Pith. sign in

REVIEW 2 major objections 1 minor 1 cited by

Traditional fidelity metrics like PSNR often fail to select the best super-resolution models for Earth monitoring tasks.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.3

2026-07-01 08:21 UTC pith:PEC4WROT

load-bearing objection GeoSR-Bench shows traditional fidelity metrics often fail to predict or even hurt downstream task performance on remote-sensing data, backed by 270 settings across 36k image pairs. the 2 major comments →

arxiv 2605.00310 v2 pith:PEC4WROT submitted 2026-05-01 cs.CV cs.AIcs.LG

Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration

Zhili Li , Kangyang Chai , Zhihao Wang , Xiaowei Jia , Yanhua Li , Gengchen Mai , Sergii Skakun , Dinesh Manocha
show 1 more author
Yiqun Xie
This is my paper
classification cs.CV cs.AIcs.LG
keywords super-resolutionremote sensingdownstream tasksbenchmark datasetimage fidelity metricsland cover segmentationEarth observation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper creates GeoSR-Bench, a collection of 36,000 quality-controlled satellite image pairs spanning multiple resolutions and land covers, to test whether super-resolution outputs that score well on visual metrics actually improve real downstream work. It evaluates nine SR models across GAN, transformer, neural operator, and diffusion families on five tasks including land cover segmentation, infrastructure mapping, and biophysical variable estimation. Results show that gains in PSNR or SSIM frequently show zero or negative correlation with task accuracy, meaning models chosen by standard metrics can underperform on practical monitoring. A reader would care because current SR development loops may be optimizing for the wrong objective when the end goal is usable satellite data for planning or disaster response.

Core claim

When SR models are applied to the co-located image pairs in GeoSR-Bench and the outputs are fed into fixed downstream task models, improvements on conventional fidelity metrics do not reliably produce gains in task performance and can even produce losses, revealing that those metrics supply limited guidance for choosing models intended for Earth observation applications.

What carries the argument

GeoSR-Bench, the spatially co-located and temporally aligned image-pair dataset that directly links SR outputs to five downstream Earth-monitoring tasks for joint evaluation.

Load-bearing premise

The five selected downstream tasks together with the 36,000 image pairs adequately capture the real-world value of super-resolved satellite imagery for monitoring applications.

What would settle it

A controlled experiment on a new set of tasks or image pairs in which higher PSNR or SSIM scores consistently predict higher downstream accuracy across the same SR model families would falsify the central claim.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • SR model rankings for remote sensing shift when evaluation uses task performance instead of fidelity scores.
  • Model developers should incorporate downstream task losses or validation during training rather than relying solely on reconstruction objectives.
  • Existing published SR results for satellite imagery may need re-evaluation for actual utility.
  • Cross-platform SR tasks require separate benchmarking because correlation patterns differ by resolution gap.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Loss functions that directly optimize task-relevant features rather than pixel-level similarity could close the observed gap.
  • The same mismatch between fidelity and utility may appear in SR for medical or autonomous-driving imagery once comparable task-linked benchmarks exist.
  • Task-integrated benchmarks could become a standard requirement for publishing SR work aimed at operational use.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper introduces GeoSR-Bench, a benchmark dataset of ~36,000 spatially co-located, temporally aligned, quality-controlled image pairs spanning resolutions from 500 m to 0.6 m across diverse land covers. It evaluates 9 SR models (GAN, transformer, neural operator, diffusion-based) across 2 cross-platform tasks using 270 experimental settings that integrate 3 downstream task models and 5 downstream tasks (land cover segmentation, infrastructure mapping, biophysical variable estimation, and two others). The central empirical finding is that gains in traditional fidelity metrics (PSNR, SSIM) frequently show zero or negative correlation with downstream task performance, implying these metrics offer limited guidance for selecting SR models useful for Earth monitoring applications.

