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Paper Citation Record · LEDGER

RemoteSAM: Towards Segment Anything for Earth Observation

As of 9 August 2026, this Paper Citation Record lists 88 of 88 outbound references and 4 inbound Pith citation observations for arXiv:2505.18022.

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

pith.paper-citation-record.v1
2505.18022 v3

Coverage vector

measured 88 of 88 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:39:40.475109Z

measured 92 of 92 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T16:10:02.906054Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T05:40:59.536689Z

Reference resolution

88 of 88 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 56621087-da4a-4cd5-abb7-ae4a44d2c91b · outbound

This paper cites Fmars: Annotating remote sensing images for disas- ter management using foundation models.

RemoteSAM: Towards Segment Anything for Earth Observation Fmars: Annotating remote sensing images for disas- ter management using foundation models

Reference 1

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Observation d387e6b1-6410-4932-91e4-b2032527d8da · outbound

This paper cites Foundation models defining a new era in vision: a survey and outlook.

RemoteSAM: Towards Segment Anything for Earth Observation Foundation models defining a new era in vision: a survey and outlook

Reference 2

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Observation b32e0cd8-42cc-43a3-b657-f680576b0a5f · outbound

This paper cites Grounding everything: Emerging localiza- tion properties in vision-language transformers.

RemoteSAM: Towards Segment Anything for Earth Observation Grounding everything: Emerging localiza- tion properties in vision-language transformers

Reference 3

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Observation 2f6122d9-4b14-4f32-89e5-0eba73ba3cad · outbound

This paper cites Learned embed- ding fields for multi-source, multi-temporal earth observa- tion imagery.

RemoteSAM: Towards Segment Anything for Earth Observation Learned embed- ding fields for multi-source, multi-temporal earth observa- tion imagery

Reference 4

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Observation 653c4e72-11e0-4e9b-bea7-b64e492ea7ca · outbound

This paper cites A billion-scale foundation model for remote sensing images.

RemoteSAM: Towards Segment Anything for Earth Observation A billion-scale foundation model for remote sensing images

Reference 5

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Observation 8ac2a643-88e3-4729-ae2c-c4b3ffee19b2 · outbound

This paper cites What makes for good image captions?, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation What makes for good image captions?, 2024

Reference 6

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Observation b5415d56-d47f-4837-89f6-02530175036a · outbound

This paper cites Subobject-level Image Tokenization.

RemoteSAM: Towards Segment Anything for Earth Observation Subobject-level Image Tokenization

Reference 7

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Observation b63959e0-28e3-45cd-bf68-21825bdc6786 · outbound

This paper cites Remote sensing of diverse urban environments: From the single city to multiple cities.RSE, 305:114108,.

RemoteSAM: Towards Segment Anything for Earth Observation Remote sensing of diverse urban environments: From the single city to multiple cities.RSE, 305:114108,

Reference 8

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Observation 9c7226f3-4880-44db-9cf5-9b04b4690689 · outbound

This paper cites Fleet, and Ge- offrey Hinton.

RemoteSAM: Towards Segment Anything for Earth Observation Fleet, and Ge- offrey Hinton

Reference 9

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Observation a583835b-5ab3-4eeb-be12-58c43106177d · outbound

This paper cites Fleet, and Geoffrey Hinton.

RemoteSAM: Towards Segment Anything for Earth Observation Fleet, and Geoffrey Hinton

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 00ec97cf-7763-4ef2-8a9d-d889b836f89a · outbound

This paper cites Mask grounding for referring image seg- mentation.

RemoteSAM: Towards Segment Anything for Earth Observation Mask grounding for referring image seg- mentation

Reference 11

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 6384034f-97b0-43c7-b1ad-69208ba8e5ad · outbound

This paper cites Cross-aware early fusion with stage-divided vision and language trans- former encoders for referring image segmentation.IEEE TMM, 26:5823–5833, 2023.

RemoteSAM: Towards Segment Anything for Earth Observation Cross-aware early fusion with stage-divided vision and language trans- former encoders for referring image segmentation.IEEE TMM, 26:5823–5833, 2023

Reference 12

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Observation 3be89101-7464-4bd7-9405-fa6657251919 · outbound

This paper cites Functional map of the world.

RemoteSAM: Towards Segment Anything for Earth Observation Functional map of the world

Reference 13

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Observation a6d577a4-ec82-4172-b382-65d10e356960 · outbound

This paper cites Satmae: Pre-training transformers for tem- poral and multi-spectral satellite imagery.NeurIPS, 35:197– 211, 2022.

