Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies

Andreas Weinmann; Damjan Hatic; Markus Rauhut; Ronald R\"osch; Thomas M\"arz; Vladyslav Polushko

REVIEW 4 major objections 4 minor 29 references

Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The paper claims that among nine augmentation families tested for flood-water segmentation on the BlessemFlood21 river-flood dataset, geometric distortion and blur/noise augmentations give the best results, color transforms the worst, and…

desk verdict A clean but thin empirical scan: useful augmentation ranking on BlessemFlood21, weakened by single runs and one telling outlier. read the letter →

arxiv 2504.20203 v1 pith:L4JFSZNY submitted 2025-04-28 cs.CV eess.IV

Vladyslav Polushko , Damjan Hatic , Ronald Rösch , Thomas März , Markus Rauhut , Andreas Weinmann This is my paper · ORCID

classification cs.CVeess.IV

keywords semanticsegmentationflooddetectionremotesensingimageaugmentationdataUNet++DeepLabV3+BlessemFlood21

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks whether the choice of image augmentation meaningfully changes how well deep segmentation models detect flood water in overhead imagery. By sorting augmentations into nine families—basic flips and crops, blur and noise, color shifts, dropout, geometric distortion, pixel-level changes, quality degradation, sophisticated color adjustments, and simulated weather—and training UNet++ and DeepLabV3+ on the BlessemFlood21 river-flood dataset, it finds that geometric distortion and blur-and-noise families help most, while color-only augmentation helps least. The largest spread across families is about 2 percent IoU, so the effect is real but modest. Training for 300 epochs instead of 100 generally raises scores and narrows the gap between augmentation families. A sympathetic reader would take this as guidance for configuring flood-segmentation training rather than as a dramatic accuracy breakthrough.

What carries the argument

The central machinery is a taxonomy of augmentations plus a controlled comparison protocol: each of the nine families is applied one at a time while training two standard segmentation networks, UNet++ and DeepLabV3+, for 100, 200, and 300 epochs, with the Basic family as a benchmark and IoU, Dice, and Accuracy as metrics. The load-bearing tool is an augmentation library whose default parameters set the probabilities—50% per transform, except 30% for blur—and the modifications include splitting shift-scale-rotate into separate operations with a full 360-degree rotation range. This setup is what lets the paper attribute performance differences to the augmentation families rather than to other training variables.

What would settle it

Run each of the nine augmentation groups across multiple random seeds with the same hyperparameters; if the 1-2% IoU ordering between Distortion/Blur-and-Noise and Color does not hold across seeds, the ranking is a single-run artifact rather than a property of the augmentations.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central finding is empirical: under a controlled protocol where each of nine augmentation families is applied alone to the BlessemFlood21 dataset, the Distortion group—optical barrel and pincushion warping, elastic deformation, grid distortion, perspective shifts—delivers the highest IoU and Dice for UNet++ (90.56% IoU at 300 epochs), while the Blur and Noise group is the best for DeepLabV3+ (88.75% IoU). The Color group, built from jitter, gamma changes, grayscale, solarization, inversion, RGB shifting, and equalization, is the worst for both models yet still stays above 82-83% IoU. The paper also claims that extending training from 100 to 300 epochs improves all groups and reduces the performance spread among them. These claims are framed as findings on a single high-resolution river-flood dataset, not as a general theory of augmentation.

Load-bearing premise

The claim rests on single training runs for each augmentation group with hand-picked, mostly 50% application probabilities and no random-seed repetition; if those runs are atypical, the observed ranking could be coincidence.