Significance. If the reported decorrelations hold after verification of the experimental protocol, the work would be significant for remote-sensing SR research by providing a large-scale empirical demonstration that fidelity metrics are unreliable proxies for task utility. The scale (36k pairs, 270 settings) and explicit integration of downstream tasks constitute a concrete contribution that could shift evaluation practices in applications such as agriculture, urban planning, and disaster response. The absence of mathematical derivations or fitted parameters is appropriate for an empirical benchmark study.

major comments (2)
  1. [Abstract] Abstract and benchmark description: the assertion that the five chosen downstream tasks plus the 36,000 pairs 'adequately represent' real-world Earth-monitoring utility is load-bearing for the generalizability of the negative-correlation claim, yet no quantitative coverage metrics, land-cover stratification statistics, or sensitivity analysis to alternative task selections are supplied.
  2. [Results (270 experimental settings)] Results section on the 270 settings: the reported negative or zero correlations cannot be assessed for robustness without explicit description of the correlation calculation method (Pearson/Spearman, per-task or aggregated), controls for image-pair quality variation, and the precise training protocols for the three downstream task models.
minor comments (1)
  1. [Abstract] The abstract refers to 'two others' among the five downstream tasks without naming them; listing all five explicitly would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the generalizability of our claims and the robustness of the reported correlations. We respond to each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Abstract] Abstract and benchmark description: the assertion that the five chosen downstream tasks plus the 36,000 pairs 'adequately represent' real-world Earth-monitoring utility is load-bearing for the generalizability of the negative-correlation claim, yet no quantitative coverage metrics, land-cover stratification statistics, or sensitivity analysis to alternative task selections are supplied.

    Authors: We agree that quantitative coverage metrics would better support the generalizability claim. The current manuscript states that the pairs span diverse land covers but does not include explicit stratification statistics or coverage metrics. In revision we will add land-cover type distributions and geographic coverage statistics derived from the 36,000 locations. A full sensitivity analysis to alternative task selections would require new experiments outside the present benchmark scope; we will instead expand the task-selection rationale in the discussion. revision: partial

  2. Referee: [Results (270 experimental settings)] Results section on the 270 settings: the reported negative or zero correlations cannot be assessed for robustness without explicit description of the correlation calculation method (Pearson/Spearman, per-task or aggregated), controls for image-pair quality variation, and the precise training protocols for the three downstream task models.

    Authors: We acknowledge that these methodological details should be stated more explicitly. The correlations were computed using Spearman rank correlation, reported both per downstream task and in aggregated form. Image-pair quality variation was controlled via the quality-controlled co-location and filtering procedure described in Section 3. The three downstream task models were trained with fixed hyperparameters and standard protocols detailed in Section 4.2 and the supplementary material. We will revise the Results section to state the correlation method, quality controls, and training protocols explicitly. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark with no derivation chain or self-referential reductions

full rationale

This is a purely empirical benchmark paper introducing GeoSR-Bench and reporting experimental results across 270 settings on 9 SR models and 5 downstream tasks. No equations, fitted parameters, predictions, or derivations are present in the abstract or described structure. The central claim (lack of correlation between fidelity metrics and task performance) is a direct observation from the collected data rather than a quantity defined by the authors' own prior choices or self-citations. No load-bearing steps reduce to self-definition, fitted inputs, or ansatzes smuggled via citation. The representativeness concern is a validity issue, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract introduces a new benchmark dataset but contains no free parameters, mathematical axioms, or invented physical entities; the contribution is purely empirical construction and evaluation.

pith-pipeline@v0.9.1-grok · 5887 in / 1280 out tokens · 45376 ms · 2026-07-01T08:21:15.860959+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration." pith.science (2026). https://pith.science/paper/PEC4WROT

@misc{pith2026260500310,
  author       = {Pith},
  title        = {Pith review of: Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PEC4WROT}},
  note         = {Machine review of arXiv:2605.00310}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs. The increased resolution provides visual enhancement and utility for monitoring tasks. In particular, SR has been increasingly developed for satellite-based Earth observation, with applications in urban planning, agriculture, ecology, and disaster response. However, existing SR studies and benchmarks typically use fidelity metrics such as PSNR or SSIM, whereas the true utility of super-resolved images lies in supporting downstream tasks such as land cover classification, biomass estimation, and change detection. To bridge this gap, we introduce GeoSR-Bench, a downstream task-integrated SR benchmark dataset to evaluate SR models beyond fidelity metrics. GeoSR-Bench comprises spatially co-located, temporally aligned, and quality-controlled image pairs from about 36,000 locations across diverse land covers, spanning resolutions from 500m to 0.6m. To the best of our knowledge, GeoSR-Bench is the first SR benchmark that directly connects improved image resolution from SR models with downstream Earth monitoring tasks, including land cover segmentation, infrastructure mapping, and biophysical variable estimation. Using GeoSR-Bench, we benchmark GAN, transformer, neural operator, and diffusion-based SR models on perceptual quality and downstream task performance. We conduct experiments with 270 settings, covering 2 cross-platform SR tasks, 9 SR models, 3 downstream task models, and 5 downstream tasks for each SR task. The results show that improvements in traditional SR metrics often do not correlate with gains in task performance, and the correlations can be negative, indicating that these metrics provide limited guidance for selecting superior models for downstream tasks. This reveals the need to integrate downstream tasks into SR model development and evaluation.