RemoteSAM: Towards Segment Anything for Earth Observation Satmae: Pre-training transformers for tem- poral and multi-spectral satellite imagery.NeurIPS, 35:197– 211, 2022

Reference 14

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Observation f465854b-e2cf-452c-bdb4-e401f253de23 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

RemoteSAM: Towards Segment Anything for Earth Observation Imagenet: A large-scale hierarchical image database

Reference 15

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Observation 4b8cd8fd-af2b-4bf9-bacd-a1e606d6ebb3 · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding.

RemoteSAM: Towards Segment Anything for Earth Observation Bert: Pre-training of deep bidirectional trans- formers for language understanding

Reference 16

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Observation 963a1dc1-5d15-4648-b204-458244225179 · outbound

This paper cites Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation.

RemoteSAM: Towards Segment Anything for Earth Observation Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation

Reference 17

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Observation c0c9e451-f8e4-4f1f-a04d-b8afaa5edb4b · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

RemoteSAM: Towards Segment Anything for Earth Observation MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 18

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Observation 8aec6e65-6a32-4022-8493-ea2f0f593b71 · outbound

This paper cites Beyond one-to-one: Re- thinking the referring image segmentation.

RemoteSAM: Towards Segment Anything for Earth Observation Beyond one-to-one: Re- thinking the referring image segmentation

Reference 19

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 446fe35a-3a61-4aca-bf24-f19451e8017d · outbound

This paper cites Bi-directional relationship inferring network for referring image segmentation.

RemoteSAM: Towards Segment Anything for Earth Observation Bi-directional relationship inferring network for referring image segmentation

Reference 20

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Observation fe9ad41a-cb4f-42ac-a3b3-4a163a436398 · outbound

This paper cites Look before you leap: Learning landmark features for one-stage visual grounding.

RemoteSAM: Towards Segment Anything for Earth Observation Look before you leap: Learning landmark features for one-stage visual grounding

Reference 21

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Observation ec353354-982a-4ab0-bc48-cc19075fa63e · outbound

This paper cites Referring im- age segmentation via cross-modal progressive comprehen- sion.

RemoteSAM: Towards Segment Anything for Earth Observation Referring im- age segmentation via cross-modal progressive comprehen- sion

Reference 22

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verified fuzzy
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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9467fdb3-f55b-422e-b534-199ddcbcfb7b · outbound

This paper cites Linguistic structure guided context modeling for referring image segmentation.

RemoteSAM: Towards Segment Anything for Earth Observation Linguistic structure guided context modeling for referring image segmentation

Reference 23

Resolution
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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d0cc04e8-b4a8-4d7f-afae-d3c168556ccd · outbound

This paper cites A survey of methods for addressing the chal- lenges of referring image segmentation.Neurocomputing, 583:127599, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation A survey of methods for addressing the chal- lenges of referring image segmentation.Neurocomputing, 583:127599, 2024

Reference 24

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Observation d6aa7afe-fd89-40b5-bb2d-2daca472777d · outbound

This paper cites Geochat: Grounded large vision-language model for remote sensing.

RemoteSAM: Towards Segment Anything for Earth Observation Geochat: Grounded large vision-language model for remote sensing

Reference 25

Resolution
verified fuzzy
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Observation 10869e94-90d8-4a11-8b7d-03e8b0f8e230 · outbound

This paper cites Clearclip: Decom- posing clip representations for dense vision-language infer- ence.

RemoteSAM: Towards Segment Anything for Earth Observation Clearclip: Decom- posing clip representations for dense vision-language infer- ence

Reference 26

Resolution
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Source-reported events for the cited work

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Observation 9a21a7a3-88ed-4571-820a-b58f251ced82 · outbound

This paper cites Exploring fine-grained image-text alignment for referring remote sensing image segmentation.

RemoteSAM: Towards Segment Anything for Earth Observation Exploring fine-grained image-text alignment for referring remote sensing image segmentation

Reference 27

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2ac767b4-1905-456e-a9da-1eb3f3fb33b5 · outbound

This paper cites Object detection in optical remote sensing images: A survey and a new benchmark.ISPRS PRS, 159:296–307,.

RemoteSAM: Towards Segment Anything for Earth Observation Object detection in optical remote sensing images: A survey and a new benchmark.ISPRS PRS, 159:296–307,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 042a8e88-9378-48c7-b35e-00a45800fee8 · outbound

This paper cites SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images.

RemoteSAM: Towards Segment Anything for Earth Observation SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images

Reference 29

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Source-reported events for the cited work

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Observation 93a85133-f238-4bd7-bef9-28af22f2ce8f · outbound

This paper cites Referring transformer: A one- step approach to multi-task visual grounding.NeurIPS, 34: 19652–19664, 2021.

