Progressive Cross Attention Network for Flood Segmentation using Multispectral Satellite Imagery

Alex M. Lechner; Bahrul I. Nasution; Fithrothul Khikmah; Irfan Dwiki Bhaswara; Muhamad Risqi U. Saputra; Vicky Feliren

REVIEW 4 major objections 5 minor 18 references

Progressive Cross Attention Network for Flood Segmentation using Multispectral Satellite Imagery

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read A flood-segmentation network that progressively fuses RGB and near-infrared features with self- and cross-attention reports IoU of 0.815 on the Sen1Floods11 test set.

desk verdict ProCANet is a sensible attention-based fusion idea with a plausible but unproven empirical claim; the split ambiguity and single-run numbers prevent me from trusting the headline IoU. read the letter →

arxiv 2501.11923 v1 pith:PE4NOX2E submitted 2025-01-21 cs.CV cs.LG

Vicky Feliren , Fithrothul Khikmah , Irfan Dwiki Bhaswara , Bahrul I. Nasution , Alex M. Lechner , Muhamad Risqi U. Saputra This is my paper · ORCID

classification cs.CVcs.LG

keywords floodsegmentationmultispectralsatelliteimagerycrossattentionself-attentionsemanticnear-infraredSen1Floods11remotesensing

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 sets out to establish that ProCANet, a segmentation model which progressively applies self-attention and cross-attention to multispectral satellite features, outperforms seven baselines on Sen1Floods11 with an IoU of 0.815, an F1 score of 0.8982, and an accuracy of 0.9811. The motivation is that naively concatenating spectral bands ignores complementary and contrastive information between modalities, while attention lets each band group filter itself and then modulate the other before fusion. The paper also claims the model generalizes without retraining to 5 m PlanetScope imagery of the Citarum River basin, reaching an IoU of 0.659 against a manually corrected NDWI pseudo ground truth. If these results hold, attention-based fusion of RGB and near-infrared data is a practical route to more accurate flood mapping from publicly available satellite imagery.

What carries the argument

The load-bearing component is the progressive cross-attention block, inserted after each pooling operation in a UNet-like dual-encoder architecture. For features $\mathbf{X}_R^i$ from the RGB+NIR encoder and $\mathbf{X}_N^i$ from the NIR encoder, the block computes self-attention masks $\mathbf{a}_R^i = \sigma(\mathbf{W}_R^i(\mathbf{X}_R^i))$ and $\mathbf{a}_N^i$, multiplies them back element-wise to obtain $\hat{\mathbf{X}}_R^i$ and $\hat{\mathbf{X}}_N^i$, then computes cross-attention masks $\mathbf{a}_{R\to N}^i = \sigma(\mathbf{W}_{R\to N}^i(\hat{\mathbf{X}}_R^i))$ and $\mathbf{a}_{N\to R}^i$ to modulate the opposite stream, and finally fuses the results by element-wise addition, $\tilde{\mathbf{X}}_{R+N}^i = \tilde{\mathbf{X}}_R^i \oplus \tilde{\mathbf{X}}_N^i$. Applying this block at every scale is what makes the attention 'progressive'; the mechanism lets the water-sensitive NIR channel re-weight the RGB+NIR stream and vice versa before the decoder reconstructs the flood map.

What would settle it

Re-run the comparison on a properly held-out Sen1Floods11 test split with multiple random seeds and report the mean and standard deviation of IoU; if the 0.815 versus 0.791 gap over UNet falls within one standard deviation, the claimed advantage is not established. Independently, replace the Citarum pseudo ground truth with flood extent from an independent source such as Sentinel-1 SAR or multi-annotator manual labelling; if the 0.659 IoU is mostly agreement with NDWI-like water signals rather than true flood boundaries, the generalization claim is weakened.

