmes sentinel-1 mission,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Gmes sentinel-1 mission,

Reference 20

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Observation f6c354d7-0c8e-416c-b6d1-8db06cb4e59a · outbound

This paper cites Sentinel-2: Esa’s optical high-resolution mission for gmes operational services,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Sentinel-2: Esa’s optical high-resolution mission for gmes operational services,

Reference 21

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Observation 74fb3b6c-25de-4c8c-b77f-71f56182420c · outbound

This paper cites The landsat 7 mission: Terrestrial research and applications for the 21st century,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction The landsat 7 mission: Terrestrial research and applications for the 21st century,

Reference 22

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Observation 7ce69069-7cba-4444-9c25-371078be4f8f · outbound

This paper cites Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Fully convolutional change detection framework with generative adversarial network for unsupervised, weakly supervised and regional supervised change detection,

Reference 23

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Observation b48a8007-dba7-4d8a-9053-9c98dd820769 · outbound

This paper cites An a-contrario approach for subpixel change detection in satellite imagery,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction An a-contrario approach for subpixel change detection in satellite imagery,

Reference 24

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Observation 3f4cd9c8-ab35-4227-8655-b63a4a966ea1 · outbound

This paper cites Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis,

Reference 25

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Observation abd56d1a-2668-4e50-a544-39e951896144 · outbound

This paper cites Vegediff: Latent dif- fusion model for geospatial vegetation forecasting,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Vegediff: Latent dif- fusion model for geospatial vegetation forecasting,

Reference 26

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Observation 22e53273-e35b-40e4-aa56-852e89015d0a · outbound

This paper cites Highly efficient and unsupervised framework for moving object detection in satellite videos,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Highly efficient and unsupervised framework for moving object detection in satellite videos,

Reference 27

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Observation 07cc0ce6-3a05-4a30-aa91-77a41fc4a47f · outbound

This paper cites Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (aviris),.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (aviris),

Reference 28

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Observation 3694dede-ef37-4e9e-acf8-f3d0241ce534 · outbound

This paper cites The enmap spaceborne imaging spectroscopy mission for earth observation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction The enmap spaceborne imaging spectroscopy mission for earth observation,

Reference 29

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Observation 08087a86-cb35-4993-8244-fa8db4b03813 · outbound

This paper cites Spectralgpt: Spectral remote sensing foundation model,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Spectralgpt: Spectral remote sensing foundation model,

Reference 30

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Observation 2315e48e-e10a-4bc4-b451-6457a28d3bc4 · outbound

This paper cites Selective adversarial adaptation-based cross-scene change detection framework in remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Selective adversarial adaptation-based cross-scene change detection framework in remote sensing images,

Reference 31

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Observation f99a8f5f-5356-4d4a-b178-c8104c4ae5b1 · outbound

This paper cites Neural plasticity-inspired foundation model for observing the earth crossing modalities,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Neural plasticity-inspired foundation model for observing the earth crossing modalities,

Reference 32

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Observation 1ee0afe9-c8c8-4c00-8474-aa92f96f76e4 · outbound

This paper cites Transforming Weather Data from Pixel to Latent Space.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Transforming Weather Data from Pixel to Latent Space

Reference 33

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Observation 6fe30487-7309-4f28-b7ab-96659bb9e812 · outbound

This paper cites Farseg++: Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Farseg++: Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery,

Reference 34

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Observation ea08a9b7-0d01-4a06-98fa-585985c19b93 · outbound

This paper cites Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery,

Reference 35

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Observation 82a22455-54d1-49c2-82e8-3e2fc1c81475 · outbound

This paper cites Channel exchanging networks for multimodal and multitask dense image prediction,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Channel exchanging networks for multimodal and multitask dense image prediction,

Reference 36

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Observation fde7f2de-95ea-43da-8ee5-5f608eac85f8 · outbound

This paper cites Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,

Reference 37

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Observation f6df9841-f6dc-455f-92d8-d367af33924b · outbound

This paper cites A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.948344Z

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Observation 23af86c9-ec14-40ad-a3ca-88da2345fa3a · outbound