Editorial extensions

If this is right

Practitioners training flood segmentation on similar RGB imagery should expect geometric-distortion and blur/noise augmentations to give the best accuracy among single-group choices.
Extending training from 100 to 300 epochs should improve IoU, Dice, and accuracy for every augmentation family, making longer schedules a low-risk investment.
Color-only augmentation is the weakest family but is not harmful enough to abandon; it stays above 82% IoU on both models.
The gap between best and worst augmentation family is about 2% IoU, so augmentation choice is a secondary tuning lever compared with architecture and training length.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the single-run results are taken at face value, combining Distortion with Blur-and-Noise is the most promising next experiment, since the paper leaves group combinations unexplored.
The Color group's collapse to 10.68 IoU for DeepLabV3+ at 100 epochs hints that short schedules amplify bad interactions with aggressive color transforms; testing lower probabilities or longer warm-ups might change the ranking.
The ranking may not be a stable property of the augmentations themselves: without seed repetition, the reported 1-2% margins are within the range that random training variation could flip, so a multi-seed replication is the natural falsifier.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a straightforward empirical scan—nine augmentation groups from Albumentations, two pretrained segmentation models, three training lengths, one flood dataset. There is no new augmentation method and no new theory; the value is in the structured grouping and the up-to-date dataset. As a benchmark it is useful for people training flood segmentation models, and the broad conclusion (distortion and blur/noise help most, color least) is plausible. But the evidence as presented is only moderately convincing, and the paper needs revision before I'd rely on the ranking.

What's good: The grouping is sensible and clearly described. Evaluating on BlessemFlood21 rather than the usual FloodNet adds a useful data point. The 100/200/300 epoch comparison is also reasonable, and the tables make the broad pattern easy to read. Credit where due: this is honest, unpretentious engineering work. The fact that the dataset is co-authored by the same group is not a problem here; the augmentation-ranking comparison is not hardwired by dataset construction.

Where it's soft: First, every number is a single training run. With stochastic augmentation at 50% probability, one seed is not enough to support the headline ranking, especially when the 300-epoch differences between top groups and the Basic group are around 1–2 IoU points. Second, there is no no-augmentation baseline, so "contribute positively" is asserted but not demonstrated. Third, the tables contain at least one red flag: DeepLabV3+ shows Color at 10.68% IoU at 100 epochs while everything else is in the 70s–80s, then it recovers to 83% at 200 epochs. That looks like a training failure or a bad run, not a stable property of the augmentation group. The text says "deviation peaks at 2%" and "IoU above 82%," which directly contradicts the table; that would need to be resolved or the result removed. The 100-epoch Quality/Pixel numbers also look noisy. Fourth, no code, seeds, or configs are given, so the scan isn't reproducible as-is.

On balance: the paper's central argument—that augmentation choice matters a little and certain groups are better than others—probably holds in broad brush. But the specific ranking is less solid than the prose claims. This is a paper for a workshop or a short application-focused venue, not a methods contribution. It deserves a serious referee if it is cleaned up, but I would not accept it in its current form.

Recommendation: send it to peer review with "revise and resubmit" in mind—require at least repeated runs, a no-augmentation control, and a correction or explanation of the Color outlier. If it's for a top conference, it is too thin; if it's for a remote-sensing workshop or an applied journal, it's a legitimate small contribution.

Referee Report

4 major / 4 minor

Summary. The paper investigates the influence of nine groups of image augmentations (Basic, Blur and Noise, Color, Drop, Distortion, Pixel, Quality, Sophisticated, Weather) on flood-water semantic segmentation. Using the BlessemFlood21 dataset and two pretrained segmentation models (UNet++ and DeepLabV3+), the authors report IoU, Dice, and Accuracy at 100, 200, and 300 training epochs. The central claims are that the Distortion and Blur and Noise augmentation groups yield the best performance, that the Color group tends to lag behind, and that longer training generally improves performance.