Watch

Extended reading notes

Core claim

The paper's central claim is that progressively applying self-attention followed by cross-attention to intermediate features from two encoders—one fed with RGB+NIR and one with NIR alone—produces feature combinations that segment floods more accurately than state-of-the-art models. In the self-attention stage, each modality's features pass through a 3x3 convolution and sigmoid to generate an attention mask, which is multiplied back to amplify relevant and suppress irrelevant content. In the cross-attention stage, each self-attended stream generates a mask that modulates the other stream, and the two modulated streams are merged by element-wise addition at every pooling scale. On Sen1Floods11 this yields IoU 0.815, F1 0.8982, and accuracy 0.9811, above the best baseline UNet at IoU 0.791; the ablation attributes a 0.011 IoU gain to the attention mechanism (0.804 to 0.815) and identifies RGB+NIR with a NIR-only second encoder as the best modality combination.

Load-bearing premise

The paper assumes the comparison models were trained and evaluated fairly on a genuinely held-out test set and that the manually corrected NDWI labels for the Citarum imagery are a valid proxy for true flood extent.

Editorial extensions

If this is right

If the reported results are reproducible, ProCANet provides a consistent accuracy gain for flood segmentation on Sen1Floods11 without changing the backbone or the training data, moving IoU from 0.791 (UNet) to 0.815.
The ablation indicates that the NIR-only second encoder is essential: removing it drops IoU from 0.815 to 0.804, and using RGB alone drops IoU to 0.483, so multispectral input is doing real work.
The claimed cross-resolution result implies a model trained on 10 m Sentinel-2 patches can delineate floods on 5 m PlanetScope imagery without retraining, which would be useful for rapid response when local labels do not exist.
Because the attention block uses only 3x3 convolutions and sigmoids at each scale, the accuracy gain could be added to existing UNet-style flood segmentation pipelines at modest computational cost.
Band-combination experiments suggest that the same architecture can be adjusted by swapping encoder inputs (e.g., RGB+NDWI, or RGB with SAR) with only small changes in performance.

Reading between the lines

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

A direct comparison against a single channel-wise attention module at the bottleneck is not reported, so whether the progressive multi-scale application is the specific source of the gain, rather than attention in general, remains an open question this paper does not settle.
Because the Citarum pseudo ground truth was built partly from NDWI, the IoU of 0.659 may partly reflect agreement with NDWI water signals; independent flood-extent labels would be needed to confirm that the model generalizes to a new region and resolution.
The progressive cross-attention block is a generic fusion operator, so the same design could be tested on other modality pairs such as Sentinel-1 SAR with Sentinel-2 optical, or optical with elevation data, which the paper leaves as future work.

Share X Bluesky LinkedIn Reddit HN

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

The paper's central idea—progressively applying self- and cross-attention at each encoder scale to fuse an RGB+NIR stream with a NIR-only stream—is a reasonable extension of existing attention-based segmentation work. The modality ablation in Table IV is genuinely useful: it shows NIR in the second encoder helps and that RGB alone is far weaker. That kind of systematic input-band comparison is more than many flood-segmentation papers bother to do.

The problem is that the headline results are not demonstrated robustly. Section III-A describes a 65/35 train/validation split, yet Section III-D-1 reports performance on 'Sen1Floods11's test set' without reconciling the two. If the authors used their own random split rather than the official benchmark split, the comparison to published baselines is not apples-to-apples. Even taking the numbers at face value, there are no seeds, no repeated runs, and no error bars. The margin over UNet is 0.024 IoU, which is well within run-to-run variance for 25 epochs. The attention ablation makes this worse: removing attention still gives IoU 0.804, so the proposed mechanism contributes only 0.011—again, noise-level without error bars.

The generalization experiment is honestly labeled as a pseudo ground truth, and the authors explicitly say they cannot validate it. That makes the Citarum IoU a case study, not a validation. It's fine to include, but the wording in the conclusions overstates it.

The baselines are under-specified: all use MobileNetV2, but there is no account of training patches, learning rates, or early stopping for the competitors. So the reader cannot tell if the comparison was fair.

That said, the paper is clearly written and the core idea is plausible. The fixes are straightforward: define the test split against the official benchmark, run multiple seeds and report mean and std, release code, and describe baseline training in enough detail to reproduce. With those changes, the claimed gain might survive, but as submitted the evidence only supports a modest, possibly within-noise improvement.

This deserves peer review because the architecture is sensible and the question matters for operational flood mapping, but it needs major revision before acceptance. I'd bring it to a reading group as an example of common validation pitfalls in applied remote-sensing deep learning.

