Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T17:09:42.289146Z
Paper Citation Record · LEDGER
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery
As of 21 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 0 inbound Pith citation observations for arXiv:2608.10801.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
80 of 80 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7471dc6a-0a4e-4d43-9b40-17b483b4176f · outbound
This paper cites A deep learning based framework for solar panel segmen- tation and fault classification enhanced with explainable ai.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery A deep learning based framework for solar panel segmen- tation and fault classification enhanced with explainable ai
Reference 1
- Resolution
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Observation 04778a95-6b6a-471c-8f22-8510bcf80506 · outbound
This paper cites Flamingo: a visual language model for few-shot learning.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Flamingo: a visual language model for few-shot learning
Reference 2
- Resolution
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Observation e9cf17f0-e59b-4ee8-bb9e-70102b2bfc99 · outbound
This paper cites Energy transition at local level: Analyzing the role of peer effects and socio-economic factors on uk solar photovoltaic deployment.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Energy transition at local level: Analyzing the role of peer effects and socio-economic factors on uk solar photovoltaic deployment
Reference 3
- Resolution
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Observation f3394357-774a-4390-85b5-2a375ab64662 · outbound
This paper cites Economic aspects of urban green- ness along a dryland rainfall gradient: A time-series analysis.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Economic aspects of urban green- ness along a dryland rainfall gradient: A time-series analysis
Reference 4
- Resolution
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Observation 5a3e8da0-e627-484b-9e58-a74239bfdb6b · outbound
This paper cites On the effectiveness of textual prompting with lightweight fine-tuning for sam3 remote sensing segmentation.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery On the effectiveness of textual prompting with lightweight fine-tuning for sam3 remote sensing segmentation
Reference 5
- Resolution
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Observation abfdd8f5-7cef-4f1c-b4a9-dbf6c77d85c2 · outbound
This paper cites Performance of human annotators in object detection and segmentation of remotely sensed data.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Performance of human annotators in object detection and segmentation of remotely sensed data
Reference 6
- Resolution
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Observation 22b94e22-d531-4907-b1c8-1658c17d28bb · outbound
This paper cites Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
Reference 7
- Resolution
- verified exact
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Source-reported events for the cited work
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Observation fe47c82c-d2e3-4599-9e64-a60125ac7a04 · outbound
This paper cites SAM 3: Segment Anything with Concepts.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery SAM 3: Segment Anything with Concepts
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation 85bbf95b-ec2b-4529-af2d-dc1ca0cd068d · outbound
This paper cites Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model
Reference 9
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 5395acbd-42c5-40de-baef-549db138c84d · outbound
This paper cites Multiscale adapter based on sam for remote sensing semantic segmentation.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Multiscale adapter based on sam for remote sensing semantic segmentation
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 5e2bb040-a808-4c9b-b22a-0ff0b979266e · outbound
This paper cites Dgtrsd and dgtrsclip: A dual-granularity remote sensing image– text dataset and vision–language foundation model for alignment.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Dgtrsd and dgtrsclip: A dual-granularity remote sensing image– text dataset and vision–language foundation model for alignment
Reference 11
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation a858aaa2-092e-4a83-952c-2653f4537221 · outbound
This paper cites Edge-enhanced sam for extracting photovoltaic power plants from remote sensing imagery.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Edge-enhanced sam for extracting photovoltaic power plants from remote sensing imagery
Reference 12
- Resolution
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Observation d298c535-159a-4684-893e-51c6c842c6e5 · outbound
This paper cites Masked-attention mask transformer for universal image segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Masked-attention mask transformer for universal image segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 077827d4-c96b-4c6a-bd27-0ada9f7ad12d · outbound
This paper cites Unleashing the potential of sam for medical adaptation via hierarchical decoding, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Unleashing the potential of sam for medical adaptation via hierarchical decoding, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp
Reference 14
- Resolution
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Observation f78718cd-2cb3-491e-b09a-3613896ce043 · outbound
This paper cites Heterogeneity in the adoption of photovoltaic systems in flanders.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Heterogeneity in the adoption of photovoltaic systems in flanders
Reference 15
- Resolution
- verified fuzzy
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Observation 79d63107-60c4-4e63-a1d0-9c36661cff36 · outbound
This paper cites Op- timizing zero-shot text-based segmentation of remote sensing imagery using sam and grounding dino.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Op- timizing zero-shot text-based segmentation of remote sensing imagery using sam and grounding dino
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation 8eae8cdc-ecd9-4417-98a1-67d41b949448 · outbound
This paper cites an unresolved cited work.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Unresolved cited work
Reference 17
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Observation ccb500ab-ab21-40f3-b4c0-eba88ea1e3e0 · outbound
This paper cites Generalized deep learning model for photovoltaic module segmentation from satellite and aerial imagery.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Generalized deep learning model for photovoltaic module segmentation from satellite and aerial imagery
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation b987a54d-521f-4708-b11b-b6927919d948 · outbound
This paper cites Global Off-Grid Solar Market Report: Sales and Impact Data (H2 2022).
