Pith. sign in

REVIEW 2 major objections 6 minor 99 references

Seasonal Forecasting of Pan-Arctic Sea Ice with State Space Model

T0 review · 2 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read IceMamba, a state space deep learning model, is claimed to deliver state-of-the-art seasonal forecasts of pan-Arctic sea ice concentration, beating all 25 models in the September benchmark on average RMSE and ACC.

desk verdict Solid architecture paper whose external SOTA claim is not yet clean: benchmark protocol is under-specified and input selection overlaps test years. read the letter →

arxiv 2505.10665 v1 pith:5C4BDHUB submitted 2025-05-15 cs.LG cs.AI

Wei Wang , Weidong Yang , Lei Wang , Guihua Wang , Ruibo Lei This is my paper · ORCID
classification cs.LGcs.AI
keywords seaiceconcentrationseasonalforecastingstatespacemodeldeeplearningpan-Arcticreanalysisbenchmarkcomparisonexplainability
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

IceMamba, a deep learning architecture built on a state space model with embedded attention, is put forward as a new method for seasonal forecasting of pan-Arctic sea ice concentration at 25 km resolution. The paper claims that, on the September forecast benchmark of 25 dynamical, statistical, and deep learning models over 2001-2020, IceMamba-4 achieves the lowest average RMSE ($22.0086\%$), IceMamba-1-only-SIC achieves the highest average anomaly correlation ($0.6604$), and the same variant ranks second in integrated ice edge error. It further claims this is the first application of state space models to sea ice forecasting, that the proposed Residual Efficient State Space Block improves on the plain Vision State Space Block, and that adding subsurface ocean reanalysis variables (ocean heat content, mixed-layer depth) improves skill, especially for longer lead times. A sympathetic reader would care because data-driven seasonal sea ice forecasts at useful spatial detail, if they hold up, are much cheaper to run than dynamical systems and could support Arctic operations and climate adaptation decisions.

What carries the argument

The load-bearing component is the Residual Efficient State Space Block (RESSB), a modification of the Vision State Space Block (VSSB). RESSB passes the feature map through an Efficient Channel Attention module, then two VSSB blocks, and adds a SiLU-activated $1\times1$ convolution branch via residual addition. The VSSB itself uses a 2D Selective Scan (SS2D) that scans image patches in four directions through a cross-scan module and processes each sequence with the S6 selective state space model, giving a global receptive field at linear cost. The ECA channel attention selects the most informative input variables and lag months, and the residual branch helps fuse information across channels. This stack sits inside an encoder-decoder with patch merging and expanding plus skip connections, and the model is retrained under a rolling-window scheme so that forecast years stay out of the training set.

What would settle it

Re-run IceMamba-4 and IceMamba-1-only-SIC through the published benchmark's own evaluation code on the exact verification mask (September SIC standard deviation above 10 percent), climatology, and 2001-2020 forecast years; if the recomputed mean RMSE and ACC differ materially from $22.0086\%$ and $0.6604$, or fall below the best dynamical baseline and damped persistence, the state-of-the-art claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that a state space sequence model, equipped with channel attention and residual connections, can produce the most skillful seasonal pan-Arctic sea ice concentration forecasts among 25 compared models. On the benchmark, IceMamba-4, which predicts the next four monthly mean SIC maps in a single step, has the lowest mean RMSE ($22.0086\%$), while IceMamba-1-only-SIC, a recurrent variant that updates its input monthly and uses only SIC, has the highest mean ACC ($0.6604$) and the second-lowest mean IIEE ($0.9459\times 10^{12}\,\text{m}^2$) across 2001-2020. The authors argue that the model learns the seasonal cycle implicitly: short lead times lean on the previous month's SIC, longer leads shift to the same calendar month from prior years, and sensitivity to upward surface solar radiation during summer reflects the ice-albedo feedback. They also report that apparent sensitivity to 10 hPa stratospheric wind largely disappears when the input is detrended, indicating a shared anthropogenic trend rather than a causal dynamical coupling. A separate examination of extreme September years (2012, 2016, 2019) supports the claim of robust performance under strong ice loss.

Load-bearing premise

The claim that IceMamba beats all 25 models assumes its scores were computed under exactly the same evaluation rules as the published scores of the other models\u2014the same set of grid cells, the same definition of September mean, the same comparison years\u2014which the paper does not demonstrate.