Significance. If the reported ranking were statistically robust, the paper would offer useful practical guidance for augmentation selection in flood segmentation. The systematic categorization of augmentations and the evaluation across two architectures and three training durations on a publicly available dataset are a reasonable reference setup, and the paper is honest about the small overall effect sizes (1–2% IoU). However, the evidentiary basis for the central claims is currently weak: all results come from single training runs with no variance estimates, no significance testing, and no no-augmentation baseline, and one reported entry (DeepLabV3+ Color at 100 epochs) is a 70-point outlier that contradicts the paper's own prose. The practical significance is modest, and the conclusions therefore require additional experimental support rather than merely editorial changes.

major comments (4)

[Result and Discussion / Table 2] Table 2 reports DeepLabV3+ with the Color augmentation group at 100 epochs as IoU=10.68%, Dice=19.3%, Accuracy=97.59%, a 70-point drop relative to all other configurations. The text in the same section claims 'IoU achieving more than 83%' and 'deviation peaks at 2% for IoU', which is directly contradicted by this entry. Because the conclusion that Color 'tends to lag behind' relies on tables containing this unreplicated outlier, the authors must either explain this run (e.g., a training failure, a broken augmentation recipe, or an initialization issue) and exclude it with justification, or re-run the experiment and report the corrected value. As written, this single number undermines the internal consistency of the paper.
[Result and Discussion / Tables 1 and 2] All reported results are from single training runs; no random seeds, no repeated runs, and no variance estimates are provided. The central ranking claim is based on differences of roughly 1–2 IoU points at 300 epochs (e.g., Table 1: Distortion 90.56 vs Basic 88.89 for UNet++; Table 2: Blur and Noise 88.75 vs Basic 88.69 for DeepLabV3+). For stochastic augmentations applied with default probabilities (50%, except 30% for blur), such small gaps are well within plausible run-to-run variance. The paper should present means and standard deviations over at least 3–5 seeds, or equivalently perform significance tests, before claiming that one augmentation group 'consistently' outperforms another.
[Setup / Result and Discussion] There is no no-augmentation baseline. The comparison is always against the 'Basic' augmentation group, not against training without any augmentation. Consequently, the conclusion that 'all the augmentation groups contribute positively to some extent' and the broader claim that augmentation benefits flood segmentation are not actually evaluated. The authors should include a baseline trained with no augmentations (or at least with only the essential resizing/cropping operations needed to run the models) to support these claims.
[Result and Discussion / Epoch Influence] The claim that 'longer training periods generally improve model performance' is not consistently supported by the tables. In Table 1, performance decreases from 200 to 300 epochs for Basic (89.11 to 88.89), Color (84.22 to 83.61), Pixel (87.23 to 85.86), Quality (89.32 to 89.00), and Sophisticated (88.92 to 88.72); Weather stays flat. Similar non-monotonic behavior appears in Table 2 (e.g., Drop 86.72 at 200 vs 88.44 at 300, but Color 83.45 at 200 vs 82.88 at 300). The authors should either qualify the statement to reflect the observed non-monotonicity or provide an analysis of when and why longer training helps.

minor comments (4)

[Abstract / Introduction] The phrase 'state-of-the-art' is used repeatedly but no comparison with prior state-of-the-art segmentation results on BlessemFlood21 on which to base the claim is given. Please clarify whether 'state-of-the-art' refers to the architectures themselves (which is fine) or to the obtained performance.
[Augmentations / Parameters] The choice of default Albumentations parameters is described, but the specific probability settings for each augmentation within a group (except blur at 30% and the rest at 50%) are not tabulated. Since probability is a free parameter that can affect the ranking, a table listing per-augmentation probabilities would improve reproducibility.
[References] Several references contain typesetting or punctuation errors, for example [3] and [5] have an erroneous extra comma after the author list, and 'R. R¨osch' in [16] is inconsistent with 'Ronald R¨osch' in the author list. Please proofread the bibliography.
[Result and Discussion] The phrase 'the Color augmentation group registers the lowest scores across all metrics for both models' is stated in the text, but Table 1 shows at 300 epochs the Pixel group (85.86 IoU) is lower than Color (83.61? No, Color is lowest at 83.61, Pixel is 85.86, so this is correct). However, the text also says 'the deviations remain relatively insignificant' which is hard to reconcile with the 10.68% IoU entry in Table 2; the prose should be updated consistently after the outlier is addressed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the augmentation ranking is an empirical measurement, not a derivation from its inputs.