Figures

Figures reproduced from arXiv: 2605.00310 by Dinesh Manocha, Gengchen Mai, Kangyang Chai, Sergii Skakun, Xiaowei Jia, Yanhua Li, Yiqun Xie, Zhihao Wang, Zhili Li.

Figure 1
Figure 1. Figure 1: GeoSR benchmark focuses on two cross-platform SR tasks: MODIS [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: GeoSR Dataset Construction Process Overview. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 1
Figure 1. Figure 1: These two SR tasks also cover diverse utilities. For [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Spatial distributions of image pairs in (a) MODIS-to-Landsat-8 and (b) Sentinel-2-to-NAIP SR coincident datasets. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: PSNR and SSIM do not always align with visual perception in SR. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Relative performance comparison across SR models on the MODIS-to-Landsat-8 downstream tasks using SegFormer as the downstream model. Relative [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Relative performance comparison across SR models on the Sentinel-2-to-NAIP downstream tasks using SegFormer as the downstream model. Relative [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Pearson correlation between visual fidelity metrics (PSNR/SSIM) and downstream task performance computed within the top- [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Spearman’s rank correlation between visual fidelity metrics (PSNR/SSIM) and downstream task performance computed within the top- [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Downstream task performance comparison on the TreeFinder dataset [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗

Review history (2 revisions) →

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Does Super-Resolution Preserve Defect Evidence? A Low-False-Call Benchmark for Semiconductor Inspection

    cs.CV 2026-07 accept novelty 6.0

    Learned super-resolution improves SSIM but reduces defect-pixel recall under a fixed detector, and clean-calibration feasibility does not transfer to held-out low-false-positive inspection.

Reference graph

Works this paper leans on

85 extracted references · 85 canonical work pages · cited by 1 Pith paper

  1. [1]

    Ntire 2017 challenge on single image super-resolution: Dataset and study

    Eirikur Agustsson and Radu Timofte. Ntire 2017 challenge on single image super-resolution: Dataset and study. InProceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 126–135, 2017

    work page 2017

  2. [2]

    Improving component substitution pansharpening through multivariate regression of ms + pan data.IEEE Transactions on Geoscience and Remote Sensing, 45(10):3230– 3239, 2007

    Bruno Aiazzi, Stefano Baronti, and Massimo Selva. Improving component substitution pansharpening through multivariate regression of ms + pan data.IEEE Transactions on Geoscience and Remote Sensing, 45(10):3230– 3239, 2007

    work page 2007

  3. [3]

    Canopy height model and naip imagery pairs across conus.Scientific Data, 12(1):322, 2025

    Brady W Allred, Sarah E McCord, and Scott L Morford. Canopy height model and naip imagery pairs across conus.Scientific Data, 12(1):322, 2025

    work page 2025

  4. [4]

    Apache Sedona

    Apache. Apache Sedona. https://sedona.apache.org/1.6.0/, 2025. Ac- cessed: 2025-11-05

    work page 2025

  5. [5]

    Cnn-based super-resolution of hyperspectral images.IEEE Transactions on Geoscience and Remote Sensing, 58(9):6106–6121, 2020

    Pattathal V Arun, Krishna Mohan Buddhiraju, Alok Porwal, and Jocelyn Chanussot. Cnn-based super-resolution of hyperspectral images.IEEE Transactions on Geoscience and Remote Sensing, 58(9):6106–6121, 2020

    work page 2020

  6. [6]

    Toward real-world single image super-resolution: A new benchmark and a new model

    Jianrui Cai, Hui Zeng, Hongwei Yong, Zisheng Cao, and Lei Zhang. Toward real-world single image super-resolution: A new benchmark and a new model. InProceedings of the IEEE/CVF international conference on computer vision, pages 3086–3095, 2019

    work page 2019

  7. [7]

    Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach

    Shenglong Chen, Yoshiki Ogawa, Chenbo Zhao, and Yoshihide Sekimoto. Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach. ISPRS Journal of Photogrammetry and Remote Sensing, 195:129–152, 2023

    work page 2023

  8. [8]