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Global Off-Grid Solar Market Report: Sales and Impact Data (H2 2022)
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7a54d32c-7af7-485d-9ce3-67601f833ad3 · outbound
This paper cites Spatial patterns of solar photovoltaic system adoption: The influence of neighbors and the built environment.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Spatial patterns of solar photovoltaic system adoption: The influence of neighbors and the built environment
Reference 20
- Resolution
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Observation 7000cc4b-8dce-4368-ad29-18af57e1a80a · outbound
This paper cites Transpv: Refining photovoltaic panel detection accuracy through a vision transformer- based deep learning model.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Transpv: Refining photovoltaic panel detection accuracy through a vision transformer- based deep learning model
Reference 21
- Resolution
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Observation 96184146-5327-4641-91ae-fdb7ac682cc3 · outbound
This paper cites SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagery.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagery
Reference 22
- Resolution
- verified exact
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Source-reported events for the cited work
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Observation 30cbb04a-6131-46f8-a608-d99831dd498b · outbound
This paper cites an unresolved cited work.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Unresolved cited work
Reference 23
- Resolution
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Observation ef043b9e-df0d-4902-9c4d-5aa3993cca20 · outbound
This paper cites When 17 remote sensing meets foundation model: A survey and beyond.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery When 17 remote sensing meets foundation model: A survey and beyond
Reference 24
- Resolution
- verified fuzzy
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Observation f5cd6abf-f670-489d-8ebe-aae8e63cfbdc · outbound
This paper cites Population without electricity access, 2010–.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Population without electricity access, 2010–
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 04056698-ee45-41a8-926a-99f6babe9f4a · outbound
This paper cites Tracking SDG 7: The Energy Progress Report.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Tracking SDG 7: The Energy Progress Report
Reference 26
- Resolution
- verified fuzzy
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Observation 4335e938-6d52-449b-92cb-3d1a4d2b457b · outbound
This paper cites Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 762e670b-6c87-44c8-bbf4-bd1cd60fef71 · outbound
This paper cites Geoseg: Training-free reasoning- driven segmentation in remote sensing imagery.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Geoseg: Training-free reasoning- driven segmentation in remote sensing imagery
Reference 28
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 092f4364-b213-40fd-8b33-d879a4c6bcc0 · outbound
This paper cites Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation 4749fbe3-fa2f-4d5e-80d9-e478886156be · outbound
This paper cites A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata
Reference 30
- Resolution
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Source-reported events for the cited work
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Observation 0492a698-468c-4e85-bd7f-be7a00e71527 · outbound
This paper cites Geoai for detection of solar photovoltaic installations in the netherlands.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Geoai for detection of solar photovoltaic installations in the netherlands
Reference 31
- Resolution
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Source-reported events for the cited work
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Observation 880c3739-d4c3-4954-8792-7c8bc5dd9dca · outbound
This paper cites Segment anything, in: Proceedings of the IEEE/CVF international conference on computer vision, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Segment anything, in: Proceedings of the IEEE/CVF international conference on computer vision, pp
Reference 32
- Resolution
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Source-reported events for the cited work
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Observation c5a183b8-57cd-4291-a647-18ca1f773ee5 · outbound
This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
Reference 33
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c610e2fa-eca2-4566-86ce-5721c290d1fd · outbound
This paper cites The group robustness is in the details: Revisiting finetuning under spurious correlations.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery The group robustness is in the details: Revisiting finetuning under spurious correlations
Reference 34
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation bb0c40d9-8692-4e7f-a416-b99716423c6b · outbound
This paper cites A review of remote sensing image segmentation by deep learning methods.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery A review of remote sensing image segmentation by deep learning methods
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.478453Z
Source-reported events for the cited work
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Observation d031a9f2-ddb3-49e4-94eb-26f95f317dd5 · outbound
This paper cites Segearth-ov: Towards training- free open-vocabulary segmentation for remote sensing images, in: Proceedings of the Computer Vision and Pattern Recognition Conference, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Segearth-ov: Towards training- free open-vocabulary segmentation for remote sensing images, in: Proceedings of the Computer Vision and Pattern Recognition Conference, pp