full rationale

This paper is an empirical benchmark study, not a derivation. It defines nine augmentation groups and reports IoU, Dice, and accuracy for two segmentation models after training on a fixed dataset. The central claims—that Distortion and Blur/Noise augmentations perform best, that Color tends to lag, and that longer training helps—are direct readings of Tables 1 and 2, which are experimental outputs rather than consequences of the augmentation definitions. The only self-citation is reference [16], the BlessemFlood21 dataset, which is co-authored by several of the present authors; however, that dataset is used as a fixed evaluation substrate for all augmentation groups, and the augmentation ranking is not hardwired into the dataset construction or into any fitted parameter. No equation in the paper reduces a prediction to an input, no fitted constant is renamed as a result, and no uniqueness or ansatz is imported from prior work. Concerns about missing seed repetition, single runs, and the anomalous 10.68% IoU for DeepLabV3+ with Color at 100 epochs pertain to experimental robustness and statistical support, not circularity. These are legitimate correctness risks but do not make the argument self-referential. Accordingly, the paper's comparison is self-contained and empirically independent of its inputs.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central comparison rests on hand-set augmentation probabilities, a single dataset split, and unstated mask-handling details. These are not fitted to the reported outcome, but they are not varied either, so the ranking may not generalize.

free parameters (3)

Augmentation application probability = 0.5 default; 0.3 for blur group
Hand-set defaults from Albumentations; controls how often each transformation is applied and could favor some groups.
Basic group shift/scale range and rotation = shift/scale 0.3; full 360 degree rotation
Manual modification of default parameters; directly affects the Basic group benchmark.
Training duration = 100, 200, 300 epochs
Discrete experimental variable; the claim that longer training helps depends on these chosen points.

assumptions (3)

domain assumption Augmentation transforms are applied consistently to both image and segmentation mask.
Required for valid semantic segmentation training; the paper never states how masks are transformed, only that augmentations are applied.
domain assumption The 80/10/10 split of BlessemFlood21, with 10% of water-containing images held out for validation and test, is representative.
Single split; no cross-validation or repeated splits, so split artifacts could influence small differences.
ad hoc to paper Default Albumentations parameters are a neutral setting for comparing augmentation groups.
Parameters were chosen by convention or light modification, not swept; rankings may be parameter-specific.

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Cite this review

Pith. "Pith review of Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies." pith.science (2026). https://pith.science/paper/L4JFSZNY

@misc{pith2026250420203,
  author       = {Pith},
  title        = {Pith review of: Remote Sensing Imagery for Flood Detection: Exploration of Augmentation Strategies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L4JFSZNY}},
  note         = {Machine review of arXiv:2504.20203}
}

read the original abstract

Floods cause serious problems around the world. Responding quickly and effectively requires accurate and timely information about the affected areas. The effective use of Remote Sensing images for accurate flood detection requires specific detection methods. Typically, Deep Neural Networks are employed, which are trained on specific datasets. For the purpose of river flood detection in RGB imagery, we use the BlessemFlood21 dataset. We here explore the use of different augmentation strategies, ranging from basic approaches to more complex techniques, including optical distortion. By identifying effective strategies, we aim to refine the training process of state-of-the-art Deep Learning segmentation networks.

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Reference graph

Works this paper leans on

29 extracted references · 27 canonical work pages

[1]

Ritchie and P

H. Ritchie and P . Rosado, Natural disasters, Our World in Data, 2022, https: //ourworldindata.org/natural- disasters (accessed June 22, 2024) ( 2022)

work page 2022
[2]

Arias, N

P . Arias, N. Bellouin, E. Coppola, R. Jones, Krinner, Ger hard, and J. Marotzke et al., “2021: Technical Summary. In Climate Change 2021: The Physical Science Basis . Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on C limate Change,” Tech. Rep. (IPPC, United Kingdom and New Y ork, 2021) https://www.ipcc.ch/...

