Paper Citation Record · LEDGER

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting

As of 12 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2412.08536.

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

pith.paper-citation-record.v1
2412.08536 v1

Coverage vector

measured 50 of 50 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T22:41:00.027266Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T22:42:13.401476Z

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 0ab94a2a-178a-4a68-a620-1e8f4e1d3281 · outbound

This paper cites A simple zero-shot prompt weight- ing technique to improve prompt ensembling in text-image models.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting A simple zero-shot prompt weight- ing technique to improve prompt ensembling in text-image models

Reference 1

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Observation 3fd0840b-e78f-497d-9b80-8000d092188c · outbound

This paper cites A land use and land cover classi- fication system for use with remote sensor data, volume 964.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting A land use and land cover classi- fication system for use with remote sensor data, volume 964

Reference 2

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Observation ae9cc4b1-3eff-4c5e-9e8d-33a99eabfd77 · outbound

This paper cites Corine land cover and land cover change products.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Corine land cover and land cover change products

Reference 3

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Observation ac824e20-804d-4d12-9823-468ab9e8d41e · outbound

This paper cites Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images

Reference 4

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Observation b71d5a3a-149f-4e21-8778-97c08396db29 · outbound

This paper cites Lanczos filtering in one and two dimen- sions.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Lanczos filtering in one and two dimen- sions

Reference 5

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Observation f52f2ed5-b422-43d7-baac-897868b8384a · outbound

This paper cites Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Harmonised lucas in-situ land cover and use database for field surveys from 2006 to 2018 in the european union

Reference 6

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Observation 2fb40610-a7b7-471f-a61a-9bfddc37c449 · outbound

This paper cites Knowledge generation using satellite earth ob- servations to support sustainable development goals (sdg): A use case on land degradation.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Knowledge generation using satellite earth ob- servations to support sustainable development goals (sdg): A use case on land degradation

Reference 7

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Observation f282187f-578b-4e21-99fe-ca2e11dc7614 · outbound

This paper cites High-resolution global maps of 21st-century forest cover change.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting High-resolution global maps of 21st-century forest cover change

Reference 8

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Observation 311e9a01-b4f7-441e-a25e-d601a3a3544d · outbound

This paper cites Hargreaves and Gary R.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Hargreaves and Gary R

Reference 9

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Observation 3889583a-c32a-4412-acf8-70f1ed9f04fd · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Momentum contrast for unsupervised visual rep- resentation learning

Reference 10

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Observation 2b3c29cd-f580-4ed5-9c6f-3712bf667c4c · outbound

This paper cites Deep residual learning for image recognition.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Deep residual learning for image recognition

Reference 11

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Observation bb4e43fa-ded3-46ab-86df-fae2d831cabc · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 12

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Observation 8e3f1726-d362-4784-98cc-0823973c76d9 · outbound

This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 13

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Observation 0257b67b-b693-4606-9c6c-c797e4a7ac09 · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Imagenet classification with deep convolutional neural net- works

Reference 14

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Observation ea500d0d-8b59-4cc3-a4d2-b47e681afc02 · outbound

This paper cites Learning to detect unseen object classes by between- class attribute transfer.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Learning to detect unseen object classes by between- class attribute transfer

Reference 15

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Observation c0633773-2c0a-4c41-90c0-35d5cf629650 · outbound

This paper cites Zero-shot scene classification for high spatial reso- lution remote sensing images.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Zero-shot scene classification for high spatial reso- lution remote sensing images

Reference 16

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Observation 51e0718d-5cb3-4e2b-9cdc-27dbeb1d6ebc · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 17

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This paper cites Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Blip: Bootstrapping language-image pre-training for uni- fied vision-language understanding and generation

Reference 18

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Observation 5f3b8790-01b4-44cc-bc5b-5603d34f3d9b · outbound

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

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Rs-clip: Zero shot remote sensing scene classification via contrastive vision-language supervision

Reference 19

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Observation 49b51dd7-402a-4cce-a547-d7c3f1266496 · outbound

This paper cites Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero- shot remote sensing image scene classification.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero- shot remote sensing image scene classification

Reference 20

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Observation e86cde1a-1ca3-4aef-93ef-6329c44a1a17 · outbound

This paper cites Learning deep representations for ground-to-aerial geolocal- ization.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Learning deep representations for ground-to-aerial geolocal- ization

Reference 21

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Observation bec077b1-cd19-4ef5-95c9-64a454972f70 · outbound

This paper cites RemoteCLIP: A Vision Language Foundation Model for Remote Sensing.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting RemoteCLIP: A Vision Language Foundation Model for Remote Sensing

Reference 22

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This paper cites Decoupled Weight Decay Regularization.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Decoupled Weight Decay Regularization

Reference 23

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Observation 842ba62e-65ce-4816-9d0d-c1568443655a · outbound

This paper cites Jehanzeb Mirza, Leonid Karlinsky, Wei Lin, Ma- teusz Kozinski, Horst Possegger, Rogerio Feris, and Horst Bischof.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Jehanzeb Mirza, Leonid Karlinsky, Wei Lin, Ma- teusz Kozinski, Horst Possegger, Rogerio Feris, and Horst Bischof

Reference 24

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Observation c8f18d3f-8df4-401f-820b-5cc15199d5c5 · outbound

This paper cites ClipCap: CLIP Prefix for Image Captioning.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting ClipCap: CLIP Prefix for Image Captioning

Reference 25

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This paper cites Representation Learning with Contrastive Predictive Coding.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Representation Learning with Contrastive Predictive Coding

Reference 26

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This paper cites Training language models to follow instructions with human feedback.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Training language models to follow instructions with human feedback

Reference 27

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This paper cites What does a platypus look like? generating customized prompts for zero-shot image classification.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting What does a platypus look like? generating customized prompts for zero-shot image classification

Reference 28

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Observation e4e9ca8a-12d0-4bfe-a426-84f4362b851d · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Learning transferable visual models from natural language supervi- sion

Reference 29

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Observation 51225b03-bbd5-46e8-9c8e-85c55c94d436 · outbound

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

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Remote sensing technology for mapping and monitoring land-cover and land-use change

Reference 30

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Observation 16a9f0a0-0c89-42a8-8056-5aeb53b770d3 · outbound

This paper cites Waffling around for Performance: Visual Classification with Random Words and Broad Concepts.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Waffling around for Performance: Visual Classification with Random Words and Broad Concepts

Reference 31

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This paper cites Meta-learning for few-shot land cover classification.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Meta-learning for few-shot land cover classification

Reference 32

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This paper cites Humans are poor few-shot classifiers for sentinel-2 land cover.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Humans are poor few-shot classifiers for sentinel-2 land cover

Reference 33

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This paper cites Moein Shariatnia.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Moein Shariatnia

Reference 34

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This paper cites Beyond cross-view image retrieval: Highly accurate vehicle localization using satel- lite image.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Beyond cross-view image retrieval: Highly accurate vehicle localization using satel- lite image

Reference 35

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This paper cites Where am i looking at? joint location and orientation es- timation by cross-view matching.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Where am i looking at? joint location and orientation es- timation by cross-view matching

Reference 36

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Observation ec1060a1-b4d6-479c-b9a7-43e70bc63e0f · outbound

This paper cites mi- crosoft/planetarycomputer: October 2022, oct 2022.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting mi- crosoft/planetarycomputer: October 2022, oct 2022

Reference 37

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Observation 4886c903-6192-45b0-a27d-8b893e2b341d · outbound

This paper cites Fine-grained landuse characterization us- ing ground-based pictures: a deep learning solution based on globally available data.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Fine-grained landuse characterization us- ing ground-based pictures: a deep learning solution based on globally available data

Reference 38

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This paper cites Fine-grained object recognition and zero-shot learn- ing in remote sensing imagery.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Fine-grained object recognition and zero-shot learn- ing in remote sensing imagery

Reference 39

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Observation 4c971e80-8766-4020-94a0-08e941e93c26 · outbound

This paper cites BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting BigEarthNet Dataset with A New Class-Nomenclature for Remote Sensing Image Understanding

Reference 40

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Observation 3e6d1486-1ab5-4f08-8651-b84d4bd0ad23 · outbound

This paper cites The emergence of land change science for global environmen- tal change and sustainability.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting The emergence of land change science for global environmen- tal change and sustainability

Reference 41

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Observation 6934c5df-22ac-450a-94c1-4912b6b210a5 · outbound

This paper cites Skyscript: A large and seman- tically diverse vision-language dataset for remote sensing.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Skyscript: A large and seman- tically diverse vision-language dataset for remote sensing

Reference 42

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Observation 5e24e098-f225-48e3-b936-5f13df6a3010 · outbound

This paper cites Mixed land use measurement and mapping with street view images and spatial context-aware prompts via zero-shot mul- timodal learning.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Mixed land use measurement and mapping with street view images and spatial context-aware prompts via zero-shot mul- timodal learning

Reference 43

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This paper cites Concept-Guided Prompt Learning for Generalization in Vision-Language Models.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Concept-Guided Prompt Learning for Generalization in Vision-Language Models

Reference 44

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

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing

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SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Conditional prompt learning for vision-language mod- els

Reference 46

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SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Learning to prompt for vision-language models

Reference 47

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Observation ee284f3f-c9f4-4044-bc7b-2a0cc91a3a58 · outbound

This paper cites Transgeo: Trans- former is all you need for cross-view image geo-localization.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Transgeo: Trans- former is all you need for cross-view image geo-localization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:49:54.717856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 521fef1f-8194-4ede-92ee-647d3f4e0485 · outbound

This paper cites Vigor: Cross- view image geo-localization beyond one-to-one retrieval.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Vigor: Cross- view image geo-localization beyond one-to-one retrieval

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T17:49:54.469193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97fce7a5-872c-4bd3-8dd5-9262d2743cde · outbound

This paper cites Meta Prompt for Ground View Prompts.

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting Meta Prompt for Ground View Prompts

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:49:54.691126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 004cfdd8-4ec9-4013-b302-b25eef27a304 · inbound

GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations cites this paper.

GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-22T22:42:13.403907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.