RemoteSAM: Towards Segment Anything for Earth Observation Referring transformer: A one- step approach to multi-task visual grounding.NeurIPS, 34: 19652–19664, 2021

Reference 30

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9745a97a-3861-47c1-94c2-fe67e0f48fcc · outbound

This paper cites Co- training transformer for remote sensing image classification, segmentation and detection.IEEE TGRS, 62:1–18, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Co- training transformer for remote sensing image classification, segmentation and detection.IEEE TGRS, 62:1–18, 2024

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation ba3893a0-96df-47ed-b4ca-32cf6aeaeeda · outbound

This paper cites Toward open vocabulary aerial object detection with clip-activated student-teacher learning.

RemoteSAM: Towards Segment Anything for Earth Observation Toward open vocabulary aerial object detection with clip-activated student-teacher learning

Reference 32

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verified fuzzy
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Source-reported events for the cited work

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Observation 9ad75552-0fba-48e7-b7e2-be53b8fc297a · outbound

This paper cites Masked angle-aware autoen- coder for remote sensing images.

RemoteSAM: Towards Segment Anything for Earth Observation Masked angle-aware autoen- coder for remote sensing images

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5e134ef0-2cfa-41e2-8403-292b2b339daa · outbound

This paper cites A real-time cross-modality correlation fil- tering method for referring expression comprehension.

RemoteSAM: Towards Segment Anything for Earth Observation A real-time cross-modality correlation fil- tering method for referring expression comprehension

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation b0c182b3-f14a-4ef5-b0c9-1a44a0b8e6c3 · outbound

This paper cites Progressive language-customized visual feature learn- ing for one-stage visual grounding.IEEE TIP, 31:4266– 4277, 2022.

RemoteSAM: Towards Segment Anything for Earth Observation Progressive language-customized visual feature learn- ing for one-stage visual grounding.IEEE TIP, 31:4266– 4277, 2022

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:51.235352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:33.786872Z digest=sha256:545de6707a1ef4d8084429407e2cb376e024b71e0c49157b6b04efbce7fc6691

Observation 82994afb-c722-417a-977a-8edcf4696a5c · outbound

This paper cites SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models.

RemoteSAM: Towards Segment Anything for Earth Observation SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:33.912755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:33.912755Z digest=sha256:ab63e97b9dadcaa3105bf4e0ee6e29beae6258561d9afcc291e7b2102abbc218

Observation bc2339a3-d04c-4a18-9db3-fc316a5eead3 · outbound

This paper cites Gres: Gen- eralized referring expression segmentation.

RemoteSAM: Towards Segment Anything for Earth Observation Gres: Gen- eralized referring expression segmentation

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:51.067598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:34.081091Z digest=sha256:912258f22118d2f7c936ac071369986ac2cc768c0371f81bc14210ed4e7c5e67

Observation b0a8f9f3-95b6-4230-b73e-4781ff71edf7 · outbound

This paper cites Re- moteclip: A vision language foundation model for remote sensing.IEEE TGRS, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Re- moteclip: A vision language foundation model for remote sensing.IEEE TGRS, 2024

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:50.935469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:34.237764Z digest=sha256:11e34fc36ffd9ced5a54a93f4dd71432324a0c8028de88e6465eb34ee1bdb5f7

Observation 9e2c9fd8-d223-4797-bb47-775852b79dba · outbound

This paper cites Boost UAV-based Ojbect Detection via Scale-Invariant Feature Disentanglement and Adversarial Learning.

RemoteSAM: Towards Segment Anything for Earth Observation Boost UAV-based Ojbect Detection via Scale-Invariant Feature Disentanglement and Adversarial Learning

Reference 39

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:39:41.344972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:34.362395Z digest=sha256:1153673025d67621d60f7b88ce30eb01103770e75ec2d2bba48073fc67f13faf

Observation dd758db8-f691-4f95-91ba-4bc2a2ca889b · outbound

This paper cites Few-shot adaptation of multi-modal foundation models: A survey.Ar- tificial Intelligence Review, 57(10):268, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Few-shot adaptation of multi-modal foundation models: A survey.Ar- tificial Intelligence Review, 57(10):268, 2024

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:50.759016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:34.498334Z digest=sha256:e3ea35cd0f6b0409d5240885b8090b59bf396a872ec515055f54399f346a26e8

Observation d436a106-48a0-4981-80a4-a1b1f67c11ba · outbound

This paper cites Visual instruction tuning, 2023.