work page 2021
[3]

Deep convolutional neural network for ﬂood extent mapping using unmanned aeria l vehicles data,

A. Gebrehiwot, L. Hashemi-Beni, G. Thompson, P . Kordjam shidi, and T. E. Langan “Deep convolutional neural network for ﬂood extent mapping using unmanned aeria l vehicles data,” , Sensors 19, p. 1486 (2019)

work page 2019
[4]

UNICEF Malawi, Summative Evaluation of the Impact of Usi ng Drones on Population Health & Other Outcomes, https://www.unicef.org/malawi/reports/summative-eva luation-impact-using -drones-population-health-other-outcomes ( 2021), accessed: 2023-07-07

work page 2021
[5]

The u se of unmanned aerial vehicles in ﬂood hazard assessment,

E. Karamuz, R. J. Romanowicz, and J. Doroszkiewicz “The u se of unmanned aerial vehicles in ﬂood hazard assessment,” , Journal of Flood Risk Management 13, p. e12622 (2020)

work page 2020
[6]

Applica tion of deep learning on uav-based aerial images for ﬂood detection,

H. S. Munawar, F. Ullah, S. Qayyum, and A. Heravi “Applica tion of deep learning on uav-based aerial images for ﬂood detection,” , Smart Cities 4, 1220–1242 (2021)

work page 2021
[7]

Automated indunati on mapping: comparison of methods,

A. Gebrehiwot and L. Hashemi-Beni, “Automated indunati on mapping: comparison of methods,” in IGARSS 2020 IEEE International Geoscience and Remote Sensing Symp osium (2020), pp. 3265–3268

work page 2020
[8]

A Real-Time Flood Detection System Based on Machine Learning Algorithms with Emphasis on Deep L earning,

A. Hashi, A. Abdirahman, M. Elmi, S. Hashi, and O. Rodrigu ez “A Real-Time Flood Detection System Based on Machine Learning Algorithms with Emphasis on Deep L earning,” , International Journal of Engi- neering Trends and Technology 69, 249–25605 (2021)

work page 2021

Show all 29 references

[9]

Artiﬁcal Intelligence (AI) Appli ed to Unmanned Aerial V ehicles (UA Vs) And its Impact on Humanitarian Action,

C. Oren and A. V erity, “Artiﬁcal Intelligence (AI) Appli ed to Unmanned Aerial V ehicles (UA Vs) And its Impact on Humanitarian Action,” Tech. Rep. (UN-OCHA, 2020)

2020
[10]

Image segmentation using deep learning: A survey,

S. Minaee, Y . Boykov, F. Porikli, A. Plaza, N. Kehtarnav az, and D. Terzopoulos “Image segmentation using deep learning: A survey,” , IEEE transactions on pattern ana lysis and machine intelligence 44, 3523–3542 (2021)

2021
[11]

Deep learning-based ﬂood detection system using semantic segmentation,

D. V . Chandran, J. Anitha, A. Anusree, and G. Nair, “Deep learning-based ﬂood detection system using semantic segmentation,” in 2024 7th International Conference on Circuit Power and Computing T echnologies (ICCPCT), V ol. 1 (IEEE, 2024), pp. 1584–1592

2024
[12]

The use of the normalized di ﬀerence water index (ndwi) in the delineation of open water features,

S. K. McFeeters “The use of the normalized di ﬀerence water index (ndwi) in the delineation of open water features,” , International journal of remote sensing 17, 1425–1432 (1996)

1996
[13]

Deep learning methods for ﬂood mapping: A review of existing applications and future research direct ions,

R. Bentivoglio, E. Isuﬁ, S. N. Jonkman, and R. Taormina “ Deep learning methods for ﬂood mapping: A review of existing applications and future research direct ions,” , Hydrology and Earth System Sciences Dis- cussions 2021, 1–43 (2021)

2021
[14]

Flood extent mapp ing: an integrated method using deep learning and region growing using UA V optical data,