This paper cites Coud: Continual urbanization detector for time series building change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Coud: Continual urbanization detector for time series building change detection,

Reference 39

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Observation 7d6ceb15-0e86-4078-93e3-4370a6a1ae61 · outbound

This paper cites LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Reference 40

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Observation 71916300-5a4c-42f3-ab36-f216a2f9d575 · outbound

This paper cites Dynamicearthnet: Daily multi- spectral satellite dataset for semantic change segmentation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Dynamicearthnet: Daily multi- spectral satellite dataset for semantic change segmentation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.922984Z

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Observation 5fb327a3-3da7-4250-b9bb-b66231217c95 · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Fully convolutional networks for semantic segmentation,

Reference 42

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Observation 6ce9f9ee-49bc-49a9-9c51-da40c1f1d11c · outbound

This paper cites Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,

Reference 43

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Observation 0620be94-3795-4e40-9ad1-b64118e898fa · outbound

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

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction U-net: Convolutional networks for biomedical image segmentation,

Reference 44

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Observation d12d7058-ae29-4239-b89c-c0009e191143 · outbound

This paper cites Pyramid scene parsing network,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Pyramid scene parsing network,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:51.963714Z

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Observation ac100cd5-1f4b-4ac0-a0ca-5787a967877d · outbound

This paper cites Deep high-resolution representa- tion learning for human pose estimation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Deep high-resolution representa- tion learning for human pose estimation,

Reference 46

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Observation 04c5419a-eeae-4968-b3b4-d9c8001611bc · outbound

This paper cites Toward automatic building footprint delineation from aerial images using cnn and regularization,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Toward automatic building footprint delineation from aerial images using cnn and regularization,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.874408Z

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Observation 20aa38d4-b5c4-44a8-a1d3-88fd8fbd1ceb · outbound

This paper cites Encoder- decoder with atrous separable convolution for semantic image seg- mentation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Encoder- decoder with atrous separable convolution for semantic image seg- mentation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.861724Z

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Observation 982a2c3b-d1ab-4fde-b12d-758b7df92add · outbound

This paper cites Resunet- a: A deep learning framework for semantic segmentation of remotely sensed data,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Resunet- a: A deep learning framework for semantic segmentation of remotely sensed data,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.850838Z

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Observation ef0ca065-2b5d-4909-9f2f-5415654f6f1e · outbound

This paper cites Map-net: Multiple attending path neural network for building footprint extraction from remote sensed imagery,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Map-net: Multiple attending path neural network for building footprint extraction from remote sensed imagery,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.838988Z

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Observation aeb2238e-d1a2-47df-a870-24395972f1ec · outbound

This paper cites D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.826928Z

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Observation c49210cc-8685-483c-9c53-49bf80d4563d · outbound

This paper cites Spatial information inference net: Road extraction using road-specific contextual information,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Spatial information inference net: Road extraction using road-specific contextual information,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.814414Z

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Observation 508b85db-f0c9-497c-9b81-028b27e89f62 · outbound

This paper cites Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Building change detection for remote sensing images using a dual-task constrained deep siamese convolutional network model,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.798087Z

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Observation 2ab556ba-c5d9-42b3-99d5-666345f79c3f · outbound

This paper cites Snunet-cd: A densely connected siamese network for change detection of vhr images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Snunet-cd: A densely connected siamese network for change detection of vhr images,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.785565Z

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Observation 2fa924db-1016-4e7c-bd7f-6f6cb44b1544 · outbound

This paper cites Adversarial instance augmentation for building change detection in remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Adversarial instance augmentation for building change detection in remote sensing images,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.772972Z

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Observation 8fd2a37f-8b8f-4016-8679-f1d1a79b97be · outbound

This paper cites Optical remote sensing image change detection based on attention mechanism and image difference,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Optical remote sensing image change detection based on attention mechanism and image difference,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.761967Z

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Observation 26c0d4af-e96d-4b9e-85be-7125a8826f0f · outbound

This paper cites Dasnet: Dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Dasnet: Dual attentive fully convolutional siamese networks for change detection in high-resolution satellite images,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.750754Z

