Paper Citation Record · LEDGER

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images

As of 19 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2507.08096.

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

pith.paper-citation-record.v1
2507.08096 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:35:29.258666Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

20 of 20 outbound references displayed

verified exact9
verified fuzzy4
unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba98bbb2-7e2d-49c0-b6c6-ee646f928892 · outbound

This paper cites Automatic 3D Multiple Building Change Detection Model Based on Encoder–Decoder Network Using Highly Unbalanced Remote Sensing Datasets.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Automatic 3D Multiple Building Change Detection Model Based on Encoder–Decoder Network Using Highly Unbalanced Remote Sensing Datasets

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35f8a8d2-b7ef-47ec-9d70-620e7aa3655a · outbound

This paper cites 3D urban object change detection from aerial and terrestrial point clouds: A review.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images 3D urban object change detection from aerial and terrestrial point clouds: A review

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:35:32.300933Z

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 8ee7990f-c4a0-4c56-a20d-433e6d6f1980 · outbound

This paper cites A Lightweight Building Extraction Approach for Contour Recovery in Complex Urban Environments.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images A Lightweight Building Extraction Approach for Contour Recovery in Complex Urban Environments

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:35:33.015175Z

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 81b1dbb6-3686-4c54-8fc8-76d9d22ba3d0 · outbound

This paper cites A prior knowledge guided deep learning method for building extraction from high-resolution remote sensing images.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images A prior knowledge guided deep learning method for building extraction from high-resolution remote sensing images

Reference 4

Resolution: verified exact; doi, observed 2026-08-06T18:35:31.243573Z

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 1b2eecc0-14b7-4a0b-9d68-c4da4a090c21 · outbound

This paper cites Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Deep learning-based building height mapping using Sentinel-1 and Sentinel-2 data

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:35:32.144379Z

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 a89a8a0b-a3b6-4333-8188-31c335bc7308 · outbound

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

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images U-Net: Convolutional networks for biomedical image segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:35:32.847525Z

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 93360fa6-b41b-4d25-834d-28881ce268c3 · outbound

This paper cites A CNN Regression Model to Estimate Buildings Height Maps Using Sentinel-1 SAR and Sentinel-2 MSI Time Series.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images A CNN Regression Model to Estimate Buildings Height Maps Using Sentinel-1 SAR and Sentinel-2 MSI Time Series

Reference 7

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Source-reported events for the cited work

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Observation e377b197-c765-4482-a1da-bb88f3cce8fb · outbound

This paper cites FusionHeightNet: A Multi-Level Cross-Fusion Method from Multi-Source Remote Sensing Images for Urban Building Height Estimation.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images FusionHeightNet: A Multi-Level Cross-Fusion Method from Multi-Source Remote Sensing Images for Urban Building Height Estimation

Reference 8

Resolution: verified exact; doi, observed 2026-08-06T18:35:30.959327Z

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 a3c82013-2734-4d2e-a104-e7590e7b5ca1 · outbound

This paper cites How high are we? Large-scale building height estimation at 10 m using Sentinel-1 SAR and Sentinel-2 MSI time series.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images How high are we? Large-scale building height estimation at 10 m using Sentinel-1 SAR and Sentinel-2 MSI time series

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:35:31.837092Z

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 7a3c1c65-70cc-4a17-a74a-926d516972fd · outbound

This paper cites Developing a method to estimate building height from Sentinel-1 data.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Developing a method to estimate building height from Sentinel-1 data

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:35:27.981244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72d01d0b-0c86-4e27-a4ab-c7e6a9641404 · outbound

This paper cites Automated Estimation of Building Heights with ICESat-2 and GEDI LiDAR Altimeter and Building Footprints: The Case of New York City and Los Angeles.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Automated Estimation of Building Heights with ICESat-2 and GEDI LiDAR Altimeter and Building Footprints: The Case of New York City and Los Angeles

Reference 11

Resolution: verified exact; doi, observed 2026-08-06T18:35:30.712124Z

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 bae5e7ca-6375-4e84-9e21-653037b5eacd · outbound

This paper cites 2D building change detection from high resolution satelliteimagery: A two-step hierarchical method based on 3D invariant primitives.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images 2D building change detection from high resolution satelliteimagery: A two-step hierarchical method based on 3D invariant primitives

Reference 12

Resolution: verified exact; doi, observed 2026-08-06T18:35:30.445378Z

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 8d5902fb-2cd6-4ec8-bcf4-de4826dae192 · outbound

This paper cites The SAR2Height framework for urban height map reconstruction from single SAR intensity images.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images The SAR2Height framework for urban height map reconstruction from single SAR intensity images

Reference 13

Resolution: verified exact; doi, observed 2026-08-06T18:35:30.124914Z

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 7b897f74-b427-4ca3-932f-868e18af8b62 · outbound

This paper cites Large-scale building height retrieval from single SAR imagery based on bounding box regression networks.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Large-scale building height retrieval from single SAR imagery based on bounding box regression networks

Reference 14

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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 3c7b2543-1ed1-48ca-bd2e-f2fa7edecb08 · outbound

This paper cites COSMO-SkyMed an existing opportunity for observing the Earth.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images COSMO-SkyMed an existing opportunity for observing the Earth

Reference 15

Resolution: verified exact; doi, observed 2026-08-06T18:35:29.891190Z

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 0c204581-6c94-4df5-974a-251e21f3f5d1 · outbound

This paper cites EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings

Reference 16

Resolution: verified exact; doi, observed 2026-08-06T18:35:29.652919Z

Source-reported events for the cited work

correction dated 2023-04-18. Source: crossref record 10.1038/s41597-023-02141-y->10.1038/s41597-023-02040-2:correction, observed 2026-07-11T02:58:24.110016+00:00. This notice travels one citation hop only.

Observation c81c9edc-03cc-4270-8a04-d48e092ceaa4 · outbound

This paper cites Building Floorspace in China: A Dataset and Learning Pipeline.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Building Floorspace in China: A Dataset and Learning Pipeline

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:35:31.465922Z

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 0046fdc9-2e73-4d12-b6b9-c64cf9523a64 · outbound

This paper cites Data augmentation for building footprint segmenta- tion in SAR images: an empirical study.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Data augmentation for building footprint segmenta- tion in SAR images: an empirical study

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:35:32.598673Z

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 10e43113-c0ba-44a3-87fb-f3344a0946c9 · outbound

This paper cites Deep residual learning for image recognition.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Deep residual learning for image recognition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:35:32.461388Z

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 e7957d28-bd92-445a-959c-926b10689519 · outbound

This paper cites Leveraging Chinese GaoFen-7 imagery for high-resolution building height estimation in multiple cities.

An Object-Based Deep Learning Approach for Building Height Estimation from Single SAR Images Leveraging Chinese GaoFen-7 imagery for high-resolution building height estimation in multiple cities

Reference 20

Resolution: verified exact; doi, observed 2026-08-06T18:35:29.434348Z

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.