Paper Citation Record · LEDGER

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2507.20798.

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pith.paper-citation-record.v1
2507.20798 v1

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measured 34 of 34 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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34 of 34 outbound references displayed

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Outbound references

Observation 008056bd-217a-463d-97b3-3f1f4beee3fb · outbound

This paper cites Forest SAR Tomography: Principles and Applications,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Forest SAR Tomography: Principles and Applications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.246947Z

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

Observation b56dabff-7775-4ef7-83c9-270fd4bba26c · outbound

This paper cites Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.239234Z

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Observation 3fe24927-f8f9-402c-a1c9-e61f96785ff9 · outbound

This paper cites First demonstration of airborne sar to- mography using multibaseline l-band data,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data First demonstration of airborne sar to- mography using multibaseline l-band data,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.231225Z

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Observation 88233aa3-3b6e-4875-b7c4-5eeeab4c3dbb · outbound

This paper cites Polarimetric sar interferometry,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Polarimetric sar interferometry,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.222531Z

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Observation 7062a282-e22f-47f7-af01-dd0d51906edc · outbound

This paper cites Single-baseline polarimetric sar interferometry,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Single-baseline polarimetric sar interferometry,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.214995Z

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.

Observation 63bd05a1-9a34-4b08-829c-c14ec52f8412 · outbound

This paper cites 3-d time-domain sar imaging of a forest using airborne multibaseline data at l- and p-bands,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data 3-d time-domain sar imaging of a forest using airborne multibaseline data at l- and p-bands,

Reference 6

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Observation 6ee786d3-ff2b-4948-88a3-d733cb5dc523 · outbound

This paper cites Multibaseline polarimetric sar tomography of a boreal forest at p- and l-bands,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Multibaseline polarimetric sar tomography of a boreal forest at p- and l-bands,

Reference 7

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Observation dca78d0c-c6a2-41e6-8cbf-c44278ba1cba · outbound

This paper cites Fornaro, G.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Fornaro, G

Reference 8

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Observation 31bb34a9-00c9-40d8-b669-24a2fab5f908 · outbound

This paper cites Three- dimensional imaging of objects concealed below a forest canopy using sar tomography at l-band and wavelet-based sparse estimation,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Three- dimensional imaging of objects concealed below a forest canopy using sar tomography at l-band and wavelet-based sparse estimation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.181484Z

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Observation 84ca787f-4867-4b41-b42a-903c6c5a7450 · outbound

This paper cites On the use of tomographically derived reflectivity profiles for pol-insar forest height inversion in the context of the biomass mission,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data On the use of tomographically derived reflectivity profiles for pol-insar forest height inversion in the context of the biomass mission,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.173228Z

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Observation b8da7102-9751-4a0f-b42d-8869d53f99ef · outbound

This paper cites Forest Height Mapping Using Complex-Valued Convolutional Neural Network,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Forest Height Mapping Using Complex-Valued Convolutional Neural Network,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.165170Z

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Observation d06c0fc6-283a-4247-8973-f8e146cce38a · outbound

This paper cites PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data,

Reference 12

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Observation dbabfc95-c351-4586-9ec0-a336eb11f919 · outbound

This paper cites A Deep Learning Solution for Height Estimation on a Forested Area Based on Pol-TomoSAR Data,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data A Deep Learning Solution for Height Estimation on a Forested Area Based on Pol-TomoSAR Data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.148058Z

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Observation 0730186c-6dac-4c42-8597-7df79ebbec40 · outbound

This paper cites Sar tomography based on deep learning,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Sar tomography based on deep learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.138796Z

Source-reported events for the cited work

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Observation 49585f50-c6f6-48df-b1f0-7b6ed711df6c · outbound

This paper cites A deep learning framework for the estimation of forest height from bistatic tandem-x data,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data A deep learning framework for the estimation of forest height from bistatic tandem-x data,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.130884Z

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.