RemoteSAM: Towards Segment Anything for Earth Observation Visual instruction tuning, 2023

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:50.564001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:34.646206Z digest=sha256:176b46ff3b03b792cbca944ff168367a8b797fa311d51cb2ae8b8eb7d404ec39

Observation b86cce4d-ddb2-48de-917f-54fe33192821 · outbound

This paper cites Cross-modal progressive comprehension for referring segmentation.IEEE TPAMI, 44(9):4761–4775,.

RemoteSAM: Towards Segment Anything for Earth Observation Cross-modal progressive comprehension for referring segmentation.IEEE TPAMI, 44(9):4761–4775,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:50.368660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:34.796298Z digest=sha256:39f8d5023dd0c174eb647e55bc4aa1b593ee23aa443c14a2edf6b46525bc89ad

Observation 6ccc335c-bcf7-46fb-9336-e62dee340bb5 · outbound

This paper cites Rotated multi-scale interaction network for referring remote sensing image seg- mentation.

RemoteSAM: Towards Segment Anything for Earth Observation Rotated multi-scale interaction network for referring remote sensing image seg- mentation

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:50.153209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:34.897555Z digest=sha256:a3edb4062f68721e9b46c7d978682ecb799b9828625571a61a8db71b2a599c8c

Observation 60b9a2e6-9eed-434a-89c4-62740cf8043b · outbound

This paper cites Caris: Context-aware re- ferring image segmentation.

RemoteSAM: Towards Segment Anything for Earth Observation Caris: Context-aware re- ferring image segmentation

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:49.932482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:35.026593Z digest=sha256:6e784bf3690b43515718d3d52812c39887e1e79dab419e6669c6dccaf044652f

Observation 56876629-8eb6-4453-aefb-3bfbfaa6aadd · outbound

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

RemoteSAM: Towards Segment Anything for Earth Observation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:35.161575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:35.161575Z digest=sha256:1a3f548c4f87eede90e4c837ee71767aaf459abd13a344f02bd79ca3d619b05d

Observation fef5218e-1104-4bbf-9d48-121ff639db5a · outbound

This paper cites On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid.IEEE JSTARS, 14:4205–4230, 2021.

RemoteSAM: Towards Segment Anything for Earth Observation On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid.IEEE JSTARS, 14:4205–4230, 2021

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:49.736189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:35.321814Z digest=sha256:a41e36d35834fb71633d8a77a71b887a32a6d7e4bbe840e0875dc92e45461b4a

Observation 7ac4e6d7-a652-426d-9ed3-33c6623ca4a6 · outbound

This paper cites Change- aware sampling and contrastive learning for satellite images.

RemoteSAM: Towards Segment Anything for Earth Observation Change- aware sampling and contrastive learning for satellite images

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:49.537560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:35.425160Z digest=sha256:6e95cf85cb08b0e8adc28e7901e9d1fe990bc63a6002ac416c0af58a62a46767

Observation 5ac3dc67-fb24-4ea6-930e-36d6756f0db2 · outbound

This paper cites Seasonal contrast: Un- supervised pre-training from uncurated remote sensing data.

RemoteSAM: Towards Segment Anything for Earth Observation Seasonal contrast: Un- supervised pre-training from uncurated remote sensing data

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:49.317453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:35.512171Z digest=sha256:75412468a2f79af068216380e7b6ce0f129dd22511d3f24c5659a6bc6d9e3e85

Observation 7b693eef-5c7e-4c01-a2cb-041e29f1aba1 · outbound

This paper cites Prompting directsam for semantic contour extraction in remote sensing images.

RemoteSAM: Towards Segment Anything for Earth Observation Prompting directsam for semantic contour extraction in remote sensing images

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:49.045808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:35.643998Z digest=sha256:51502b3c8d415e9446073a5d865b029d8f0bc2626dd0a4e33ef7939ba39d9c89

Observation c007f98a-2884-44fe-9ad8-1a926b241d97 · outbound

This paper cites Lhrs-bot: Empowering remote sensing with vgi-enhanced large multimodal language model.

RemoteSAM: Towards Segment Anything for Earth Observation Lhrs-bot: Empowering remote sensing with vgi-enhanced large multimodal language model

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:48.833651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:35.760305Z digest=sha256:811e5355c417a194886dec84fd150c07afa8f3f8a10841d9464e2017aaf41724

Observation 2f0bcf59-d8d0-4ef2-8468-e185eb2a5b57 · outbound

This paper cites Rethinking transformers pre-training for multi- spectral satellite imagery.

RemoteSAM: Towards Segment Anything for Earth Observation Rethinking transformers pre-training for multi- spectral satellite imagery

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:48.589953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:35.917178Z digest=sha256:250364d920ffc0cea38d903d54c3fae3bd588a35141c0aa88451fbb101d16ce9

Observation 02cb14a4-ef02-42d5-b441-fcfdd2b2e937 · outbound

This paper cites Sentinel-2 data for land cover/use mapping: A review.Re- mote sensing, 12(14):2291, 2020.

RemoteSAM: Towards Segment Anything for Earth Observation Sentinel-2 data for land cover/use mapping: A review.Re- mote sensing, 12(14):2291, 2020

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:48.395403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:36.052039Z digest=sha256:6b7008d824959b96ed0fead70d5fa5e1e4de906a7a55b88fa890d1586b958939

Observation c4c419d6-e54a-42b9-9887-7009d2f4bcb3 · outbound

This paper cites Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning.

RemoteSAM: Towards Segment Anything for Earth Observation Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:48.174410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:36.195604Z digest=sha256:dbcc9ce338e2e8c7a32addc437c46a4554121c96001974781dfbec395f698c06

Observation 0fa4dd9c-654a-4135-be4e-e1588572fd5a · outbound

This paper cites Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning.

RemoteSAM: Towards Segment Anything for Earth Observation Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:47.948967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:36.301817Z digest=sha256:77012313568974841b56c9357ee0ca42a6eea20a33d43ecd7a5a4ff69f9ab3cc

Observation c6fd16e5-a0cb-4294-b187-d0281edda843 · outbound

This paper cites Grounded sam: Assembling open-world models for diverse visual tasks,.

RemoteSAM: Towards Segment Anything for Earth Observation Grounded sam: Assembling open-world models for diverse visual tasks,

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:36.414990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:36.414990Z digest=sha256:f7aa50175ae083e87568cec7d2f530d3ef92ac9957eb0a98746c68929c5a9fbb

Observation 3dd93602-bcfd-46d6-9e50-2c7fda595a82 · outbound

This paper cites SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models.

RemoteSAM: Towards Segment Anything for Earth Observation SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models

Reference 56

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:39:41.112935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:36.512720Z digest=sha256:295e1eecf8330b4bd2efec54e7e93cb47924a9f656fecd84f56118f9354431ff

Observation 2254ba76-af29-42aa-b8ac-e16930608cea · outbound

This paper cites Cus- tomized sam 2 for referring remote sensing image segmenta- tion, 2025.

RemoteSAM: Towards Segment Anything for Earth Observation Cus- tomized sam 2 for referring remote sensing image segmenta- tion, 2025

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:47.741616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:36.625561Z digest=sha256:aa9e78a1af183f288bd0891ace26875e2d0eb855ee3404a95fab78f6efc25154

Observation 4df9a94c-6eaf-4f07-9bf3-ca2aac50595d · outbound

This paper cites Zero-shot ground- ing of objects from natural language queries.

RemoteSAM: Towards Segment Anything for Earth Observation Zero-shot ground- ing of objects from natural language queries

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:47.515624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:36.777793Z digest=sha256:8ef66c123fc450849979e7b2efb6aa8f1d3fb73d16781cfd65f8f53528fe20fe

Observation 43ddfced-ed38-48d8-9a48-0220065e981f · outbound

This paper cites Ringmo: A remote sensing foundation model with masked image modeling.IEEE TGRS, 61:1–22, 2022.

RemoteSAM: Towards Segment Anything for Earth Observation Ringmo: A remote sensing foundation model with masked image modeling.IEEE TGRS, 61:1–22, 2022

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:47.315453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:36.919444Z digest=sha256:f5365e5e3eac6522e5571c2ad6ec945a1c8d9c9b98e949fa21b94f80523859a4

Observation 12923a39-d500-4033-9bc7-36bf39ad6b51 · outbound

This paper cites Ringmo: A remote sensing foundation model with masked image modeling.IEEE TGRS, 61:1–22, 2023.

RemoteSAM: Towards Segment Anything for Earth Observation Ringmo: A remote sensing foundation model with masked image modeling.IEEE TGRS, 61:1–22, 2023

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:47.106287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:37.051513Z digest=sha256:c4004c3a9d042a6f1d888cb44469080df9cb63f141570b1cf648487440468f82

Observation a3d3caf8-9233-4b7a-a5e2-33ad04b8bcb2 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

RemoteSAM: Towards Segment Anything for Earth Observation SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:37.156610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:37.156610Z digest=sha256:e6b10d22b48e16d0aa64157d6fdfe0bd2266c97af2a633074f3ca83df23ce6b5

Observation 0ff422df-e8dd-43ce-a40d-517f04eb4ce9 · outbound

This paper cites Arti- ficial intelligence to advance earth observation: A review of models, recent trends, and pathways forward.IEEE GRSM,.

RemoteSAM: Towards Segment Anything for Earth Observation Arti- ficial intelligence to advance earth observation: A review of models, recent trends, and pathways forward.IEEE GRSM,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:46.912875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:37.287546Z digest=sha256:800b0a4d23f03c3ae208f61e91a2ab237668674eacfdc7327009f133e4dfcdd9

Observation cff54c5b-02d3-4a47-bd91-9a6f13e7adb8 · outbound

This paper cites Remote sensing for agriculture in the era of industry 5.0–a survey.IEEE JSTARS, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Remote sensing for agriculture in the era of industry 5.0–a survey.IEEE JSTARS, 2024

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:46.746943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:37.409874Z digest=sha256:c124c50a711ca80e7d93e7c133ef9d36c48106abab6948a830773fbe1117c087

Observation 90fea180-fa20-4b76-9665-6a98cde531bd · outbound

This paper cites Advancing plain vision transformer toward remote sensing foundation model.IEEE TGRS, 61:1–15, 2022.

RemoteSAM: Towards Segment Anything for Earth Observation Advancing plain vision transformer toward remote sensing foundation model.IEEE TGRS, 61:1–15, 2022

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:46.557340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:37.504869Z digest=sha256:b3781d39c2911ebbd1db8eee686e6f324288d14604fa136b9efa313769a3c9db

Observation 0c504ada-7315-4fd7-b89c-dd1592d8e53a · outbound

This paper cites Samrs: Scaling-up re- mote sensing segmentation dataset with segment anything model.NeurIPS, 36:8815–8827, 2023.

RemoteSAM: Towards Segment Anything for Earth Observation Samrs: Scaling-up re- mote sensing segmentation dataset with segment anything model.NeurIPS, 36:8815–8827, 2023

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:46.368972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:37.633871Z digest=sha256:1a8171dc01c4829725ea65610b91ba026f1445ef39911335763d591934e95cd0

Observation dea6da46-5bb6-46f9-afb3-3b67f67e0f14 · outbound

This paper cites SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference.

RemoteSAM: Towards Segment Anything for Earth Observation SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:37.747635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:37.747635Z digest=sha256:e0172d37b60830e77d33afc926f4d08fb7ebfbc33fb339977974f79c318567c4

Observation 60173593-fd8b-4dc6-b13b-60a95f6fffa3 · outbound

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

RemoteSAM: Towards Segment Anything for Earth Observation LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:37.888754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:37.888754Z digest=sha256:b71f93b1a288e0e49d3799ac58c704394623620b22eeb818f18a3876a4980a84

Observation 76cfe5a3-dfbc-4d75-b05b-b53d72b03259 · outbound

This paper cites Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework.

RemoteSAM: Towards Segment Anything for Earth Observation Ofa: Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:46.143111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:38.012826Z digest=sha256:63330cbe07519eae5c7bee211bdf7d787718ef0a0e2dbd46322d64f61fe9aff9

Observation 5a7ef037-b21b-4338-8982-c190b465e014 · outbound

This paper cites Ssl4eo- s12: A large-scale multimodal, multitemporal dataset for self-supervised learning in earth observation [software and data sets].IEEE GRSM, 11(3):98–106, 2023.

RemoteSAM: Towards Segment Anything for Earth Observation Ssl4eo- s12: A large-scale multimodal, multitemporal dataset for self-supervised learning in earth observation [software and data sets].IEEE GRSM, 11(3):98–106, 2023

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:45.907993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:38.141017Z digest=sha256:1f9c1a015364acac6e331f1b2c6c2e1c92d40be3e8e25cc806d25edb48882306

Observation 9ebbefdf-e553-481d-8624-7f40435f4f4b · outbound

This paper cites isaid: A large- scale dataset for instance segmentation in aerial images.

RemoteSAM: Towards Segment Anything for Earth Observation isaid: A large- scale dataset for instance segmentation in aerial images

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:45.535345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:38.244249Z digest=sha256:75f848dc74ddd2e9f0f5a505116346597ecdfb5635adba1840ed7f3219c692aa

Observation bfad8b16-be98-4c7f-9313-6dbded5447e6 · outbound

This paper cites Towards robust referring image seg- mentation.IEEE TIP, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Towards robust referring image seg- mentation.IEEE TIP, 2024

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:45.271957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:38.357917Z digest=sha256:e4ffd72e64d4449f892938c223bd17010bfe6909a47a0587d51a7b86a5367d27

Observation 572c3566-4fd4-4e61-8955-5609e4adcbb5 · outbound

This paper cites Dota: A large-scale dataset for object detection in aerial images.

RemoteSAM: Towards Segment Anything for Earth Observation Dota: A large-scale dataset for object detection in aerial images

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:45.023171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:38.468425Z digest=sha256:bc16ba61d57609693c4199fe39a44ac2b9e4c7d27b406e5a6412f6befdcff211

Observation 43fc9d22-1de8-4232-b617-138f7c708697 · outbound

This paper cites Qwen2.5 tech- nical report, 2025.

RemoteSAM: Towards Segment Anything for Earth Observation Qwen2.5 tech- nical report, 2025

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:44.788251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:38.610518Z digest=sha256:573be787a2199fb2dd9f354450b9a5884b927823028ec8cfbde80986cffcfdf3

Observation 2deb9a5d-b670-4980-a10b-0090246e0bce · outbound

This paper cites Graph adversarial self-supervised learning.NeurIPS, 34:14887– 14899, 2021.

RemoteSAM: Towards Segment Anything for Earth Observation Graph adversarial self-supervised learning.NeurIPS, 34:14887– 14899, 2021

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:44.564871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:38.728513Z digest=sha256:e1912ce1520dda1a328cbb0e20bfc7557ff0b4e3f0cc0b89d82bba9a830a7133

Observation 43a36087-e987-4f55-94f3-f78bb3d80d86 · outbound

This paper cites Lavt: Language-aware vi- sion transformer for referring image segmentation.

RemoteSAM: Towards Segment Anything for Earth Observation Lavt: Language-aware vi- sion transformer for referring image segmentation

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:44.308732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:38.850708Z digest=sha256:3d2692d7a28aa2807401467167d3408e9359b689f9a2e9322d552b75ea8b2918

Observation 4a25db20-d296-4c92-8bee-adbc8477fc08 · outbound

This paper cites Falcon: A remote sensing vision-language foun- dation model.arXiv preprint arXiv:2503.11070, 2025.

RemoteSAM: Towards Segment Anything for Earth Observation Falcon: A remote sensing vision-language foun- dation model.arXiv preprint arXiv:2503.11070, 2025

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:38.954809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:38.954809Z digest=sha256:dda6695fa14be70f2e3711e1abb8e9307653444a934534bdcf489b0a0a6e20dd

Observation 6d05411a-4b4f-42dc-9360-681908031318 · outbound

This paper cites Domain-invariant progressive knowledge distillation for uav-based object detection.IEEE GRSL, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Domain-invariant progressive knowledge distillation for uav-based object detection.IEEE GRSL, 2024

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:44.067257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:39.041139Z digest=sha256:55173b015e913e4d19d2bed56fa84577e5a8d91aa153b9939c48e4962545e9d0

Observation 3fdac325-7c4d-4b44-814b-87b71e3fbfa8 · outbound

This paper cites Shifting more attention to visual backbone: Query-modulated refinement networks for end-to-end visual grounding.

RemoteSAM: Towards Segment Anything for Earth Observation Shifting more attention to visual backbone: Query-modulated refinement networks for end-to-end visual grounding

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:43.782855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:39.160822Z digest=sha256:8b3d20afe819a173e05016b4473865bc29e60ae21e901998452b59ee635090bf

Observation e95f51b8-4a03-4e69-a10f-9ec3e8cde4ca · outbound

This paper cites Rrsis: Referring remote sensing image seg- mentation.IEEE TGRS, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Rrsis: Referring remote sensing image seg- mentation.IEEE TGRS, 2024

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:43.615222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:39.311195Z digest=sha256:3b3073c794228d10f86e931d26f733d1c1b3417bf1481a3ae3347500d644b28e

Observation 271a2b3c-2f0b-471e-a9e1-40cad64059dc · outbound

This paper cites Rsvg: Exploring data and models for visual grounding on remote sensing data.

RemoteSAM: Towards Segment Anything for Earth Observation Rsvg: Exploring data and models for visual grounding on remote sensing data

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:43.324994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:39.406069Z digest=sha256:2b1b4fa4993383755703087944aa67122177063f9e5cfe302afb4c474c144806

Observation 93ca58a4-276b-4805-9e43-51b3085c25de · outbound

This paper cites Skyeyegpt: Uni- fying remote sensing vision-language tasks via instruction tuning with large language model.ISPRS PRS, 221:64–77,.

RemoteSAM: Towards Segment Anything for Earth Observation Skyeyegpt: Uni- fying remote sensing vision-language tasks via instruction tuning with large language model.ISPRS PRS, 221:64–77,

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:43.064342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:39.529811Z digest=sha256:0766f782dde1bd83308396920e0653d9023f70271aa1a0698094661d69dfed31

Observation 88c08a40-fa77-4a30-a0ce-c2b3cd74b64d · outbound

This paper cites Vision-language models for vision tasks: A survey.IEEE TPAMI, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Vision-language models for vision tasks: A survey.IEEE TPAMI, 2024

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:42.727020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:39.654591Z digest=sha256:ebb9358f62753f7edffa0274ae35eaed8b76968881cf82b0f4f8e9df08b3adbb

Observation 5953b534-6cdf-4d61-946c-7b792fc88348 · outbound

This paper cites Earthgpt: A universal multi-modal large lan- guage model for multi-sensor image comprehension in re- mote sensing domain.IEEE TGRS, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Earthgpt: A universal multi-modal large lan- guage model for multi-sensor image comprehension in re- mote sensing domain.IEEE TGRS, 2024

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:42.440103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:39.806655Z digest=sha256:2f721a5d2036ef6c29687c4545faa396685a6e1fc60d9ed3aaf27ac21ff136d9

Observation 5f0e3176-fd5d-45a9-9ce6-3819f37429d2 · outbound

This paper cites Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection.IEEE TGRS, 57(8):5535–5548, 2019.

RemoteSAM: Towards Segment Anything for Earth Observation Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection.IEEE TGRS, 57(8):5535–5548, 2019

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:42.235876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:39.939145Z digest=sha256:2e4855d58acbd39a42080863dff5a2afbd02c84fbc495120563ac19262788607

Observation 9462e545-ad44-4fb9-b6cb-1aba1753b399 · outbound

This paper cites Rs5m and georsclip: A large scale vision-language dataset and a large vision-language model for remote sensing.IEEE TGRS, 2024.

RemoteSAM: Towards Segment Anything for Earth Observation Rs5m and georsclip: A large scale vision-language dataset and a large vision-language model for remote sensing.IEEE TGRS, 2024

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:41.996454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:40.082678Z digest=sha256:2246da492bff6c66a97fddbf64ac2cbbebf097ad6ae2119b802a035ad271e583

Observation 2c9795ac-95da-4136-b478-7a876e051b7e · outbound

This paper cites Extract free dense labels from clip.

RemoteSAM: Towards Segment Anything for Earth Observation Extract free dense labels from clip

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:39:41.745510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:39:40.189656Z digest=sha256:e08bd1725270df4bec46231e65b9aba6cd1818a18f42c34cb633510848433dd0

Observation 8eae8324-237a-4692-9e0d-64f8792191cd · outbound

This paper cites GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding.

RemoteSAM: Towards Segment Anything for Earth Observation GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding

Reference 87

Resolution
unresolved
no resolver link, observed 2026-08-07T14:39:40.308034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:40.308034Z digest=sha256:f1f68bfa25751480eb070d844c3ad815f7a411db09149c3a9cc18f4bc2ed8914

Observation 8d281888-74c1-4085-84a4-cc02f18bfebb · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

RemoteSAM: Towards Segment Anything for Earth Observation MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 88

Resolution
malformed identifier
no resolver link, observed 2026-08-07T14:39:40.475109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:39:40.475109Z digest=sha256:21e76f804e68a9fa1dcf321add5d57f896528ce94aae60476d52ab18a2ce4c74

Pith citing papers

Observation 3339ef41-4e78-4ed1-bde3-98b0c530d010 · inbound

RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs cites this paper.

RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs RemoteSAM: Towards Segment Anything for Earth Observation

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Resolution
verified exact
arxiv_id, observed 2026-05-11T05:40:59.544650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T18:00:20.216268Z digest=sha256:b499545367180db86cdc604d799f701f1817e5b8022c546dbd5f7aff64328182

Observation b7f110aa-927e-4889-a8b0-27930134c18f · inbound

PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation cites this paper.

PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation RemoteSAM: Towards Segment Anything for Earth Observation

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-10T09:23:37.152572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T09:20:54.635375Z digest=sha256:97714a7a333e8d07166897717c34e50f33eac4522b1456ace927e2de5a886ad5

Observation 10aeaf36-070a-48c7-ad60-4ca41dcf38e1 · inbound

PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation cites this paper.

PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation RemoteSAM: Towards Segment Anything for Earth Observation

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-02T16:10:02.906054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T16:10:02.906054Z digest=sha256:94358df257d0b2763ae1f8842b04beabc75898c2f0e173865827fe0826521615

Observation b447adff-5dbd-407e-ab0d-6c23c642e757 · inbound

RemoteZero: Geospatial Reasoning with Zero Human Annotations cites this paper.

RemoteZero: Geospatial Reasoning with Zero Human Annotations RemoteSAM: Towards Segment Anything for Earth Observation

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:30:40.304593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T18:24:46.030608Z digest=sha256:f58fab1a5b14788b0f947778d6fe373a3a1ae842bbdd31f136c9c4aae32e8c33