    Activating more pixels in image super-resolution transformer

    Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, and Chao Dong. Activating more pixels in image super-resolution transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22367–22377, 2023

    work page 2023

  9. [9]

    Binarized diffusion model for image super- resolution.Advances in Neural Information Processing Systems, 37:30651–30669, 2024

    Zheng Chen, Haotong Qin, Yong Guo, Xiongfei Su, Xin Yuan, Linghe Kong, and Yulun Zhang. Binarized diffusion model for image super- resolution.Advances in Neural Information Processing Systems, 37:30651–30669, 2024

    work page 2024

  10. [10]

    Recursive generalization transformer for image super-resolution.arXiv preprint arXiv:2303.06373, 2023

    Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, and Xiaokang Yang. Recursive generalization transformer for image super-resolution.arXiv preprint arXiv:2303.06373, 2023

    work page arXiv 2023

  11. [11]

    A solar panel dataset of very high resolution satellite imagery to support the sustainable development goals

    Cecilia N Clark and Fabio Pacifici. A solar panel dataset of very high resolution satellite imagery to support the sustainable development goals. Scientific Data, 10(1):636, 2023

    work page 2023

  12. [12]

    Open high- resolution satellite imagery: The worldstrat dataset–with application to super-resolution.Advances in Neural Information Processing Systems, 35:25979–25991, 2022

    Julien Cornebise, Ivan Oršoli ´c, and Freddie Kalaitzis. Open high- resolution satellite imagery: The worldstrat dataset–with application to super-resolution.Advances in Neural Information Processing Systems, 35:25979–25991, 2022

    work page 2022

  13. [13]

    National land cover database (nlcd) 2021 products, 2023

    Jon Dewitz. National land cover database (nlcd) 2021 products, 2023. U.S. Geological Survey data release

    work page 2021

  14. [14]

    Learning a deep convolutional network for image super-resolution

    Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Learning a deep convolutional network for image super-resolution. InComputer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13, pages 184–199. Springer, 2014

    work page 2014

  15. [15]

    Esa worldcover global land cover service

    European Space Agency (ESA) WorldCover. Esa worldcover global land cover service. https://esa-worldcover.org/en/data-access, 2021. Accessed: 2025-12-11

    work page 2021

  16. [16]

    Remote sensing time series analysis: A review of data and applications

    Yingchun Fu, Zhe Zhu, Liangyun Liu, Wenfeng Zhan, Tao He, Huanfeng Shen, Jun Zhao, Yongxue Liu, Hongsheng Zhang, Zihan Liu, et al. Remote sensing time series analysis: A review of data and applications. Journal of Remote Sensing, 4:0285, 2024

    work page 2024

  17. [17]

    Copernicus_s2_cloud_probability: Sentinel -2 cloud probability

    Google Earth Engine. Copernicus_s2_cloud_probability: Sentinel -2 cloud probability. https://developers.google.com/earth-engine/datasets/catalog/ COPERNICUS_S2_CLOUD_PROBABILITY, 2025. Accessed: 2025- 12-11

    work page 2025

  18. [18]

    Spatial-temporal super-resolution of satellite imagery via conditional pixel synthesis

    Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David Lobell, and Stefano Ermon. Spatial-temporal super-resolution of satellite imagery via conditional pixel synthesis. Advances in Neural Information Processing Systems, 34:27903–27915, 2021

    work page 2021

  19. [19]

    Tracking urbanization in developing regions with remote sensing spatial-temporal super-resolution.arXiv preprint arXiv:2204.01736, 2022

    Yutong He, William Zhang, Chenlin Meng, Marshall Burke, David B Lobell, and Stefano Ermon. Tracking urbanization in developing regions with remote sensing spatial-temporal super-resolution.arXiv preprint arXiv:2204.01736, 2022

    work page arXiv 2022

  20. [20]

    Cascaded diffusion models for high fidelity image generation.Journal of Machine Learning Research, 23(47):1–33, 2022

    Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffusion models for high fidelity image generation.Journal of Machine Learning Research, 23(47):1–33, 2022

    work page 2022

  21. [21]

    Efficient swin transformer for remote sensing image super-resolution.IEEE Transactions on Image Processing, 2024

    Xudong Kang, Puhong Duan, Jier Li, and Shutao Li. Efficient swin transformer for remote sensing image super-resolution.IEEE Transactions on Image Processing, 2024

    work page 2024

  22. [22]