L. Hashemi-Beni and A. A. Gebrehiwot “Flood extent mapp ing: an integrated method using deep learning and region growing using UA V optical data,” , IEEE Journal ofSelected Topics in Applied Earth Observations and Remote Sensing 14, 2127–2135 (2021)

2021
[15]

Floodnet: A high resolution aerial imagery dataset for post ﬂood scene under standing,

M. Rahnemoonfar, T. Chowdhury, A. Sarkar, D. V arshney, M. Y ari, and R. R. Murphy “Floodnet: A high resolution aerial imagery dataset for post ﬂood scene under standing,” , IEEE Access 9, 89644–89654 (2021)

2021
[16]

Blessemﬂood21: Advancing ﬂood analysis with a high-resol ution georeferenced dataset for humanitarian aid support,

V . Polushko, A. Jenal, J. Bongartz, I. Weber, D. Hatic, R . R¨ osch, T. M¨ arz, M. Rauhut, and A. Weinmann, “Blessemﬂood21: Advancing ﬂood analysis with a high-resol ution georeferenced dataset for humanitarian aid support,” in IEEE International Geoscience and Remote Sensing...

2024
[17]

Im age data augmentation for deep learning: A survey,

S. Y ang, W . Xiao, M. Zhang, S. Guo, J. Zhao, and F. Shen “Im age data augmentation for deep learning: A survey,” , arXiv preprint arXiv:2204.08610 (2022)

2022 arXiv
[18]

Evaluating self and semi-supervised methods for remote sensing segmentation tasks,

C. Patel, S. Sharma, and V . Gulshan “Evaluating self and semi-supervised methods for remote sensing segmentation tasks,” , arXiv preprint arXiv:2111.10079 (2 021)

arXiv
[19]

Selective data au gmentation approach for remote sensing scene clas- siﬁcation,

R. Alharbi, H. Alhichri, and Y . Bazi, “Selective data au gmentation approach for remote sensing scene clas- siﬁcation,” in 2020 2nd International Conference on Computer and Informat ion Sciences (ICCIS) (IEEE, 2020), pp. 1–4

2020
[20]

Automatic detection of passable roads after ﬂoods in remote sensed and social media data,

K. Ahmad, K. Pogorelov, M. Riegler, O. Ostroukhova, P . H alvorsen, N. Conci, and R. Dahyot “Automatic detection of passable roads after ﬂoods in remote sensed and social media data,” , Signal Processing: Image Communication 74, 110–118 (2019)

2019
[21]

Ha-net: A lake water body ex traction network based on hybrid-scale attention and transfer learning,

Z. Wang, X. Gao, and Y . Zhang “Ha-net: A lake water body ex traction network based on hybrid-scale attention and transfer learning,” , Remote Sensing 13, p. 4121 (2021)

2021
[22]

Deep learning semantic segmentation for water level estimation using surveillance camera,

N. A. Muhadi, A. F. Abdullah, S. K. Bejo, M. R. Mahadi, and A. Mijic “Deep learning semantic segmentation for water level estimation using surveillance camera,” , Ap plied Sciences 11, p. 9691 (2021)

2021
[23]

Urban ﬂood mappin g with residual patch similarity learning,

B. Peng, X. Liu, Z. Meng, and Q. Huang, “Urban ﬂood mappin g with residual patch similarity learning,” in Proceedings of the 3rd ACM SIGSPATIAL International W orkshop on AI for Geographic Knowledge Dis- covery (2019), pp. 40–47

2019
[24]

Y . Bai, W . Wu, Z. Y ang, J. Y u, B. Zhao, X. Liu, H. Y ang, E. Ma s, and S. Koshimura “Enhancement of detecting permanent water and temporary water in ﬂood disas ters by fusing sentinel-1 and sentinel-2 imagery using deep learning algorithms: Demonstration of sen1ﬂood s11 be...

