SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation | Paper Citation Record

Observation ae83dfce-f7a3-4b5a-86d2-29b1e1344a27 · outbound

This paper cites Road extraction methods in high-resolution remote sensing images: A comprehensive review,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Road extraction methods in high-resolution remote sensing images: A comprehensive review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.412189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 03b8e224-ec86-4205-886c-561444dbc58a · outbound

This paper cites Glh-water: A large-scale dataset for global surface water detection in large-size very-high-resolution satellite imagery,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Glh-water: A large-scale dataset for global surface water detection in large-size very-high-resolution satellite imagery,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.397071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af8ce216-b6db-4625-84f1-71946b761a28 · outbound

This paper cites Brrnet: A fully convolutional neural network for automatic building extraction from high-resolution remote sensing images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Brrnet: A fully convolutional neural network for automatic building extraction from high-resolution remote sensing images,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.380013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5f92b25e-8d79-44ea-9bdc-28d4563ca152 · outbound

This paper cites A multimodal feature fusion network for building extraction with very high-resolution remote sensing image and lidar data,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation A multimodal feature fusion network for building extraction with very high-resolution remote sensing image and lidar data,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.364398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 61b4a2aa-73b0-48be-b834-3c12fdf855a7 · outbound

This paper cites Applications in remote sensing to forest ecology and management,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Applications in remote sensing to forest ecology and management,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.348825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 259faf81-0f2e-467c-bc9c-02d38368a77d · outbound

This paper cites Remote sensing technology for mapping and monitoring land-cover and land-use change,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Remote sensing technology for mapping and monitoring land-cover and land-use change,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.330320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2b29f3d2-81b4-4c02-a938-9385cb189d4e · outbound

This paper cites A comprehensive review of geospatial technology applications in earthquake preparedness, emergency management, and damage assessment,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation A comprehensive review of geospatial technology applications in earthquake preparedness, emergency management, and damage assessment,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.312125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 74b9dc82-0829-45f4-9dfe-ea35fd762e1c · outbound

This paper cites Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.295759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 806d7fd1-2ced-48ef-8555-c1ab497d22ef · outbound

This paper cites A review of remote sensing for environmental monitoring in china,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation A review of remote sensing for environmental monitoring in china,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.279402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8e6ddd0c-1fee-4aae-bb03-c133bfbbcc7e · outbound

This paper cites Global open data remote sensing satellite missions for land monitoring and conservation: A review,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Global open data remote sensing satellite missions for land monitoring and conservation: A review,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.263796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 56ec3640-4c83-4663-9057-e0e51e981f64 · outbound

This paper cites Identifying urban building function by integrating remote sensing imagery and poi data,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Identifying urban building function by integrating remote sensing imagery and poi data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.236355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 04e20486-9f58-4433-996a-6c2e9378e48e · outbound

This paper cites Detecting ecological spatial- temporal changes by remote sensing ecological index with local adapt- ability,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Detecting ecological spatial- temporal changes by remote sensing ecological index with local adapt- ability,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.213095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 510edbc4-b440-400d-9e65-bc462993be28 · outbound

This paper cites Iterdanet: Iterative intra-domain adaptation for semantic segmentation of remote sensing images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Iterdanet: Iterative intra-domain adaptation for semantic segmentation of remote sensing images,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.195534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a041f61-18bc-4420-8bed-3ce58171ac9a · outbound

This paper cites Bifdanet: Unsupervised bidirectional domain adaptation for semantic segmentation of remote sensing images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Bifdanet: Unsupervised bidirectional domain adaptation for semantic segmentation of remote sensing images,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.179468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3a00b81f-b25f-4e59-8d4a-46a816e1a3c4 · outbound

This paper cites Db-blendmask: Decomposed attention and balanced blendmask for instance segmenta- tion of high-resolution remote sensing images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Db-blendmask: Decomposed attention and balanced blendmask for instance segmenta- tion of high-resolution remote sensing images,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.159431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a6f319ab-9eae-495b-9995-7e4a4f73792c · outbound

This paper cites Encoder- decoder with atrous separable convolution for semantic image segmen- tation,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.116737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6a9af7-ebeb-48c5-bbcf-fbeab159ba69 · outbound

This paper cites Panoptic feature pyramid networks,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Panoptic feature pyramid networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.127870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 96c8caab-b264-4da6-a0bf-a8233f0d1a39 · outbound

This paper cites Pyramid scene parsing network,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Pyramid scene parsing network,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.126591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87b9fee1-d497-402d-a270-8e26f94104cb · outbound

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

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.131420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08ca33d6-7813-4db8-85b1-16fa1d80f1a0 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmenta- tion,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Unet++: A nested u-net architecture for medical image segmenta- tion,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.136217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 642fc992-8afe-43cf-83bb-046709ad4dce · outbound

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

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Segformer: Simple and efficient design for semantic segmentation with transformers,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.141190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57bfd031-3050-4b1e-8d16-7104a64d19e5 · outbound