    Lobell, and Stefano Ermon

    Samar Khanna, Patrick Liu, Linqi Zhou, Chenlin Meng, Robin Rombach, Marshall Burke, David B. Lobell, and Stefano Ermon. Diffusionsat: A generative foundation model for satellite imagery. InThe Twelfth International Conference on Learning Representations, 2024

    work page 2024

  23. [23]

    Accurate image super-resolution using very deep convolutional networks

    Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 1646–1654, 2016

    work page 2016

  24. [24]

    Single-image super-resolution using sparse regression and natural image prior.IEEE transactions on pattern analysis and machine intelligence, 32(6):1127–1133, 2010

    Kwang In Kim and Younghee Kwon. Single-image super-resolution using sparse regression and natural image prior.IEEE transactions on pattern analysis and machine intelligence, 32(6):1127–1133, 2010

    work page 2010

  25. [25]

    Toward bridging the simulated-to-real gap: Benchmarking super-resolution on real data.IEEE transactions on pattern analysis and machine intelligence, 42(11):2944–2959, 2019

    Thomas Köhler, Michel Bätz, Farzad Naderi, André Kaup, Andreas Maier, and Christian Riess. Toward bridging the simulated-to-real gap: Benchmarking super-resolution on real data.IEEE transactions on pattern analysis and machine intelligence, 42(11):2944–2959, 2019

    work page 2019

  26. [26]

    A real-world benchmark for sentinel-2 multi-image super-resolution.Scientific Data, 10(1):644, 2023

    Pawel Kowaleczko, Tomasz Tarasiewicz, Maciej Ziaja, Daniel Kostrzewa, Jakub Nalepa, Przemyslaw Rokita, and Michal Kawulok. A real-world benchmark for sentinel-2 multi-image super-resolution.Scientific Data, 10(1):644, 2023

    work page 2023

  27. [27]

    A high-resolution canopy height model of the earth.Nature Ecology & Evolution, 7(11):1778–1789, 2023

    Nico Lang, Walter Jetz, Konrad Schindler, and Jan Dirk Wegner. A high-resolution canopy height model of the earth.Nature Ecology & Evolution, 7(11):1778–1789, 2023

    work page 2023

  28. [28]

    Photo-realistic single image super- resolution using a generative adversarial network

    Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Jo- hannes Totz, Zehan Wang, et al. Photo-realistic single image super- resolution using a generative adversarial network. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 4681–4690, 2017

    work page 2017

  29. [29]

    Transformer-based multistage enhancement for remote sensing image super-resolution.IEEE Transac- tions on Geoscience and Remote Sensing, 60:1–11, 2021

    Sen Lei, Zhenwei Shi, and Wenjing Mo. Transformer-based multistage enhancement for remote sensing image super-resolution.IEEE Transac- tions on Geoscience and Remote Sensing, 60:1–11, 2021

    work page 2021

  30. [30]

    Sed: Semantic-aware discriminator for image super-resolution

    Bingchen Li, Xin Li, Hanxin Zhu, Yeying Jin, Ruoyu Feng, Zhizheng Zhang, and Zhibo Chen. Sed: Semantic-aware discriminator for image super-resolution. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 25784–25795, 2024

    work page 2024

  31. [31]

    A global analysis of sentinel-2a, sentinel-2b and landsat-8 data revisit intervals and implications for terrestrial monitoring

    Jian Li and David P Roy. A global analysis of sentinel-2a, sentinel-2b and landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9(9):902, 2017

    work page 2017

  32. [32]

    Lsdir: A large scale dataset for image restoration

    Yawei Li, Kai Zhang, Jingyun Liang, Jiezhang Cao, Ce Liu, Rui Gong, Yulun Zhang, Hao Tang, Yun Liu, Denis Demandolx, et al. Lsdir: A large scale dataset for image restoration. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1775– 1787, 2023

    work page 2023

  33. [33]

    A new sensor bias-driven spatio-temporal fusion model based on convolutional neural networks.Science China Information Sciences, 63:1–16, 2020

    Yunfei Li, Jun Li, Lin He, Jin Chen, and Antonio Plaza. A new sensor bias-driven spatio-temporal fusion model based on convolutional neural networks.Science China Information Sciences, 63:1–16, 2020

    work page 2020

  34. [34]

    Swinir: Image restoration using swin transformer

    Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. Swinir: Image restoration using swin transformer. InProceedings of the IEEE/CVF international conference on computer vision, pages 1833–1844, 2021

    work page 2021

  35. [35]