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Observation c3b39010-4187-42ef-85c4-46b20eb4363c · outbound

This paper cites A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.738986Z

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Observation b040203c-6fe3-4895-97e3-eb26e6219c9f · outbound

This paper cites Hanet: A hierarchical attention network for change detection with bitemporal very-high- resolution remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Hanet: A hierarchical attention network for change detection with bitemporal very-high- resolution remote sensing images,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.727863Z

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Observation 4e672b59-a616-4d4e-9ed9-8fd4f13c243a · outbound

This paper cites Ultralightweight spatial–spectral feature cooperation network for change detection in remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Ultralightweight spatial–spectral feature cooperation network for change detection in remote sensing images,

Reference 60

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Observation 23f8d908-01d3-465b-946d-c0c26f1088e7 · outbound

This paper cites Spatiotemporal enhancement and interlevel fusion network for remote sensing images change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Spatiotemporal enhancement and interlevel fusion network for remote sensing images change detection,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.709125Z

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Observation c6546a36-00fe-4f67-a82f-e56a6f526338 · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.695069Z

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Observation 2fdd3339-293f-44a3-a1a1-01c898393f2d · outbound

This paper cites A transformer-based siamese net- work for change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A transformer-based siamese net- work for change detection,

Reference 63

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Observation 5caf8fde-c36b-42c4-af63-56ab95283c1a · outbound

This paper cites Bdtnet: Road extraction by bi-direction transformer from remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Bdtnet: Road extraction by bi-direction transformer from remote sensing images,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.665148Z

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Observation 29421880-f3ec-4f30-9b96-18f68d845ba1 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:52.048963Z

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Observation f01fb99a-18b8-4847-8bb9-6f9d3b12cca5 · outbound

This paper cites Cmtfnet: Cnn and multiscale transformer fusion network for remote-sensing image semantic segmentation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Cmtfnet: Cnn and multiscale transformer fusion network for remote-sensing image semantic segmentation,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.653473Z

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Observation 8fc38652-2d35-4229-ae7a-c4f01c0d5414 · outbound

This paper cites A cbam based multiscale transformer fusion approach for remote sensing image change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A cbam based multiscale transformer fusion approach for remote sensing image change detection,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.641205Z

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Observation 89d365f5-72c2-4027-a4f5-58f4b8c9f0d8 · outbound

This paper cites A cnn-transformer network with multiscale context aggregation for fine-grained cropland change detec- tion,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A cnn-transformer network with multiscale context aggregation for fine-grained cropland change detec- tion,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.628364Z

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Observation ac3ee16f-78b8-4cf4-830b-7bb99a81b02b · outbound

This paper cites An attention- based multiscale transformer network for remote sensing image change detection,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction An attention- based multiscale transformer network for remote sensing image change detection,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.615686Z

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Observation bdaf304e-3e60-4f69-9474-f6d941c211f1 · outbound

This paper cites Dual attention network for scene segmentation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Dual attention network for scene segmentation,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:52.069830Z

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Observation 0d873d3a-abe3-41ea-a6fb-b1cc005025c0 · outbound

This paper cites Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:52.073450Z

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Observation 8646934b-87f2-41c3-9424-a974044d7a87 · outbound

This paper cites Hybridizing cross-level contextual and attentive representations for remote sensing imagery semantic segmentation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Hybridizing cross-level contextual and attentive representations for remote sensing imagery semantic segmentation,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.597466Z

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Observation 1f4c4681-aa68-4536-b2e4-1bd1fe7209f4 · outbound

This paper cites Raanet: A residual aspp with attention framework for semantic segmentation of high-resolution remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Raanet: A residual aspp with attention framework for semantic segmentation of high-resolution remote sensing images,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.585564Z

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Observation bd8c3c1b-fb5c-41ff-a9b7-38b9bbebe34a · outbound

This paper cites Msafnet: Multiscale successive attention fusion network for water body extraction of remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Msafnet: Multiscale successive attention fusion network for water body extraction of remote sensing images,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.574080Z