Referee Report

4 major / 5 minor

Summary. The paper introduces ProCANet, a two-encoder UNet-style segmentation network that progressively applies self-attention and cross-attention to intermediate features from an RGB+NIR encoder and an NIR-only encoder. The method is evaluated on the public Sen1Floods11 dataset, where it reports an IoU of 0.815, F1 of 0.8982, and accuracy of 0.9811 against six baselines, and on PlanetScope 5 m imagery of the Citarum River basin, where it reports IoU of 0.659 against a manually corrected modified-NDWI pseudo-ground-truth label set. The main claims are that the progressive attention fusion produces optimal multispectral feature combinations and that the method is state of the art on Sen1Floods11 while generalizing to higher-resolution imagery.

Significance. If the Sen1Floods11 numbers are obtained on a true geographically disjoint test set with fairly trained baselines, the contribution is a modest but plausible incremental advance: attention-based fusion of RGB+NIR and NIR features inside a U-shaped architecture, supported by an ablation showing attention improving IoU from 0.804 to 0.815. The paper clearly describes the attention block in equations and reports ablations over several modality configurations, which is a strength. However, the significance is currently limited by the absence of statistical rigor: all results are single-run table entries without error bars, the evaluation split is not reconciled with the official Sen1Floods11 benchmark, the generalization reference labels are explicitly unvalidated, and baseline training conditions are not described. The paper does not mention code or model releases, which limits reproducibility.

major comments (4)

[III-A / III-D-1, Table I] Section III-A states that Sen1Floods11 was divided into 65% training and 35% validation, but Section III-D-1 and Table I report results on "Sen1Floods11's test set" without reconciling the two. Sen1Floods11 includes an official benchmark split with geographically disjoint test regions; if a random 65/35 split was used instead, test images may share locations with training images, inflating IoU and making the comparison with published baselines not directly comparable. This is load-bearing because the headline IoU of 0.815 in Table I is the paper's central claim. Please explicitly state the split used; if it is not the official Sen1Floods11 split, rerun the comparison on the official test split and report those results.
[III-C, Table I, Table III] Section III-C says all baselines were trained with MobileNetV2 backbones and RGB+NIR inputs, but it gives no training epochs, optimizer settings, loss functions, patch sizes, or data augmentation for the baselines. Table I is therefore a comparison of ProCANet with under-specified versions of UNet, PSPNet, LinkNet, MANet, PAN, and ConvNeXt V2. Moreover, only one run is reported for each model; the 0.024 IoU margin over UNet (0.815 vs 0.791) may be within run-to-run variance, and the attention ablation in Table III adds only 0.011 IoU (0.804 to 0.815). Please report the mean and standard deviation over at least three seeds for every method and provide a complete training protocol for all baselines.
[III-D-2, Table II, Fig. 3] Section III-D-2 evaluates generalization on the Citarum PlanetScope data against a "modified NDWI (pseudo ground truth)" that the authors explicitly state they are "unable to accurately validate," yet Table II reports an IoU of 0.659 as a quantitative result and Fig. 3's caption calls the same data "our ground truth data." Since ProCANet's second encoder consumes only the NIR band and NDWI is computed from NIR and green bands, agreement with this pseudo-label may partly reflect matching the spectral index rather than detecting true flood extent. Please present this experiment as an illustrative transfer check, or add independent validation labels that do not derive from the same spectral index used in the model's input.
[Table IV] Table IV includes a configuration with "SAR" as the second-encoder input, but Section II-A and Section III-A state that only Sentinel-2 multispectral bands (RGB and NIR) were used. The reported numbers for that row (accuracy 0.972, F1 0.838, IoU 0.722) are identical to the Y. Bai et al. entry in Table I, suggesting it is a baseline replication rather than a ProCANet modality variant. Please clarify whether SAR data were used at all; if not, remove the row and correct the modality comparison in Table IV.

minor comments (5)