    Enhanced deep residual networks for single image super- resolution

    Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. Enhanced deep residual networks for single image super- resolution. InProceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 136–144, 2017

    work page 2017

  36. [36]

    Text2earth: Unlocking text-driven remote sensing image generation with a global-scale dataset and a foundation model.arXiv preprint arXiv:2501.00895, 2025

    Chenyang Liu, Keyan Chen, Rui Zhao, Zhengxia Zou, and Zhenwei Shi. Text2earth: Unlocking text-driven remote sensing image generation with a global-scale dataset and a foundation model.arXiv preprint arXiv:2501.00895, 2025

    work page arXiv 2025

  37. [37]

    JG Liu. Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details.International Journal of remote sensing, 21(18):3461–3472, 2000

    work page 2000

  38. [38]

    Difffno: Diffusion fourier neural operator

    Xiaoyi Liu and Hao Tang. Difffno: Diffusion fourier neural operator. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 150–160, 2025

    work page 2025

  39. [39]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. InProceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021

    work page 2021

  40. [40]

    China building rooftop area: the first multi-annual (2016–2021) and high-resolution (2.5 JOURNAL OF LATEX CLASS FILES, VOL

    Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu. China building rooftop area: the first multi-annual (2016–2021) and high-resolution (2.5 JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 17 m) building rooftop area dataset in china derived with super-resolution segmentation from sentinel-2 imagery.Earth System Science Data, 15(8):3547–3572, 2023

    work page 2016

  41. [41]

    Transformer for single image super-resolution

    Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 457–466, 2022

    work page 2022

  42. [42]

    Super- resolution of proba-v images using convolutional neural networks

    Marcus Märtens, Dario Izzo, Andrej Krzic, and Daniël Cox. Super- resolution of proba-v images using convolutional neural networks. Astrodynamics, 3:387–402, 2019

    work page 2019

  43. [43]

    A conditional diffusion model with fast sampling strategy for remote sensing image super-resolution.IEEE Transactions on Geoscience and Remote Sensing, 2024

    Fanen Meng, Yijun Chen, Haoyu Jing, Laifu Zhang, Yiming Yan, Yingchao Ren, Sensen Wu, Tian Feng, Renyi Liu, and Zhenhong Du. A conditional diffusion model with fast sampling strategy for remote sensing image super-resolution.IEEE Transactions on Geoscience and Remote Sensing, 2024

    work page 2024

  44. [44]

    Global urban areas: High-resolution population density and settlement data

    Meta Data for Good. Global urban areas: High-resolution population density and settlement data. https://dataforgood.facebook.com/dfg/tools/ globalurbanareas, 2023. Accessed: 2025-12-16

    work page 2023

  45. [45]

    Sen2venµs, a dataset for the training of sentinel-2 super-resolution algorithms.Data, 7(7):96, 2022

    Julien Michel, Juan Vinasco-Salinas, Jordi Inglada, and Olivier Hagolle. Sen2venµs, a dataset for the training of sentinel-2 super-resolution algorithms.Data, 7(7):96, 2022

    work page 2022

  46. [46]

    USRoadDetections: U.S

    Microsoft. USRoadDetections: U.S. Road Network Detection Dataset. https://github.com/microsoft/USRoadDetections, 2020. Accessed: 2025- 12-11

    work page 2020

  47. [47]

    Ntsg landsat gross primary production (gpp) v2

    Numerical Terradynamic Simulation Group (NTSG). Ntsg landsat gross primary production (gpp) v2. https://developers.google.com/earth-engine/ datasets/catalog/UMT_NTSG_v2_LANDSAT_GPP, 2021. Accessed: 2025-12-16

    work page 2021

  48. [48]

    Landland- cov_baselc2022.tif, 2024

    University of Vermont Spatial Analysis Laboratory. Landland- cov_baselc2022.tif, 2024

    work page 2024

  49. [49]

    Advancing image super-resolution techniques in remote sensing: A comprehensive survey.ISPRS Journal of Photogrammetry and Remote Sensing, 231:68–100, 2026

    Yunliang Qi, Meng Lou, Yimin Liu, Lu Li, Zhen Yang, and Wen Nie. Advancing image super-resolution techniques in remote sensing: A comprehensive survey.ISPRS Journal of Photogrammetry and Remote Sensing, 231:68–100, 2026

    work page 2026

  50. [50]

