Paper Citation Record · LEDGER

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data

As of 10 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2502.06831.

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

pith.paper-citation-record.v1
2502.06831 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T09:19:23.022513Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation b7bdca7f-a4b2-4dac-9ae8-75ad02370f70 · outbound

This paper cites write newline.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data write newline

Reference 1

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Observation 8ad4fa30-0d2f-4e79-9c18-1066bfa527bf · outbound

This paper cites https://www.naturalearthdata.com/.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data https://www.naturalearthdata.com/

Reference 2

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Observation 404eae6f-3dc9-4801-a080-78edfcc76ee9 · outbound

This paper cites Model error in weather and climate forecasting.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Model error in weather and climate forecasting

Reference 3

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This paper cites The Effects of Regularization and Data Augmentation are Class Dependent.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data The Effects of Regularization and Data Augmentation are Class Dependent

Reference 4

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Unresolved cited work

Reference 5

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This paper cites Gridded population of the world, version 4 (gpwv4): Population density adjusted to match 2015 revision un wpp country totals, revision 11.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Gridded population of the world, version 4 (gpwv4): Population density adjusted to match 2015 revision un wpp country totals, revision 11

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T05:05:15.014573Z

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This paper cites Gridded population of the world, version 4 (gpwv4): Population density adjusted to match 2015 revision un wpp country totals, revision 11, 2018 b.

Reference 7

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Observation 92f80b95-2230-40ee-abe3-e58b614cf1c2 · outbound

This paper cites Implicit Neural Spatial Representations for Time-dependent PDEs.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Implicit Neural Spatial Representations for Time-dependent PDEs

Reference 8

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Observation 32d2aa0c-fb90-47e8-a5d7-1a95fc8e5cfa · outbound

This paper cites Spatial implicit neural representations for global-scale species mapping.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Spatial implicit neural representations for global-scale species mapping

Reference 9

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Observation c2be2ffd-54c1-40c3-b543-8812ad0e8de5 · outbound

This paper cites Gabor wavelets on the sphere.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Gabor wavelets on the sphere

Reference 10

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Observation c8ffe24c-4cfc-4501-95ca-542e71dbad26 · outbound

This paper cites A world of islands.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data A world of islands

Reference 11

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Unresolved cited work

Reference 12

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Observation af537267-ea6f-4bda-b621-8e4286b35796 · outbound

This paper cites An introduction to Laplace transforms and Fourier series.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data An introduction to Laplace transforms and Fourier series

Reference 13

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Observation 3ddca3d6-d0e7-455c-b6c3-d199b3c17060 · outbound

This paper cites Flores, Timothy W.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Flores, Timothy W

Reference 14

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Observation 2951c002-1494-4506-bdb5-d3f596c3111c · outbound

This paper cites Enabling FAIR Research in Earth Science through Research Objects.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Enabling FAIR Research in Earth Science through Research Objects

Reference 15

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Observation 8142b4f3-a4f0-41cb-adaa-af8d2103315f · outbound

This paper cites Nvidia earth2studio, 2024.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Nvidia earth2studio, 2024

Reference 16

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Observation c9d6db1d-edfa-4bbf-b7e3-0b107f1f7bd7 · outbound

This paper cites Measurement of areas on a sphere using fibonacci and latitude–longitude lattices.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Measurement of areas on a sphere using fibonacci and latitude–longitude lattices

Reference 17

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This paper cites Seismic Reservoir Modeling: Theory, Examples, and Algorithms.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Seismic Reservoir Modeling: Theory, Examples, and Algorithms

Reference 18

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This paper cites The era5 global reanalysis, qj roy.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data The era5 global reanalysis, qj roy

Reference 19

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This paper cites Geoinr 1.0: an implicit neural network approach to three-dimensional geological modelling.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Geoinr 1.0: an implicit neural network approach to three-dimensional geological modelling

Reference 20

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This paper cites Huffman, D.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Huffman, D

Reference 21

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Unresolved cited work

Reference 22

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This paper cites Climate endgame: Exploring catastrophic climate change scenarios.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Climate endgame: Exploring catastrophic climate change scenarios

Reference 23

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This paper cites Understanding the Detrimental Class-level Effects of Data Augmentation.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Understanding the Detrimental Class-level Effects of Data Augmentation

Reference 24

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This paper cites An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding

Reference 25

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This paper cites Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells

Reference 26

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This paper cites A review of location encoding for geoai: methods and applications.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data A review of location encoding for geoai: methods and applications

Reference 27

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This paper cites Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions

Reference 28

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data A wavelet tour of signal processing

Reference 29

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This paper cites McEwen, Michael P.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data McEwen, Michael P

Reference 30

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Reference 31

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Implicit Neural Representation in Medical Imaging: A Comparative Survey

Reference 32

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Munday and R

Reference 33

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Shuttle radar topography mission (srtm) global

Reference 35

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files , 2020

Reference 36

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Unresolved cited work

Reference 37

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Planet dump retrieved from https://planet.osm.org

Reference 38

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Palecki, J.H

Reference 39

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This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 40

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks

Reference 41

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Wavelets on the sphere. Application to the detection problem

Reference 42

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data WIRE: Wavelet Implicit Neural Representations

Reference 43

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Implicit neural representations with periodic activation functions

Reference 44

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Noaa climate data record (cdr) of precipitation estimation from remotely sensed information using artificial neural networks (persiann-cdr), version 1 revision 1, 2014

Reference 45

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation

Reference 46

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Unresolved cited work

Reference 47

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data TIML: Task-Informed Meta-Learning for Agriculture

Reference 48

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data Signal Processing for Implicit Neural Representations

Reference 49

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No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data @esa (Ref

Reference 50

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Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales cites this paper.

Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data

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