2021
[25]

Unet ++: A nested u-net architecture for medical image segmentation,

Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Lia ng, “Unet ++: A nested u-net architecture for medical image segmentation,” in Deep Learning in Medical Image Analysis and Multimodal Lear ning for Clinical Decision Support: 4th International W orkshop, an d 8th Interna...

2018
[26]

Encoder-decoder with atrous separable convolution for semantic image segmentation,

L.-C. Chen, Y . Zhu, G. Papandreou, F. Schro ﬀ, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European conference on computer vision (ECCV) (2018), pp. 801–818

2018
[27]

Albumentations: fast and ﬂexible image augmentations,

A. Buslaev, V . I. Iglovikov, E. Khvedchenya, A. Parinov , M. Druzhi-nin, and A. A. Kalinin “Albumentations: fast and ﬂexible image augmentations,” , Information 11, p. 125 (2020)

2020
[28]

Data augmentation in c lassiﬁcation and segmentation: A survey and new strategies,

K. Alomar, H. I. A ysel, and X. Cai “Data augmentation in c lassiﬁcation and segmentation: A survey and new strategies,” , Journal of Imaging 9, p. 46 (2023)

2023
[29]

Imagenet: A large-scale hierarchical image database,

J. Deng, W . Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fe i, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recogni tion (Ieee, 2009), pp. 248–255

2009

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Ritchie and P

H. Ritchie and P . Rosado, Natural disasters, Our World in Data, 2022, https: //ourworldindata.org/natural- disasters (accessed June 22, 2024) ( 2022)

work page 2022

[2] [2]

Arias, N

P . Arias, N. Bellouin, E. Coppola, R. Jones, Krinner, Ger hard, and J. Marotzke et al., “2021: Technical Summary. In Climate Change 2021: The Physical Science Basis . Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on C limate Change,” Tech. Rep. (IPPC, United Kingdom and New Y ork, 2021) https://www.ipcc.ch/...

work page 2021

[3] [3]

Deep convolutional neural network for ﬂood extent mapping using unmanned aeria l vehicles data,

A. Gebrehiwot, L. Hashemi-Beni, G. Thompson, P . Kordjam shidi, and T. E. Langan “Deep convolutional neural network for ﬂood extent mapping using unmanned aeria l vehicles data,” , Sensors 19, p. 1486 (2019)

work page 2019

[4] [4]

UNICEF Malawi, Summative Evaluation of the Impact of Usi ng Drones on Population Health & Other Outcomes, https://www.unicef.org/malawi/reports/summative-eva luation-impact-using -drones-population-health-other-outcomes ( 2021), accessed: 2023-07-07

work page 2021

[5] [5]

The u se of unmanned aerial vehicles in ﬂood hazard assessment,

E. Karamuz, R. J. Romanowicz, and J. Doroszkiewicz “The u se of unmanned aerial vehicles in ﬂood hazard assessment,” , Journal of Flood Risk Management 13, p. e12622 (2020)

work page 2020

[6] [6]

Applica tion of deep learning on uav-based aerial images for ﬂood detection,

H. S. Munawar, F. Ullah, S. Qayyum, and A. Heravi “Applica tion of deep learning on uav-based aerial images for ﬂood detection,” , Smart Cities 4, 1220–1242 (2021)

work page 2021

[7] [7]

Automated indunati on mapping: comparison of methods,

A. Gebrehiwot and L. Hashemi-Beni, “Automated indunati on mapping: comparison of methods,” in IGARSS 2020 IEEE International Geoscience and Remote Sensing Symp osium (2020), pp. 3265–3268

work page 2020

[8] [8]

A Real-Time Flood Detection System Based on Machine Learning Algorithms with Emphasis on Deep L earning,

A. Hashi, A. Abdirahman, M. Elmi, S. Hashi, and O. Rodrigu ez “A Real-Time Flood Detection System Based on Machine Learning Algorithms with Emphasis on Deep L earning,” , International Journal of Engi- neering Trends and Technology 69, 249–25605 (2021)

work page 2021