    Cfat: Un- leashing triangular windows for image super-resolution

    Abhisek Ray, Gaurav Kumar, and Maheshkumar H Kolekar. Cfat: Un- leashing triangular windows for image super-resolution. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 26120–26129, 2024

    work page 2024

  51. [51]

    Lavista Ferres, and Peyman Najafirad

    Caleb Robinson, Isaac Corley, Anthony Ortiz, Rahul Dodhia, Juan M. Lavista Ferres, and Peyman Najafirad. Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery, 2024

    work page 2024

  52. [52]

    Image super-resolution via iterative refinement.IEEE transactions on pattern analysis and machine intelligence, 45(4):4713–4726, 2022

    Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. Image super-resolution via iterative refinement.IEEE transactions on pattern analysis and machine intelligence, 45(4):4713–4726, 2022

    work page 2022

  53. [53]

    Rethinking image evaluation in super- resolution.arXiv preprint arXiv:2503.13074, 2025

    Shaolin Su, Josep M Rocafort, Danna Xue, David Serrano-Lozano, Lei Sun, and Javier Vazquez-Corral. Rethinking image evaluation in super- resolution.arXiv preprint arXiv:2503.13074, 2025

    work page arXiv 2025

  54. [54]

    Deriving high spatiotemporal remote sensing images using deep convolutional network

    Zhenyu Tan, Peng Yue, Liping Di, and Junmei Tang. Deriving high spatiotemporal remote sensing images using deep convolutional network. Remote Sensing, 10(7):1066, 2018

    work page 2018

  55. [55]

    Computer generated building footprints for the united states, 2018

    Bing Maps Team. Computer generated building footprints for the united states, 2018

    work page 2018

  56. [56]

    Geological Survey

    U.S. Geological Survey. Landsat dynamic surface water extent (dswe) science products. https://www.usgs.gov/landsat-missions/ landsat-dynamic-surface-water-extent-science-products, 2022. Accessed: 2025-12-16

    work page 2022

  57. [57]

    Naip seam lines by state

    USDA Farm Service Agency Aerial Photography Field Office. Naip seam lines by state. https://gdg.sc.egov.usda.gov/Catalog/ProductDescription/ NAIPSL.html, 2022. Accessed: 2025-12-11

    work page 2022

  58. [58]

    Cropland data layer

    USDA National Agricultural Statistics Service. Cropland data layer. https://nassgeodata.gmu.edu/CropScape/, 2021. Published crop-specific data layer. Accessed: 12/16/2025

    work page 2021

  59. [59]

    A cnn-based sentinel- 2 image super-resolution method using multiobjective training.IEEE Transactions on Geoscience and Remote Sensing, 61:1–14, 2023

    Vlad Vasilescu, Mihai Datcu, and Daniela Faur. A cnn-based sentinel- 2 image super-resolution method using multiobjective training.IEEE Transactions on Geoscience and Remote Sensing, 61:1–14, 2023

    work page 2023

  60. [60]

    Towards real-world remote sensing image super- resolution: A new benchmark and an efficient model.IEEE Transactions on Geoscience and Remote Sensing, 2024

    Jia Wang, Liuyu Xiang, Lei Liu, Jiaochong Xu, Peipei Li, Qizhi Xu, and Zhaofeng He. Towards real-world remote sensing image super- resolution: A new benchmark and an efficient model.IEEE Transactions on Geoscience and Remote Sensing, 2024

    work page 2024

  61. [61]

    Virtual image pair-based spatio-temporal fusion.Remote Sensing of Environment, 249:112009, 2020

    Qunming Wang, Yijie Tang, Xiaohua Tong, and Peter M Atkinson. Virtual image pair-based spatio-temporal fusion.Remote Sensing of Environment, 249:112009, 2020

    work page 2020

  62. [62]

    Real-esrgan: Training real-world blind super-resolution with pure synthetic data

    Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan. Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1905–1914, 2021

    work page 1905

  63. [63]

    Esrgan: Enhanced super-resolution generative adversarial networks

    Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, and Chen Change Loy. Esrgan: Enhanced super-resolution generative adversarial networks. InProceedings of the European conference on computer vision (ECCV) workshops, pages 0–0, 2018

    work page 2018

  64. [64]

    attention

    Yan Wang, Yi Liu, Shijie Zhao, Junlin Li, and Li Zhang. Camixersr: Only details need more" attention". InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 25837– 25846, 2024

    work page 2024

  65. [65]