Reference 36
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 8091f22d-4507-4338-9c28-8d4e8aa9160b · outbound
This paper cites Joint-task learning framework with scale adaptive and position guidance modules for improved household rooftop photovoltaic segmentation in remote sensing image.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Joint-task learning framework with scale adaptive and position guidance modules for improved household rooftop photovoltaic segmentation in remote sensing image
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.446017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1d85cc28-e5d1-4522-8444-d7299be5e259 · outbound
This paper cites Understanding rooftop pv panel semantic segmentation of satellite and aerial images for better using machine learning.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Understanding rooftop pv panel semantic segmentation of satellite and aerial images for better using machine learning
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.429928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 28bdebf8-eeab-4c1f-aeb7-79b8344ef112 · outbound
This paper cites Reobench: Benchmarking robustness of earth observation foundation models.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Reobench: Benchmarking robustness of earth observation foundation models
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.412819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 590dc40d-b169-4f5d-ab7a-c0ac9a99d2d1 · outbound
This paper cites Vision-language models in remote sensing: Current progress and future trends.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Vision-language models in remote sensing: Current progress and future trends
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.398526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b9c3410b-bf62-4b74-95ad-f48aaf29068c · outbound
This paper cites Remoteclip: A vision language foundation model for remote sensing.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Remoteclip: A vision language foundation model for remote sensing
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.383420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 57c5af1b-3801-4c82-ab54-5988d3778343 · outbound
This paper cites Pointsam: Pointly-supervised segment anything model for remote sensing images.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Pointsam: Pointly-supervised segment anything model for remote sensing images
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.366428Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0b721383-93fd-4b1f-8f40-9ea99b79b705 · outbound
This paper cites Pv identifier: Extraction of small-scale distributed photovoltaics in complex en- vironments from high spatial resolution remote sensing images.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Pv identifier: Extraction of small-scale distributed photovoltaics in complex en- vironments from high spatial resolution remote sensing images
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.350290Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1deb6340-0421-4ac2-9624-c15cbb0629ce · outbound
This paper cites What drives solar energy adoption in developing countries? evidence from household-level data.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery What drives solar energy adoption in developing countries? evidence from household-level data
Reference 44
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3c8874d4-1c63-44d9-9423-da357ddbfeb0 · outbound
This paper cites an unresolved cited work.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Unresolved cited work
Reference 45
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T17:09:43.334788Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 769d2890-37cd-4677-b34f-3cff5c3c0406 · outbound
This paper cites The segment anything model (sam) for remote sensing applications: From zero to one shot.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery The segment anything model (sam) for remote sensing applications: From zero to one shot
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.300557Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d9151757-4da0-45c0-ad17-1993a74eb17d · outbound
This paper cites Prompt-tuning sam: From generalist to specialist with only 2048 pa- rameters and 16 training images, in: Proceedings of the Computer Vision and Pattern Recognition Conference, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Prompt-tuning sam: From generalist to specialist with only 2048 pa- rameters and 16 training images, in: Proceedings of the Computer Vision and Pattern Recognition Conference, pp
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.286495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2958dacd-f589-4c53-804d-f94f6345009e · outbound
This paper cites Consumer-grade uav imagery facilitates semantic segmentation of species-rich savanna tree layers.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Consumer-grade uav imagery facilitates semantic segmentation of species-rich savanna tree layers
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.271910Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0ea81210-fa13-4a01-b8d7-eeecfbfbc14f · outbound
This paper cites Learning transferable visual models from natural language supervision, in: International conference on machine learning, PmLR.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Learning transferable visual models from natural language supervision, in: International conference on machine learning, PmLR