This paper cites Pvt v2: Improved baselines with pyramid vision transformer,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Pvt v2: Improved baselines with pyramid vision transformer,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.146264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d90d88c9-98ed-44a3-9448-16a58eb6bab3 · outbound

This paper cites Segmenter: Trans- former for semantic segmentation,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Segmenter: Trans- former for semantic segmentation,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.151396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6573258-c849-4e56-bb55-140a78d1400a · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.156582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3129ad25-fb56-40ef-a0a9-9a4a589efd3f · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.161677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67a8fcfa-7547-4a84-9fcb-b47ce4271493 · outbound

This paper cites Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.041147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b2dcb4d5-e747-452f-ad69-86abb0b8f0fa · outbound

This paper cites Looking outside the window: Wide-context transformer for the semantic segmentation of high-resolution remote sensing im- ages,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Looking outside the window: Wide-context transformer for the semantic segmentation of high-resolution remote sensing im- ages,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.026514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f1a94dbe-6ca9-4b21-b074-e2497ddf7f19 · outbound

This paper cites Icnet for real-time semantic segmentation on high-resolution images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Icnet for real-time semantic segmentation on high-resolution images,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:07.010689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c88f4ed2-4ab3-4586-9f04-fff7f58923a2 · outbound

This paper cites From contexts to locality: Ultra-high resolution image segmentation via locality-aware contextual correlation,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation From contexts to locality: Ultra-high resolution image segmentation via locality-aware contextual correlation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.994818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0b8f120b-33e1-4e8e-9d5a-330eaa60c74f · outbound

This paper cites Isdnet: Integrating shallow and deep net- works for efficient ultra-high resolution segmentation,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Isdnet: Integrating shallow and deep net- works for efficient ultra-high resolution segmentation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.979284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 257d2291-2143-4950-924d-41d905d606c3 · outbound

This paper cites EHSNet: End-to-End Holistic Learning Network for Large-Size Remote Sensing Image Semantic Segmentation.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation EHSNet: End-to-End Holistic Learning Network for Large-Size Remote Sensing Image Semantic Segmentation

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:46:06.511916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 042d1585-c985-45e3-ab23-d5796d9adef8 · outbound

This paper cites Ultra-high resolution segmen- tation with ultra-rich context: A novel benchmark,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Ultra-high resolution segmen- tation with ultra-rich context: A novel benchmark,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.963278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d3649bfd-05c3-4d01-a42a-2de6d754c851 · outbound

This paper cites Patch proposal network for fast semantic segmentation of high-resolution images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Patch proposal network for fast semantic segmentation of high-resolution images,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.947395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 744ac285-ba69-4d4c-85e8-b13ed8402ed8 · outbound

This paper cites Seeing beyond the patch: Scale- adaptive semantic segmentation of high-resolution remote sensing im- agery based on reinforcement learning,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Seeing beyond the patch: Scale- adaptive semantic segmentation of high-resolution remote sensing im- agery based on reinforcement learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.930730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c6f22a92-f7f6-48c8-992b-6b50e26dba79 · outbound

This paper cites Enabling country-scale land cover mapping with meter-resolution satellite imagery,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Enabling country-scale land cover mapping with meter-resolution satellite imagery,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.914988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 46e92e2e-ebfd-4693-be00-838469df34df · outbound

This paper cites Deep-learning-based semantic segmentation of remote sensing images: A survey,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Deep-learning-based semantic segmentation of remote sensing images: A survey,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.897829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1b386548-c83a-4f32-92e3-30b4aad74d9e · outbound

This paper cites An improved algorithm for neural network classification of imbalanced training sets,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation An improved algorithm for neural network classification of imbalanced training sets,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.879743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3dd23cdd-ed32-4bd9-90cd-e0e498be8a62 · outbound

This paper cites Under- standing imbalanced semantic segmentation through neural collapse,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Under- standing imbalanced semantic segmentation through neural collapse,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.862286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5eacbbc9-81b9-4762-b0a2-67817e20411c · outbound

This paper cites Region Rebalance for Long-Tailed Semantic Segmentation.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Region Rebalance for Long-Tailed Semantic Segmentation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.235270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8064fdb9-ce5b-4274-a468-9d043757eb1c · outbound

This paper cites Borderline-smote: a new over- sampling method in imbalanced data sets learning,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Borderline-smote: a new over- sampling method in imbalanced data sets learning,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.240361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4aaf35b-82e7-4a27-bb09-8f3df054e275 · outbound

This paper cites C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.835823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5ab2e848-28b1-404a-ad32-e35c3fe973f4 · outbound

This paper cites Exploring the limits of weakly supervised pretraining,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Exploring the limits of weakly supervised pretraining,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.819406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0a826c75-a842-4c9a-939f-7c44b5d9d5c0 · outbound