    Treefinder: A us-scale benchmark dataset for individual tree mortality monitoring using high- resolution aerial imagery

    Zhihao Wang, Cooper Li, Ruichen Wang, Lei Ma, George Hurtt, Xiaowei Jia, Gengchen Mai, Zhili Li, and Yiqun Xie. Treefinder: A us-scale benchmark dataset for individual tree mortality monitoring using high- resolution aerial imagery. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track

    work page

  66. [66]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004

    work page 2004

  67. [67]

    Super-resolution neural operator

    Min Wei and Xuesong Zhang. Super-resolution neural operator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18247–18256, 2023

    work page 2023

  68. [68]

    Zooming out on zooming in: Advancing super-resolution for remote sensing.arXiv:2311.18082, 2023

    Piper Wolters, Favyen Bastani, and Aniruddha Kembhavi. Zooming out on zooming in: Advancing super-resolution for remote sensing.arXiv preprint arXiv:2311.18082, 2023

    work page arXiv 2023

  69. [69]

    Free-flowing rivers - wwf hydrosheds v1

    WWF HydroSHEDS. Free-flowing rivers - wwf hydrosheds v1. https://developers.google.com/earth-engine/datasets/catalog/WWF_ HydroSHEDS_v1_FreeFlowingRivers, 2000. Accessed: 2025-12-16

    work page 2000

  70. [70]

    Ediffsr: An efficient diffusion probabilistic model for remote sensing image super-resolution.IEEE Transactions on Geoscience and Remote Sensing, 62:1–14, 2023

    Yi Xiao, Qiangqiang Yuan, Kui Jiang, Jiang He, Xianyu Jin, and Liangpei Zhang. Ediffsr: An efficient diffusion probabilistic model for remote sensing image super-resolution.IEEE Transactions on Geoscience and Remote Sensing, 62:1–14, 2023

    work page 2023

  71. [71]

    Segformer: Simple and efficient design for semantic segmentation with transformers.Advances in neural information processing systems, 34:12077–12090, 2021

    Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers.Advances in neural information processing systems, 34:12077–12090, 2021

    work page 2021

  72. [72]

    Neurop-diff: Continuous remote sensing image super-resolution via neural operator diffusion.arXiv preprint arXiv:2501.09054, 2025

    Zihao Xu, Yuzhi Tang, Bowen Xu, and Qingquan Li. Neurop-diff: Continuous remote sensing image super-resolution via neural operator diffusion.arXiv preprint arXiv:2501.09054, 2025

    work page arXiv 2025

  73. [73]

    Learning texture transformer network for image super-resolution

    Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. Learning texture transformer network for image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5791–5800, 2020

    work page 2020

  74. [74]

    On single image scale- up using sparse-representations

    Roman Zeyde, Michael Elad, and Matan Protter. On single image scale- up using sparse-representations. InInternational conference on curves and surfaces, pages 711–730. Springer, 2010

    work page 2010

  75. [75]

    Transcending the limit of local window: Advanced super-resolution transformer with adaptive token dictionary

    Leheng Zhang, Yawei Li, Xingyu Zhou, Xiaorui Zhao, and Shuhang Gu. Transcending the limit of local window: Advanced super-resolution transformer with adaptive token dictionary. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2856–2865, 2024

    work page 2024

  76. [76]

    Uncertainty- guided perturbation for image super-resolution diffusion model

    Leheng Zhang, Weiyi You, Kexuan Shi, and Shuhang Gu. Uncertainty- guided perturbation for image super-resolution diffusion model. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 17980–17989, 2025

    work page 2025

  77. [77]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. InProceedings of the IEEE/CVF international conference on computer vision, pages 3836– 3847, 2023

    work page 2023

  78. [78]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 586–595, 2018

    work page 2018

  79. [79]

    Image super-resolution using very deep residual channel attention networks

    Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. InProceedings of the European conference on computer vision (ECCV), pages 286–301, 2018

    work page 2018

  80. [80]

    Single-image super-resolution based on rational fractal inter- polation.IEEE Transactions on Image Processing, 27(8):3782–3797, 2018

    Yunfeng Zhang, Qinglan Fan, Fangxun Bao, Yifang Liu, and Caiming Zhang. Single-image super-resolution based on rational fractal inter- polation.IEEE Transactions on Image Processing, 27(8):3782–3797, 2018

    work page 2018

Showing first 80 references.