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.254967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f88658ff-9210-4ba1-99f4-4e09c4b215bf · outbound
This paper cites Segment anything, from space?, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Segment anything, from space?, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.239395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d9f51e70-acb9-43c0-a13c-7572cb29b2f5 · outbound
This paper cites GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:09:42.176933Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 606185b3-8ddd-4f3b-9d2e-d8804e0f3db6 · outbound
This paper cites General generative ai- based image augmentation method for robust rooftop pv segmentation.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery General generative ai- based image augmentation method for robust rooftop pv segmentation
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.224382Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8e961177-b276-4172-a258-12e82aa2da9b · outbound
This paper cites Enhancing pv panel segmentation in remote sensing images with constraint refinement modules.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Enhancing pv panel segmentation in remote sensing images with constraint refinement modules
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.208912Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 619aaebf-53c9-4593-9e62-0a12bfd7e045 · outbound
This paper cites Wikidata: a free collaborative knowledgebase.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Wikidata: a free collaborative knowledgebase
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.192948Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b973d83d-7eb6-435c-ab15-640dc7e15097 · outbound
This paper cites Samrs: Scaling-up remote sens- ing segmentation dataset with segment anything model.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Samrs: Scaling-up remote sens- ing segmentation dataset with segment anything model
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.177035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation bf2d8438-465d-4f83-a7d1-a872023ccb6f · outbound
This paper cites Pv segmenter: A frequency-guided edge-aware network for distributed photovoltaic segmentation in remote sensing imagery.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Pv segmenter: A frequency-guided edge-aware network for distributed photovoltaic segmentation in remote sensing imagery
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.160585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f30e60cc-d2c7-4940-916b-79160bfc427f · outbound
This paper cites Cris: Clip-driven referring image segmentation, in: Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Cris: Clip-driven referring image segmentation, in: Proceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, pp
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.143979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 882f75b9-8352-496e-a37a-3a88800023a4 · outbound
This paper cites Robust fine-tuning of zero-shot models, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Robust fine-tuning of zero-shot models, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.129525Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4fc27e25-9a52-4440-9a31-5a5bf11e898c · outbound
This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Segformer: Simple and efficient design for semantic segmentation with transformers
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.115336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0ec46abf-421e-41cf-a141-828a1c85e7db · outbound
This paper cites Segearth-r2:towards comprehensive language-guided segmentation for remote sensing images.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Segearth-r2:towards comprehensive language-guided segmentation for remote sensing images
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:09:42.216657Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eaeb397d-a31a-4b0c-b303-ec6b11fdad25 · outbound
This paper cites Remotesam: Towards segment anything for earth observation, in: Proceedings of the 33rd ACM International Conference on Multime- dia, pp.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Remotesam: Towards segment anything for earth observation, in: Proceedings of the 33rd ACM International Conference on Multime- dia, pp
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.099976Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9b6a9292-39cc-4890-a298-67ca8e0add94 · outbound
This paper cites Pvsam: Adapting geometric prompts to segment anything model for photovoltaic detection in remote sensing im- agery.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Pvsam: Adapting geometric prompts to segment anything model for photovoltaic detection in remote sensing im- agery
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.082854Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5e1b4b44-c17a-49a4-8d82-1ebd66252b4c · outbound
This paper cites Calibrating multi-modal representations: A pursuit of group robustness without annotations, in: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Calibrating multi-modal representations: A pursuit of group robustness without annotations, in: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.067659Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7eae47b9-be0f-49d1-98de-3db938a9429a · outbound
This paper cites Deepsolar: A machine learning framework to efficiently construct a solar deployment database in the united states.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Deepsolar: A machine learning framework to efficiently construct a solar deployment database in the united states