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Observation 412a04d7-c401-4f44-865d-f344b483c40f · outbound

This paper cites A2-fpn for semantic segmentation of fine-resolution remotely sensed images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A2-fpn for semantic segmentation of fine-resolution remotely sensed images,

Reference 76

Resolution: verified exact; raw_fallback, observed 2026-08-15T20:39:52.312539Z

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Observation 1921ad1a-5624-4146-8e5d-fb30f034393e · outbound

This paper cites Semi-supervised semantic segmentation with directional context-aware consistency,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Semi-supervised semantic segmentation with directional context-aware consistency,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.562894Z

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Observation 33af61c9-7768-4cfe-bfe6-94ed548a996d · outbound

This paper cites Lanet: Local attention embedding to improve the semantic segmentation of remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Lanet: Local attention embedding to improve the semantic segmentation of remote sensing images,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.550565Z

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Observation 7fe51035-5916-49ea-bac3-b65225b1ccea · outbound

This paper cites Scattnet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Scattnet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.538773Z

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Observation 29ed33bd-e436-4b2b-820d-f5ab15318874 · outbound

This paper cites Integrating spatial details with long-range contexts for semantic segmentation of very high-resolution remote- sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Integrating spatial details with long-range contexts for semantic segmentation of very high-resolution remote- sensing images,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.526911Z

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Observation cc81e6ec-76ed-497e-8df8-42932f730f14 · outbound

This paper cites Vits for sits: Vision trans- formers for satellite image time series,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Vits for sits: Vision trans- formers for satellite image time series,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.515333Z

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Observation fcd07079-9789-4096-a9cd-fb411734511d · outbound

This paper cites A synergistical attention model for semantic segmentation of remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A synergistical attention model for semantic segmentation of remote sensing images,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.503016Z

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Observation 6d951b11-a12e-4729-a9f3-cfbb5e5df2fa · outbound

This paper cites A spectral–spatial context-boosted network for semantic segmentation of remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction A spectral–spatial context-boosted network for semantic segmentation of remote sensing images,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.490162Z

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Observation 6397b04a-73ae-4870-8b10-3fb9c9adf9fb · outbound

This paper cites Panoptic segmentation of satellite image time series with convolutional temporal attention networks,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Panoptic segmentation of satellite image time series with convolutional temporal attention networks,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.468733Z

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Observation 36299a0e-e125-45b3-aedb-234565605d44 · outbound

This paper cites Lightweight remote sensing change detection with progressive feature aggregation and supervised attention,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Lightweight remote sensing change detection with progressive feature aggregation and supervised attention,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:52.130863Z

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Observation 89e19809-8a45-4ed9-92d1-b1f71d93f37b · outbound

This paper cites Joint spatio-temporal modeling for semantic change detection in remote sensing images,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Joint spatio-temporal modeling for semantic change detection in remote sensing images,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.457662Z

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Observation fc99e8e8-506c-49ec-af8f-83060ed61940 · outbound

This paper cites Time-series land cover change detection using deep learning-based temporal semantic segmentation,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Time-series land cover change detection using deep learning-based temporal semantic segmentation,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:39:52.446167Z

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Observation 2983735b-5926-4c2b-9dec-bc702935edd3 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:52.141499Z

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Observation 627cb882-d00b-4691-ad76-9dc074513c5b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Adam: A Method for Stochastic Optimization

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:52.145306Z

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Observation 23d73021-ef5f-471e-9a11-18a5abdd19ed · outbound

This paper cites Multitask learning,.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Multitask learning,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:52.149233Z

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Observation 28d69d52-5ce2-44ca-b835-defaddfe49b0 · outbound

This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction An Overview of Multi-Task Learning in Deep Neural Networks

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T20:39:52.152749Z

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Observation 3a13c6e4-48e5-476b-b754-e51bf17674ea · outbound

This paper cites Available: https://www.mdpi.com/2072-4292/16/7/1214.

Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction Available: https://www.mdpi.com/2072-4292/16/7/1214

Reference 2024

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