Paper Citation Record · LEDGER

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation

As of 7 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.03760.

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

pith.paper-citation-record.v1
2607.03760 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T00:08:46.276767Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 70817ad4-d05c-408e-9272-89f8514457d6 · outbound

This paper cites Cloud-net: An end-to-end cloud detec- tion algorithm for landsat 8 imagery,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Cloud-net: An end-to-end cloud detec- tion algorithm for landsat 8 imagery,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84351cd6-8092-4732-b10d-8e440fb05cb5 · outbound

This paper cites Vision transformers for remote sensing image classification,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Vision transformers for remote sensing image classification,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43756c8d-0b36-4032-a0b0-b6f0eb7eadfb · outbound

This paper cites Hdamnet: Hierarchical dilated adaptive mamba network for accurate cloud detection in satellite imagery,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Hdamnet: Hierarchical dilated adaptive mamba network for accurate cloud detection in satellite imagery,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3155f440-1615-4bcd-af8e-5fb5ff5d39e5 · outbound

This paper cites A lightweight network for building extraction from remote sensing images,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation A lightweight network for building extraction from remote sensing images,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5234118f-cfd5-46c6-bd62-febc38c1c081 · outbound

This paper cites Optimizing mobile vision transformers for land cover classification,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Optimizing mobile vision transformers for land cover classification,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 786c3a05-7d48-4c43-a690-5e97e6ef257c · outbound

This paper cites Segment Anything.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Segment Anything

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 540887d5-0d06-43f9-9e6c-6cbdddfb8887 · outbound

This paper cites RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 161e502d-9237-4c86-8937-46e10b7a6ca4 · outbound

This paper cites Road-sam: Adapting the seg- ment anything model to road extraction from large very-high-resolution optical remote sensing images,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Road-sam: Adapting the seg- ment anything model to road extraction from large very-high-resolution optical remote sensing images,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d08f9b7-03d7-45c0-8496-ecf709b1b4c8 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cb6212c-e438-4fb6-8517-2d3c7a4349a7 · outbound

This paper cites EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a12d4c63-85c2-490e-9b1b-ccbb096033d8 · outbound

This paper cites How Do Vision Transformers Work?.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation How Do Vision Transformers Work?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8d82d95-c5f7-48e6-bdff-10258f73c8a4 · outbound

This paper cites Rsam-seg: A sam- based model with prior knowledge integration for remote sensing image semantic segmentation,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Rsam-seg: A sam- based model with prior knowledge integration for remote sensing image semantic segmentation,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e7ff8ec-e6cb-4388-a55f-7f357346dbdc · outbound

This paper cites Cloudsen12, a global dataset for semantic understanding of cloud and cloud shadow in sentinel-2,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Cloudsen12, a global dataset for semantic understanding of cloud and cloud shadow in sentinel-2,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03dd8a52-08cb-4f2c-a800-ed713201b721 · outbound

This paper cites High-quality cloud masking of landsat 8 imagery using convolutional neural networks,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation High-quality cloud masking of landsat 8 imagery using convolutional neural networks,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2691224-b386-4ce3-9037-b172db2af217 · outbound

This paper cites Farm parcel delineation using spatio-temporal convolutional networks,.

GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation Farm parcel delineation using spatio-temporal convolutional networks,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T00:08:46.276767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.