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.047676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 611fc2c0-72f8-4cab-b1fb-b693cfbf4c85 · outbound
This paper cites Chatearthnet: A global-scale image-text dataset empow- ering vision-language geo-foundation models.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Chatearthnet: A global-scale image-text dataset empow- ering vision-language geo-foundation models
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.030401Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0eec3f04-792f-44e1-8b4f-10bce3b79c33 · outbound
This paper cites Toward global rooftop pv detection with deep active learning.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Toward global rooftop pv detection with deep active learning
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.014298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 48d290e8-e184-4000-a11c-2b8143e6136e · outbound
This paper cites Rsam-seg: A sam-based model with prior knowledge integration for remote sensing image semantic segmen- tation.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Rsam-seg: A sam-based model with prior knowledge integration for remote sensing image semantic segmen- tation
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:42.998664Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 870a038f-b913-4c9a-98ae-33d6ae6cfc37 · outbound
This paper cites an unresolved cited work.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Unresolved cited work
Reference 68
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T17:09:42.982096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 5fb946b2-9a95-4d13-8b56-4ab0faa04abf · outbound
This paper cites Segclip: Multimodal visual-language and prompt learning for high-resolution remote sensing semantic segmentation.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Segclip: Multimodal visual-language and prompt learning for high-resolution remote sensing semantic segmentation
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:42.965590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4003f556-8f2c-4003-8c6c-1f0651fe481a · outbound
This paper cites Rs5m and georsclip: A large-scale vision-language dataset and a large vision-language model for remote sensing.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Rs5m and georsclip: A large-scale vision-language dataset and a large vision-language model for remote sensing
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:42.949466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7f0a7ed4-0428-4481-b483-bbe9c0055cd8 · outbound
This paper cites an unresolved cited work.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Unresolved cited work
Reference 71
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T17:09:42.931384Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 61930ba2-cde2-4b13-ad6a-8bd91d11e364 · outbound
This paper cites Enhancing visual feature constraints in segmentation models for photovoltaic panel recognition.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Enhancing visual feature constraints in segmentation models for photovoltaic panel recognition
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:42.896913Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7a54a29c-d75b-4519-92ba-57aaa671acd8 · outbound
This paper cites GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding
Reference 73
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:09:42.283224Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16e364de-ba15-4962-9cd7-1032868931e3 · outbound
This paper cites Deep learning in remote sensing: A comprehensive review and list of resources.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Deep learning in remote sensing: A comprehensive review and list of resources
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:42.881629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 020cf760-aa9e-4e69-befe-cd68ce58cd9b · outbound
This paper cites an unresolved cited work.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Unresolved cited work
Reference 207
- Resolution
- verified exact
- doi, observed 2026-08-12T17:09:42.374465Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fd5c2edc-5a12-4d6f-8e4a-918ee154c164 · outbound
This paper cites Applied energy 183, 229–240.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Applied energy 183, 229–240
Reference 2016
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.317604Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation f9bc18a5-68ce-41fb-bf0c-a3f519eef14b · outbound
This paper cites an unresolved cited work.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Unresolved cited work
Reference 2017
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T17:09:43.619047Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 07375b1c-7e88-4515-ba96-df2d777bb93e · outbound
This paper cites Expert Systems with Applications 242, 122807.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Expert Systems with Applications 242, 122807
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:42.915118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 8da82278-8e3e-4d25-ad4d-880e6244c80d · outbound
This paper cites IEA, Paris.
Reference 2025
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.570747Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7d257a0f-5631-42f3-b037-935e58b8a5b7 · outbound
This paper cites Scientific Data 13,.
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery Scientific Data 13,
Reference 2026
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T17:09:43.695144Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
No inbound Pith citation observations are available.