This paper cites Relay backpropagation for effective learning of deep convolutional neural networks,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Relay backpropagation for effective learning of deep convolutional neural networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.803226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af09c9a6-a5c0-4dfb-a3a6-711731ad8018 · outbound

This paper cites Segment anything,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Segment anything,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.263611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d5e0a20-bc61-438d-abeb-18b6e0f28b01 · outbound

This paper cites Segment anything in high quality,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Segment anything in high quality,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.776600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 89abb5cc-942d-4834-9d01-f0890f4b411d · outbound

This paper cites Rethinking seman- tic segmentation: A prototype view,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Rethinking seman- tic segmentation: A prototype view,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.759569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 545ca039-0909-42fe-bb60-d031b60f23d1 · outbound

This paper cites Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.277135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b231f0e-58a0-424a-b191-2f1cce69706f · outbound

This paper cites Learning transferable visual models from natural language supervision,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Learning transferable visual models from natural language supervision,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.282961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caba944d-e0cb-4b22-92d3-56ef1a19c80b · outbound

This paper cites RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.287549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d954abd-e501-48a1-93c5-e85bbf416f64 · outbound

This paper cites Remoteclip: A vision language foundation model for remote sensing,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Remoteclip: A vision language foundation model for remote sensing,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.292411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88d55340-1dac-45c2-a256-97acb6730f7f · outbound

This paper cites Rs-clip: Zero shot remote sensing scene classification via contrastive vision-language supervision,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Rs-clip: Zero shot remote sensing scene classification via contrastive vision-language supervision,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.715427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1ac0dd21-c36d-4d59-8f0f-b3a25533428e · outbound

This paper cites Semantic-SAM: Segment and Recognize Anything at Any Granularity.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Semantic-SAM: Segment and Recognize Anything at Any Granularity

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.302469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e620f9e3-5e26-43b5-a3f1-803c26f3fc2d · outbound

This paper cites Personalize Segment Anything Model with One Shot.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Personalize Segment Anything Model with One Shot

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.307353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c197725-e897-4636-b1ec-be312d0ccde0 · outbound

This paper cites Adapting segment anything model for change detection in vhr remote sensing images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Adapting segment anything model for change detection in vhr remote sensing images,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.312128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96524970-b028-4cfd-9b2b-3cb4cc5e62fb · outbound

This paper cites Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM).

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.316792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4651fb7c-59b5-49c3-93e5-53b7de031539 · outbound

This paper cites Evaluating the efficacy of segment anything model for delineating agriculture and urban green spaces in multiresolution aerial and spaceborne remote sensing images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Evaluating the efficacy of segment anything model for delineating agriculture and urban green spaces in multiresolution aerial and spaceborne remote sensing images,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.688125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f2c1628a-7c20-4dde-86ed-2ebf662b710c · outbound

This paper cites Mesam: Multiscale enhanced segment anything model for optical remote sensing images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Mesam: Multiscale enhanced segment anything model for optical remote sensing images,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.670727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2d533487-7bc9-4c86-9475-862340514f0a · outbound

This paper cites The segment anything model (sam) for remote sensing applications: From zero to one shot,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation The segment anything model (sam) for remote sensing applications: From zero to one shot,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.654493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 521321d9-38d3-4930-b78e-5bcfc3500ee9 · outbound

This paper cites Equalization loss for long-tailed object recognition,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Equalization loss for long-tailed object recognition,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.638227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ef119618-5d6d-47c2-9f90-4cebf81b5d29 · outbound

This paper cites Land-cover classification with high-resolution remote sensing images using transferable deep models,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Land-cover classification with high-resolution remote sensing images using transferable deep models,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.342227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b0b21c8-12c0-4481-9652-dce1ee609aa2 · outbound

This paper cites Mcanet: A joint semantic segmentation framework of optical and sar images for land use classification,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Mcanet: A joint semantic segmentation framework of optical and sar images for land use classification,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.611700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c2da8b9e-7500-482b-b937-2e9e9282906d · outbound

This paper cites Deepglobe 2018: A challenge to parse the earth through satellite images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Deepglobe 2018: A challenge to parse the earth through satellite images,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.596524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8fca50b5-c245-4559-bd5a-147fe355e075 · outbound

This paper cites MMSegmentation: Openmmlab semantic seg- mentation toolbox and benchmark,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation MMSegmentation: Openmmlab semantic seg- mentation toolbox and benchmark,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.580325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 227742b4-b7ed-4462-9b65-f1e944bdf034 · outbound

This paper cites Unified perceptual parsing for scene understanding,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Unified perceptual parsing for scene understanding,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.361244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ac41576-7acc-4475-b264-673adf9f827d · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Fully convolutional networks for semantic segmentation,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T05:46:06.365773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fe2f8f4-541e-498d-8421-51f89b872eba · outbound

This paper cites Boundary- enhanced dual-stream network for semantic segmentation of high- resolution remote sensing images,.

SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation Boundary- enhanced dual-stream network for semantic segmentation of high- resolution remote sensing images,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:46:06.544452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers