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Paper Citation Record · LEDGER

RemoteSAM: Towards Segment Anything for Earth Observation

As of 17 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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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

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

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

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

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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
verified fuzzy
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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
verified fuzzy
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Source-reported events for the cited work

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

Resolution
verified fuzzy
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Source-reported events for the cited work

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

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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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:33.786872Z digest=sha256:1a558a353b08ec45c9cd004ee9405668b616c5535dc3e29649f5f7c65306817d

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=sha256:2a25b29d93001d0993e241cb8ae9463e7591430253b5e63dfe1c769ee33bd810

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:34.081091Z digest=sha256:7921e75eed7970885339085bf7d6d31ae695a8faa4994127013df96a56441d4a

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:34.237764Z digest=sha256:95d3c3adda750407778ac3c9e876803c20cacfdb47f9c1420da0e4f327a13fd6

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:34.362395Z digest=sha256:c313422f9ca14d671869b1d2b828012bfcdeb46a9121074017da76049001b284

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:34.498334Z digest=sha256:5682a0c7372746b7a6d933f332c47aba43849276ec59da5251c02ae55f076817

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:34.646206Z digest=sha256:b0ac34ad4ebd12d2c2a11cd982dc9cddbeb95b21100878526c42563fb1640aa6

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:34.796298Z digest=sha256:6924497eac9f47ee71315d9c355a0a51a92e21d0636feb0e37e0489145f486b9

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:34.897555Z digest=sha256:b551dbad7a9ad4fcc2ca2bf6883ce5687d953dc61c5db5dd3ce809e9dfc15278

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:35.026593Z digest=sha256:93c1bfe1872d989fbbeb6bf0cdd8371f0671fcf701cc0ab4bc173e7689f86629

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=sha256:16c01778d0b63afa99f9745cc8f4c1a72fe464ede0ab475006658c9bbe660c26

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:35.321814Z digest=sha256:90bb7ce8129285c9363dd20551d2f64eeef28f0d9b1e2520b2ce88c1e0a72e5c

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:35.425160Z digest=sha256:5a2fee0b3b0bdc89c210715b027489987b52e8e64ae0172b0aaf4cd414647edf

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:35.512171Z digest=sha256:df82e6e7a92176d66640320094adbbeb623ce63b8856c87d11f8d5ea32e407a0

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:35.643998Z digest=sha256:695fd708071fbd1acc8208e04b84d82fa1bfacd4260ba2c12ea589848d32cd4b

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:35.760305Z digest=sha256:e8df1dff6c30c5580e890dd699febf72c236861b7280f35a4ee2ce55e54d2f3a

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:35.917178Z digest=sha256:65bac07ee11f256288976049c25d6a335c928fc516e0623dcdd4c92fe2764847

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:36.052039Z digest=sha256:57a1eb2f3b4041996457b75557c6d75bde62f773590d7db3ee5853fd718bac8e

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:36.195604Z digest=sha256:f966058e8dc37cf868f35c3dc7053e094492656293b28cf1cdf86895ce0bdf4b

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:36.301817Z digest=sha256:03dbcf6348a4405e2e82af38954683498d4cb816b5b41cf3fac634fcbede50d8

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=sha256:551d01345a43540db72e24f4f910a8330d4fa0323315c431dfb30600e82574d5

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:36.512720Z digest=sha256:55f82562d605ef782f4825da62829d3af5491110bbcb40e824ade5cc63f0ef3d

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:36.625561Z digest=sha256:389971d598f929fcc1be5787c8dd6a295624c9409660bcfd162bf28a1297a109

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:36.777793Z digest=sha256:b55a86aa664fdc0eebf251591e7997e3b02266a42219498977e222541bebe9de

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:36.919444Z digest=sha256:8fcd7eb90931d6619c026e3fb32289e849a68345fee85a6dae69a589fbdc56e9

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:37.051513Z digest=sha256:021dfdb551893cd0e552c3f187a56253136aa1cf48cb867442cab88c81e9767b

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=sha256:51ad48290d22c02235284a6e856b417f6384ad6ace2efbd178034f1b5567e085

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:37.287546Z digest=sha256:c43c913fba3524774d9de9d09d906cdb11276446b9b156b8a3f29a6313b0712d

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:37.409874Z digest=sha256:1686f0341e93571a551806148d6defabd4f86b20fb5615fe3756a3db93717e74

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:37.504869Z digest=sha256:1ed08cee01726ca3baaa34674131e7eb8d43e392dbb8aee595568b0ae4f812b6

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:37.633871Z digest=sha256:b8111cc9ebe960c4f97c9218d80b769cbab68943fb88c3a95bdee6472ce248aa

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=sha256:28b9335462b9bcca2c687db0d84d9ef59fdeb76055eee7c61cddc7cdfd763777

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=sha256:5ab0df27946a8d7272a6148ebe556c8d7767b4488031dfe65ca566ea16e4a20c

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:38.012826Z digest=sha256:3342efe8662059040db053cb3a0c1c6f0bfe7e5452996d8df7c2708b6f8ac552

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:38.141017Z digest=sha256:f6ac7cdc61478925ae9d7efbaafc92fd1ef8d82ea755a1160cb5b5c7611aa3f8

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:38.244249Z digest=sha256:e26fa82119f9cefb36ad4c747c6f15b25602c26ed277f41f79d57abc03350a86

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:38.357917Z digest=sha256:070e75458bfcafe464aaa90ce608c58e84b792828010a6c4a8a9a5eeef5657b0

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:38.468425Z digest=sha256:43478be24e721891608efb2fa47afd3ea9489f52f4cc30dd1697b833e37af943

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:38.610518Z digest=sha256:8dbcd4d234885e50f854a8929825bbf0b817b03741879afac24f0b225e4fdc4b

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:38.728513Z digest=sha256:ccf22fc29839679b91d278eede076fbbd3c411632e00ea53ae1c8bbceea682f0

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:38.850708Z digest=sha256:faa5945b308928945212ac5d5d0bc2696d7667f002822d1c89daae9813774f0c

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=sha256:4e3eeb2e33cdbf898428b2c3f8e58f70db61d5c439c4719b3182d7cce3dddd63

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:39.041139Z digest=sha256:240d5868dbac051415e2eebbe4cc6e1ac69a4f9104ba20adebf1e95a82767bc9

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:39.160822Z digest=sha256:8cd57d51dadf6d68b1d30e610cbca3bbbc444a9354eb28c6d8aab109380b3540

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:39.311195Z digest=sha256:3a5bcd2fe1b79b127fe3b4542550ce619cd839371d4f6a58a53e155eeeb314be

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:39.406069Z digest=sha256:2485e7373267f6a2cdbecc2810e39a0ea5d9ce3d0ace0a9c26fb37bdcaa17de6

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:39.529811Z digest=sha256:a55679c5f4d7e6118a3804bca136643a2e597a9f5b4ba64833b749dec3a95307

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:39.654591Z digest=sha256:001e199204328f5a25bd1534063a0e9ecd8dedc4c9b74692fd318beb5ded9547

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:39.806655Z digest=sha256:a614725d347d80c8c89ef04019e352dc0ec5d301a7caaae7ba05a4fead378430

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:39.939145Z digest=sha256:32cd5517a0138e9a735d5489a224ae46b890b33e933c7bddf05228ac5b8874a9

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:40.082678Z digest=sha256:8ccd57bbee265f3154a9ff6834d1fedbad1135a71d1c87035e282d166e9ea154

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T14:39:40.189656Z digest=sha256:4363516dcd53a7bfe8adeea547dd3250a74d3f2b99aea8e33ee3ec093bc03daa

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:a349834dfc3ba48329caa9277fbe3b31033552da99aecc2075923eb13c5784c2

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:afecc8683812c117fec796ed7ed88130f909fab265865ba2768afb6c9a202b57

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

Reference 69

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T18:00:20.216268Z digest=sha256:481affe7d3a5a3e04eb0e6672fd64b4b644e66c5f2ccaea8a113d7d96992a967

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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T09:20:54.635375Z digest=sha256:5eade5a0a74e567c8cb670e60c2488e6371302b3a9eafb225ec8772f1cfb93b9

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:a36c77b4d9ff384a94f7f1f6b37699bb4c69f361797a4009ad4562690fb0b242

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

RemoteZero: Geospatial Reasoning with Zero Labels cites this paper.

RemoteZero: Geospatial Reasoning with Zero Labels 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 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-08T18:24:46.030608Z digest=sha256:5520eb044d71b5cc1c9c793eb8758944fd94d42fbf9a0d44ca1283834fd1e00c