[II-D] Section II-D first says original images are cut into non-overlapping 128x128 patches and then says the training data loader used "64 patch steps" to derive more data; please clarify whether the training patches were actually overlapping with stride 64.
[II-D] The BCE loss expression in Section II-D is missing a closing bracket and uses y_pred inconsistently as logits and as probabilities; please align the formula with the implementation.
[I] Reference [4] in the Introduction is described as using "multispectral Sentinel-1 and SAR Sentinel-2," but Sentinel-1 is the SAR sensor and Sentinel-2 is the multispectral sensor; the sensor labels are reversed.
[III-B] Section III-B describes a cosine-annealing scheduler with ten restarts and doubling period lengths while training for only 25 epochs; these numbers are inconsistent and the actual restart schedule should be described precisely.
[Table II] Table II evaluates only UNet and ProCANet on the Citarum data; since generalization is a central claim, please also report the other baselines on the same data or state explicitly why they are omitted.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline IoU is an external Sen1Floods11 benchmark comparison, and the Citarum pseudo-ground-truth issue is an evaluation-validity limitation, not a circular derivation.

full rationale

ProCANet's central claim is an empirical comparison on Sen1Floods11 (Table I), an externally defined benchmark with published baseline numbers; no model parameter is fitted to the test labels and no result is asserted to be identical to an input by construction. The self- and cross-attention blocks in Section II-C are standard learned gating operations on the encoder features, and the final fused feature map is not claimed to equal any input. The best band combination was selected empirically, but this is model selection followed by evaluation, not a fitted parameter renamed as a prediction. The Citarum experiment uses a manually corrected NDWI pseudo ground truth, and the authors explicitly state they are 'unable to accurately validate' those labels; this undermines the generalization claim as a validity or correctness matter, but it is not circular because the model is not trained on the pseudo labels and the IoU metric is not algebraically identical to the model output. The paper's inconsistent description of the Sen1Floods11 split (65/35 validation versus 'test set') and the lack of seeds or error bars are also important evaluation-validity risks, but they do not make the derivation circular. The only self-citation, Lechner et al. [11], supports a background statement about RGB/NIR band use and is not load-bearing. No imported uniqueness theorem or citation-borne ansatz appears. The derivation chain is therefore self-contained, and the appropriate circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

This is an empirical deep learning paper. There are no invented physical entities and no analytic derivation constants; the free parameters are hand-chosen training choices. The most consequential assumptions are label reliability and baseline fairness, especially the unvalidated NDWI-derived pseudo ground truth used for the generalization claim.

free parameters (5)

Patch size = 128 x 128 pixels
Selected by iterative assessment (Section II-D); controls context and compute for all experiments.
Training patch stride = 64 pixels
Data-loader stride used to generate more training patches; no sensitivity analysis.
Patch filtering threshold = exclude patches with more than 50% of pixels equal to 255
Ad hoc rule to keep informative patches; changes training distribution.
Learning rate schedule = 1e-4, Adam, 25 epochs, 10 cosine restarts
Optimization configuration from Section III-B; no ablation.
Loss weighting = BCE + Dice with equal weight
Composite loss from Section II-D; relative weights not explored.

assumptions (5)

domain assumption Sen1Floods11 manual labels are accurate enough to serve as ground truth.
All benchmark numbers in Table I assume these labels are correct (Section III-A).
ad hoc to paper The modified NDWI pseudo ground truth is a valid proxy for flood extent on PlanetScope imagery.
Section III-A creates labels from NDWI with manual corrections and states the delineation could not be accurately validated; Table II still uses them as quantitative targets.
ad hoc to paper All baseline models were trained fairly and to comparable convergence.
Section III-C specifies only backbone and bands for baselines, with no training schedule or tuning, so fairness is assumed.
domain assumption U-Net-style encoder-decoder with skip connections is a suitable base architecture for flood segmentation.
The model is built on U-Net [12] without comparing alternative base architectures.
domain assumption Near-infrared reflectance is informative for separating water from land.
NIR is used in encoder 2 and in the NDWI pseudo labels; this is consistent with remote sensing literature but is not independently tested here.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Progressive Cross Attention Network for Flood Segmentation using Multispectral Satellite Imagery." pith.science (2026). https://pith.science/paper/PE4NOX2E