Observation 35fbbb1d-f301-4d9c-9533-51ec35fba136 · outbound

This paper cites Large-Scale Forest Height Mapping by Combining TanDEM-X and GEDI Data,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Large-Scale Forest Height Mapping by Combining TanDEM-X and GEDI Data,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:57.121631Z

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Observation b07e27b6-09df-4673-b0f6-64f79ddaee9e · outbound

This paper cites Mixed tropical forests canopy height mapping from spaceborne lidar gedi and multisensor imagery using machine learning models,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Mixed tropical forests canopy height mapping from spaceborne lidar gedi and multisensor imagery using machine learning models,

Reference 17

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Observation e7786437-b7d0-4fac-a0ab-1200a3f76eef · outbound

This paper cites A deep- learning approach for sar tomographic imaging of forested areas,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data A deep- learning approach for sar tomographic imaging of forested areas,

Reference 18

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Observation 95338c8c-775c-443d-bb97-ad6c7f4d35c1 · outbound

This paper cites A Machine- Learning Approach to PolInSAR and LiDAR Data Fusion for Improved Tropical Forest Canopy Height Estimation Using NASA AfriSAR Campaign Data,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data A Machine- Learning Approach to PolInSAR and LiDAR Data Fusion for Improved Tropical Forest Canopy Height Estimation Using NASA AfriSAR Campaign Data,

Reference 19

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Source-reported events for the cited work

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Observation c9f2e9b1-b916-4d17-9624-3791cfd797e6 · outbound

This paper cites A lidar-aided multibaseline polinsar method for forest height estimation: With emphasis on dual- baseline selection,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data A lidar-aided multibaseline polinsar method for forest height estimation: With emphasis on dual- baseline selection,

Reference 20

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Observation 7762798e-1544-49f9-b242-def4fb7677a1 · outbound

This paper cites Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning,

Reference 21

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Observation 3f4c62d8-fae6-450d-8cf7-11b026c2e916 · outbound

This paper cites Forest height estimation combining single-polarization tomographic and polsar data,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Forest height estimation combining single-polarization tomographic and polsar data,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:56.250786Z

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Observation bd562348-c6d0-4ca5-82e3-95547cee0b6e · outbound

This paper cites Goodfellow, Y.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Goodfellow, Y

Reference 23

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Observation 126b7c9c-5c17-4f92-a513-2bf3d4950eac · outbound

This paper cites A survey of ensemble learning: Concepts, algorithms, applications, and prospects,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data A survey of ensemble learning: Concepts, algorithms, applications, and prospects,

Reference 24

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Observation 4a558b05-2a7c-45ab-8f26-1432dd7fd564 · outbound

This paper cites Catboost: unbiased boosting with categorical features,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Catboost: unbiased boosting with categorical features,

Reference 25

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Observation 4b0153f7-6e55-40e6-9c92-747267d6ddc1 · outbound

This paper cites Boosted decision trees as an alternative to artificial neural networks for particle identification,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Boosted decision trees as an alternative to artificial neural networks for particle identification,

Reference 26

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Observation e0269be2-0fd2-4d34-8124-ddaa2c6de591 · outbound

This paper cites CatBoost: gradient boosting with categorical features support.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data CatBoost: gradient boosting with categorical features support

Reference 27

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Observation 1fe90322-f4ab-4c9b-8aac-031690cb7811 · outbound

This paper cites Accuracy of small footprint airborne lidar in its predictions of tropical moist forest stand structure,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Accuracy of small footprint airborne lidar in its predictions of tropical moist forest stand structure,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:55.507958Z

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

Observation c5666d7f-407f-438d-a255-fe1546870d16 · outbound

This paper cites Phase calibration based on phase derivative constrained optimization in multibaseline sar tomog- raphy,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Phase calibration based on phase derivative constrained optimization in multibaseline sar tomog- raphy,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:55.286985Z

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

Observation 4e8c9ee7-8059-4501-a630-96751157e8a8 · outbound

This paper cites Algebraic synthesis of forest scenarios from multibaseline polinsar data,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Algebraic synthesis of forest scenarios from multibaseline polinsar data,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:55.098324Z

Source-reported events for the cited work

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Observation fb22f4d3-b3e0-411e-9d07-3dce3efd9a80 · outbound

This paper cites On the separation of ground and canopy scatterings using single polarimetric multi-baseline sar tomography,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data On the separation of ground and canopy scatterings using single polarimetric multi-baseline sar tomography,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:54.952869Z

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Observation 908bfb9b-2233-4c68-bc61-31d904540544 · outbound

This paper cites Analysis of a deep learning solution for tomosar forest reconstruction,.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Analysis of a deep learning solution for tomosar forest reconstruction,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:54.730599Z

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Observation bfbf8cd9-0021-483d-912c-eb2e1b1720e3 · outbound

This paper cites CatBoost: unbiased boosting with categorical features.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data CatBoost: unbiased boosting with categorical features

Reference 2019

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Source-reported events for the cited work

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Observation 1934234e-1f7a-4cdf-a677-294b97a36f04 · outbound

This paper cites Parthenope.

An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data Parthenope

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:19:54.531139Z

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