Paper Citation Record · LEDGER

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.21881.

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

pith.paper-citation-record.v1
2607.21881 v1

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measured 40 of 40 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Outbound references

Observation eeb6f6c9-d93f-4b9f-9803-f14f1b7ef3a6 · outbound

This paper cites PROSPECT: A model of leaf optical properties spectra,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement PROSPECT: A model of leaf optical properties spectra,

Reference 1

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Observation 4ef63df3-5ccc-49fc-b4b8-1cddac599e9b · outbound

This paper cites Multi-trait spectral modeling for estimating grapevine leaf traits and nutrients,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Multi-trait spectral modeling for estimating grapevine leaf traits and nutrients,

Reference 2

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Observation eb2e8394-50f1-4e00-8cd8-83ff30c0812b · outbound

This paper cites Leaf Spectral Reflectance Prediction Using Multi-Head Attention Neural Networks.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Leaf Spectral Reflectance Prediction Using Multi-Head Attention Neural Networks

Reference 3

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Observation 4a01f5f0-7304-4bf2-b06c-d91684933579 · outbound

This paper cites Areviewofadvanced techniques for detecting plant diseases,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Areviewofadvanced techniques for detecting plant diseases,

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Observation 81dd8614-9099-4f81-b2dd-ef33dbdc26bd · outbound

This paper cites Plantdiseasedetectionbyimagingsensors—parallels and specific demands for precision agriculture and plant phenotyping,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Plantdiseasedetectionbyimagingsensors—parallels and specific demands for precision agriculture and plant phenotyping,

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Observation c59a200b-ea23-4328-8c83-2ebb8b0305f5 · outbound

This paper cites Earlydetectionofbranchedbroomrape(Phelipanche ramosa) infestation in tomato crops by using leaf spectral analysis and machine learning,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Earlydetectionofbranchedbroomrape(Phelipanche ramosa) infestation in tomato crops by using leaf spectral analysis and machine learning,

Reference 6

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Observation 05b6c5a6-345c-46f7-bef1-8355f725f9dc · outbound

This paper cites Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture,

Reference 7

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This paper cites Drone-based multispectral imaging and deep learning for timely detection of branched broomrape in tomato farms,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Drone-based multispectral imaging and deep learning for timely detection of branched broomrape in tomato farms,

Reference 8

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Observation a0078b50-a102-48a1-9755-3b62e2edcd0c · outbound

This paper cites Remotesensingforagricultural applications: A meta-review,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Remotesensingforagricultural applications: A meta-review,

Reference 9

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Observation 26c46eba-b3c7-46fa-952f-2c572e73e0dc · outbound

This paper cites Remote sensing for precision agriculture: Sentinel-2 improved features and applications,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Remote sensing for precision agriculture: Sentinel-2 improved features and applications,

Reference 10

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Observation 38b8939f-868d-4c1e-b403-259cae193316 · outbound

This paper cites Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis

Reference 11

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Observation a1fc98aa-6016-4fd2-86e0-cef6113bfce9 · outbound

This paper cites Sentinel-2 for crop yield estimation: A systematic review,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Sentinel-2 for crop yield estimation: A systematic review,

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Observation b2dd8ae3-ff5c-4419-8dc3-fe822ab4a845 · outbound

This paper cites Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis

Reference 13

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Observation 09305cb2-77ab-4e5d-bddf-7f81fadfe3b2 · outbound

This paper cites Twenty five years of remote sensing in precision agriculture:Keyadvancesandremainingknowledgegaps,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Twenty five years of remote sensing in precision agriculture:Keyadvancesandremainingknowledgegaps,

Reference 14

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This paper cites Applications of remote sensing in precision agriculture: A review,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Applications of remote sensing in precision agriculture: A review,

Reference 15

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Observation 9561023b-858b-4eb3-bf43-6d05949a7e4f · outbound

This paper cites Fieldsoftheworld:Amachinelearningbenchmarkdataset for global agricultural field boundary segmentation,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Fieldsoftheworld:Amachinelearningbenchmarkdataset for global agricultural field boundary segmentation,

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Observation 44cd6dfb-a3d4-4bb1-be9d-53259d636a09 · outbound

This paper cites The first global agricultural field boundary map at 10m resolution.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement The first global agricultural field boundary map at 10m resolution

Reference 17

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This paper cites USGS EROS Archive—Aerial Photography—National Agriculture Imagery Program (NAIP),.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement USGS EROS Archive—Aerial Photography—National Agriculture Imagery Program (NAIP),

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement U-Net: Convolutional networks for biomedical image segmentation,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Deepresiduallearningforimage recognition,

Reference 20

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Road extraction by deep residual U-Net,

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This paper cites Delineation of agri- cultural field boundaries from Sentinel-2 images using a novel super- resolution contour detector based on fully convolutional networks,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Delineation of agri- cultural field boundaries from Sentinel-2 images using a novel super- resolution contour detector based on fully convolutional networks,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network,

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This paper cites Advanced fullyconvolutionalnetworksforagriculturalfieldboundarydetection,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Advanced fullyconvolutionalnetworksforagriculturalfieldboundarydetection,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Automated delineation of agricultural field boundaries from Sentinel- 2 images using recurrent residual U-Net,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Segment anything,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement SAM 2: Segment anything in images and videos,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement SAM 3: Segment Anything with Concepts

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement The segment anything model (SAM) for remote sensing applications: From zero to one shot,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement SEMPNet:Enhancingfew-shotremote sensing image semantic segmentation through the integration of the segmentanythingmodel,

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This paper cites Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Deeplearninginagriculture: A survey,

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This paper cites Automating field boundary de- lineation with multi-temporal Sentinel-2 imagery,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Automating field boundary de- lineation with multi-temporal Sentinel-2 imagery,

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This paper cites Delin- eation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Delin- eation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping,

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This paper cites Detect, consolidate, delineate: Scalable mapping of field boundaries using satellite images,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Detect, consolidate, delineate: Scalable mapping of field boundaries using satellite images,

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This paper cites Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning andweaksupervision,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning andweaksupervision,

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This paper cites AI4Boundaries: An open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement AI4Boundaries: An open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography,

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This paper cites A survey of farmland boundary extraction technology based on remote sensing images,.

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement A survey of farmland boundary extraction technology based on remote sensing images,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Encoder–decoder with atrous separable convolution for semantic image segmentation,

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Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations,

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