@misc{pith2026250111923,
  author       = {Pith},
  title        = {Pith review of: Progressive Cross Attention Network for Flood Segmentation using Multispectral Satellite Imagery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PE4NOX2E}},
  note         = {Machine review of arXiv:2501.11923}
}

read the original abstract

In recent years, the integration of deep learning techniques with remote sensing technology has revolutionized the way natural hazards, such as floods, are monitored and managed. However, existing methods for flood segmentation using remote sensing data often overlook the utility of correlative features among multispectral satellite information. In this study, we introduce a progressive cross attention network (ProCANet), a deep learning model that progressively applies both self- and cross-attention mechanisms to multispectral features, generating optimal feature combinations for flood segmentation. The proposed model was compared with state-of-the-art approaches using Sen1Floods11 dataset and our bespoke flood data generated for the Citarum River basin, Indonesia. Our model demonstrated superior performance with the highest Intersection over Union (IoU) score of 0.815. Our results in this study, coupled with the ablation assessment comparing scenarios with and without attention across various modalities, opens a promising path for enhancing the accuracy of flood analysis using remote sensing technology.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 13 canonical work pages

[1]

Satellite imaging reveals increased proportion of population exposed to floods,

B. Tellman et al. , “Satellite imaging reveals increased proportion of population exposed to floods,” Nature, vol. 596, no. 7870, pp. 80–86, 2021

work page 2021
[2]

The impact of climate -change-related disasters on africa’s economic growth, agriculture, and conflicts: Can humanitarian aid and food assistance offset the damage?,

G. Shimada, “The impact of climate -change-related disasters on africa’s economic growth, agriculture, and conflicts: Can humanitarian aid and food assistance offset the damage?,” Int J Environ Res Public Health, vol. 19, no. 1, p. 467, 2022

work page 2022
[3]

Predicting inflow rate of the Soyang river dam using deep learning techniques,

S. Lee and J. Kim, “Predicting inflow rate of the Soyang river dam using deep learning techniques,” Water (Basel), vol. 13, no. 17, p. 2447, 2021

work page 2021
[4]

Y. Bai et al., “Enhancement of detecting permanent water and temporary water in flood disasters by fusing sentinel-1 and sentinel -2 imagery using deep learning algorithms: Demonstration of sen1floods11 benchmark datasets,” Remote Sens (Basel), vol. 13, no. 11, p. 2220, 2021

work page 2021
[5]

Deep attentive fusion network for flood detection on uni -temporal Sentinel -1 data,

R. Yadav, A. Nascetti, and Y. Ban, “Deep attentive fusion network for flood detection on uni -temporal Sentinel -1 data,” Frontiers in Remote Sensing , vol. 3, p. 1060144, 2022

work page 2022
[6]

A deep learning technique -based data -driven model for accurate and rapid flood prediction,

Q. Zhou, S. Teng, X. Liao, Z. Situ, J. Feng, and G. Chen, “A deep learning technique -based data -driven model for accurate and rapid flood prediction,” Hydrology and Earth System Sciences Discussions, vol. 2022, pp. 1–22, 2022

work page 2022
[7]

Boundary-aware segmentation network for mobile and web applications,

X. Qin et al., “Boundary-aware segmentation network for mobile and web applications,” arXiv preprint arXiv:2101.04704, 2021

arXiv 2021
[8]

From local to regional compound flood mapping with deep learning and data fusion techniques,

D. F. Muñoz, P. Muñoz, H. Moftakhari, and H. Moradkhani, “From local to regional compound flood mapping with deep learning and data fusion techniques,” Science of the Total Environment , vol. 782, p. 146927, 2021

work page 2021

Show all 18 references

[9]

Attention u -net: Learning where to look for the pancreas,

O. Oktay et al., “Attention u -net: Learning where to look for the pancreas,” arXiv preprint arXiv:1804.03999, 2018

2018 arXiv
[10]

Design and Experiment of Online Detection System for Water Content of Fresh Tea Leaves after Harvesting Based on Near Infra -Red Spectroscopy,

S. Wang et al. , “Design and Experiment of Online Detection System for Water Content of Fresh Tea Leaves after Harvesting Based on Near Infra -Red Spectroscopy,” Sensors, vol. 23, no. 2, p. 666, 2023

2023
[11]

Applications in Remote Sensing to Forest Ecology and Management,

A. M. Lechner, G. M. Foody, and D. S. Boyd, “Applications in Remote Sensing to Forest Ecology and Management,” One Earth, vol. 2, no. 5, pp. 405–412, 2020, doi: https://doi.org/10.1016/j.oneear.2020.05.001

2020 doi
[12]

U -net: Convolutional networks for biomedical image segmentation,

O. Ronneberger, P. Fischer, and T. Brox, “U -net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention –MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18,...

