Paper Citation Record · LEDGER

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation

As of 19 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2506.11314.

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

pith.paper-citation-record.v1
2506.11314 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:17:11.151283Z

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One-hop event checks from named stored sources.

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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

22 of 22 outbound references displayed

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External citation measurements

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Outbound references

Observation 9f268cc5-222e-477a-a66b-2a0e72fdfe2c · outbound

This paper cites The EnMAP spaceborne imaging spectroscopy mission for earth observation,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation The EnMAP spaceborne imaging spectroscopy mission for earth observation,

Reference 1

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Observation 4add1891-473a-43bd-be2b-2d1bc7f6ce86 · outbound

This paper cites SatMAE: Pre-training transformers for temporal and multi-spectral satellite imagery,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation SatMAE: Pre-training transformers for temporal and multi-spectral satellite imagery,

Reference 2

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Observation 9bcf9b55-5e36-4ae8-9005-10052038ce6d · outbound

This paper cites SpectralEarth: Training Hyperspectral Foundation Models at Scale.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation SpectralEarth: Training Hyperspectral Foundation Models at Scale

Reference 3

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Observation f69ada62-af20-4b47-aef6-1c70c6864ad3 · outbound

This paper cites Neural plasticity-inspired multimodal foundation model for earth observation,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Neural plasticity-inspired multimodal foundation model for earth observation,

Reference 4

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Observation 353254e0-8fa5-45f0-a95c-02864c46fff2 · outbound

This paper cites Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation

Reference 5

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Observation 01ef7271-154a-4ea7-a319-d72b2bc30993 · outbound

This paper cites HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods

Reference 6

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Observation 76a10bb3-be21-44cd-b02a-b3f721a50608 · outbound

This paper cites There are no data like more data: datasets for deep learning in earth observation,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation There are no data like more data: datasets for deep learning in earth observation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:13.505332Z

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Observation 72d304bf-fc3d-482b-a841-6a72a4b03f7f · outbound

This paper cites ReUse: REgressive Unet for carbon storage and above-ground biomass estimation,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation ReUse: REgressive Unet for carbon storage and above-ground biomass estimation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:13.497051Z

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Observation 39815160-94cb-43ba-8ee4-f9887f4f48ed · outbound

This paper cites The global ecosystem dynamics investigation: High- resolution laser ranging of the Earth’s forests and topography,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation The global ecosystem dynamics investigation: High- resolution laser ranging of the Earth’s forests and topography,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:13.487641Z

Source-reported events for the cited work

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Observation 466b0d43-5815-44bc-a679-33ed5223b3f7 · outbound

This paper cites Multi-resolution gridded maps of vegetation structure from GEDI,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Multi-resolution gridded maps of vegetation structure from GEDI,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:13.368769Z

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Observation cdbe1b2d-8254-4f77-abf9-5cc1f1844f27 · outbound

This paper cites an unresolved cited work.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Unresolved cited work

Reference 11

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Observation 6f376764-04f7-447e-8324-e136b9166b31 · outbound

This paper cites Influence of GEDI acquisition and processing parameters on canopy height estimates over tropical forests,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Influence of GEDI acquisition and processing parameters on canopy height estimates over tropical forests,

Reference 12

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Observation cf9656a5-118e-4566-b5e8-645238aa8743 · outbound

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

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation High-resolution global maps of 21st-century forest cover change,

Reference 13

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Observation fa6952a1-49e5-40f0-9b6a-bf9dddce5a2a · outbound

This paper cites Unified perceptual parsing for scene understanding,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Unified perceptual parsing for scene understanding,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:12.692552Z

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Observation 77f4687a-33e8-4d1d-b0b4-bfd1d6dd8065 · outbound

This paper cites PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models

Reference 15

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Observation b70c9c06-1c48-4066-b9b7-fad0d8ca28cd · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Momentum contrast for unsupervised visual representation learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:12.492129Z

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Observation 13eef073-8430-4eee-a903-4cabf9226599 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Masked autoencoders are scalable vision learners,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:12.404024Z

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Observation 845bfa5d-462e-4e9c-8be0-ea9f36e38754 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation DINOv2: Learning Robust Visual Features without Supervision

Reference 18

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Observation 366e5869-0df2-4766-b176-dc419d52f90f · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation U-net: Convolutional networks for biomedical image segmentation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:12.170466Z

Source-reported events for the cited work

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Observation 5d9c70da-b8a8-43b3-aeee-0d4d3612989e · outbound

This paper cites Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:17:11.448072Z

Source-reported events for the cited work

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Observation cdb8bc47-ff22-44e4-ba71-50655b111d61 · outbound

This paper cites Prithvi-EO-2.0: A versatile multi-temporal foundation model for earth observation applications,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Prithvi-EO-2.0: A versatile multi-temporal foundation model for earth observation applications,

Reference 21

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Observation d42a5e80-380a-425c-900d-8017df096bc0 · outbound

This paper cites Upscaling forest biomass from field to satellite measurements: Sources of errors and ways to reduce them,.

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation Upscaling forest biomass from field to satellite measurements: Sources of errors and ways to reduce them,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:11.934942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.