2015
[13]

Sen1Floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel -1,

D. Bonafilia, B. Tellman, T. Anderson, and E. Issenberg, “Sen1Floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel -1,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 210– 211

2020
[14]

Pyramid scene parsing network,

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2881–2890

2017
[15]

Linknet: Exploiting encoder representations for efficient semantic segmentation,

A. Chaurasia and E. Culurciello, “Linknet: Exploiting encoder representations for efficient semantic segmentation,” in 2017 IEEE visual communications and image processing (VCIP), 2017, pp. 1–4

2017
[16]

Multiattention network for semantic segmentation of fine -resolution remote sensing images,

R. Li et al. , “Multiattention network for semantic segmentation of fine -resolution remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2021

2021
[17]

Pyramid attention network for semantic segmentation,

H. Li, P. Xiong, J. An, and L. Wang, “Pyramid attention network for semantic segmentation,” arXiv preprint arXiv:1805.10180, 2018

2018 arXiv
[18]

ConvNeXt V2: Co -designing and Scaling ConvNets with Masked Autoencoders,

S. Woo et al., “ConvNeXt V2: Co -designing and Scaling ConvNets with Masked Autoencoders,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 16133 –16142. doi: 10.1109/CVPR52729.2023.01548

2023

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Satellite imaging reveals increased proportion of population exposed to floods,

B. Tellman et al. , “Satellite imaging reveals increased proportion of population exposed to floods,” Nature, vol. 596, no. 7870, pp. 80–86, 2021

work page 2021

[2] [2]

The impact of climate -change-related disasters on africa’s economic growth, agriculture, and conflicts: Can humanitarian aid and food assistance offset the damage?,

G. Shimada, “The impact of climate -change-related disasters on africa’s economic growth, agriculture, and conflicts: Can humanitarian aid and food assistance offset the damage?,” Int J Environ Res Public Health, vol. 19, no. 1, p. 467, 2022

work page 2022

[3] [3]

Predicting inflow rate of the Soyang river dam using deep learning techniques,

S. Lee and J. Kim, “Predicting inflow rate of the Soyang river dam using deep learning techniques,” Water (Basel), vol. 13, no. 17, p. 2447, 2021

work page 2021

[4] [4]

Y. Bai et al., “Enhancement of detecting permanent water and temporary water in flood disasters by fusing sentinel-1 and sentinel -2 imagery using deep learning algorithms: Demonstration of sen1floods11 benchmark datasets,” Remote Sens (Basel), vol. 13, no. 11, p. 2220, 2021

work page 2021

[5] [5]

Deep attentive fusion network for flood detection on uni -temporal Sentinel -1 data,

R. Yadav, A. Nascetti, and Y. Ban, “Deep attentive fusion network for flood detection on uni -temporal Sentinel -1 data,” Frontiers in Remote Sensing , vol. 3, p. 1060144, 2022

work page 2022

[6] [6]

A deep learning technique -based data -driven model for accurate and rapid flood prediction,

Q. Zhou, S. Teng, X. Liao, Z. Situ, J. Feng, and G. Chen, “A deep learning technique -based data -driven model for accurate and rapid flood prediction,” Hydrology and Earth System Sciences Discussions, vol. 2022, pp. 1–22, 2022

work page 2022

[7] [7]

Boundary-aware segmentation network for mobile and web applications,

X. Qin et al., “Boundary-aware segmentation network for mobile and web applications,” arXiv preprint arXiv:2101.04704, 2021

arXiv 2021

[8] [8]

From local to regional compound flood mapping with deep learning and data fusion techniques,

D. F. Muñoz, P. Muñoz, H. Moftakhari, and H. Moradkhani, “From local to regional compound flood mapping with deep learning and data fusion techniques,” Science of the Total Environment , vol. 782, p. 146927, 2021

work page 2021