Editorial extensions

If this is right

  • If the benchmark comparison is valid, a purely data-driven 25 km SIC forecast can beat all 25 dynamical, statistical, and deep learning models on average September RMSE and ACC, not just match them.
  • Subsurface ocean variables (ocean heat content and mixed layer depth) become more valuable as the forecast horizon grows, with the largest gains in August-October, so future systems should treat them as standard predictors.
  • IceMamba-4 retains stable skill under June-initiated forecasts during extreme ice-loss years, suggesting single-step multi-month prediction is more robust to error accumulation than recursive updating.
  • The implicit seasonal alignment, using last year's same-month SIC at longer leads, works without explicit temporal encoding, indicating the lag ordering alone supplies enough seasonal structure.
  • The detrending result implies that the model's stratospheric-wind sensitivity is mostly a shared anthropogenic trend, so attribution statements from such models must separate trend from mechanism.

Reading between the lines

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

  • If the state space architecture is the reason for the skill, the same RESSB-style block should transfer to other gridded climate forecast problems, such as Antarctic sea ice, sea surface temperature, or vegetation indices, where long-range spatiotemporal dependence matters; the paper does not test this.
  • Because the model leans on the anthropogenic trend as a form of memory, its forecast skill in the 2030s may degrade if the trend's character changes, for example under a regime shift in Arctic sea ice; a direct test would be to retrain on data ending before 2000 and verify on 2001-2020.
  • The channel-fusion limitation named in the paper suggests a testable extension: feeding global atmospheric fields or separate per-variable encoders could remove the Arctic-grid bias it describes, and one could compare such variants against IceMamba on the same benchmark.
  • The permutation result that upward solar radiation matters more than downward radiation points to a concrete probe: an ablation that hides ussr while keeping dssr should hurt summer forecasts more than the reverse, which could be checked with the released model code.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

Summary. The paper introduces IceMamba, a state-space-model-based encoder-decoder architecture for seasonal pan-Arctic sea ice concentration (SIC) forecasting at 25 km resolution. The core modeling contribution is the Residual Efficient State Space Block (RESSB), which augments a Vision State Space Block with efficient channel attention and a residual 1x1-convolution branch. The authors train three forecast-horizon variants (1, 4, and 6 months) on NSIDC SIC together with ERA5 and ORAS5 reanalysis fields, compare them against anomaly persistence and a 25-model benchmark from Bushuk et al., and claim state-of-the-art seasonal forecast skill: IceMamba-4 achieves the lowest mean RMSE (22.0086%), IceMamba-1-only-SIC achieves the highest mean ACC (0.6604), and IceMamba-1-only-SIC ranks second in mean IIEE. The paper also presents a permutation-based explainability analysis and attributes the model's sensitivity to stratospheric wind to shared anthropogenic trends rather than direct dynamical coupling.

Significance. If the headline claims are supported, this is a valuable contribution: it is, to the authors' knowledge, the first application of state space models to sea ice forecasting; the architecture has linear complexity relative to Transformers; code and data links are provided; the benchmark evaluation uses a temporally separated rolling-window recalibration scheme; and the explainability section includes a detrending control to distinguish trend-driven from mechanistic linkages. However, the state-of-the-art claim is not yet established because the comparison with the 25-model benchmark depends on verification-protocol equivalence that is not demonstrated, and because the input-variable configuration for each IceMamba variant was selected using the same test years that overlap the benchmark evaluation. These issues are load-bearing for the central SOTA claim, and the reported margins over the next-best models are small enough that either issue could change the ranking.

major comments (2)
  1. [Skill comparison with dynamical and statistical models (Figs. 4-5)] The headline skill claims depend on a comparison in which IceMamba's RMSE, ACC, and IIEE are computed by the authors while the corresponding scores for the 25 benchmark models are taken as published from [43]. The paper states that "RMSE and ACC metrics focus on regions where the standard deviation of September SIC exceeds 10%" but does not specify the climatology period used to compute that standard deviation, the land/ocean mask, the treatment of the pole hole, the exact September-mean processing, or the grid-point count used in the spatial averages. Any mismatch between this mask and the mask used in the published benchmark directly changes the reported scores and the ordering of models; the margins in Fig. 5(a) (e.g., IceMamba-4's 22.0086% vs SEAS5's 22.4916%) are small enough that such a mismatch could alter the conclusions. The authors should demonstrate protocol equivalence, ideally by running a single verification code over all models' September-mean forecasts, or at minimum by reproducing the exact Bushuk et al. mask, climatology, and averaging definitions and making the verification code available.
  2. [Overall Performance of IceMamba (Table 1) and benchmark setup] The per-variant input configuration was selected using test data that overlap the benchmark evaluation. The text states that for each variant "the data combination with the best forecast performance is selected as the final training set," and the Method section defines the internal test set as 2015-2022 (with a corresponding forecast test period of 2016-2022). The benchmark evaluation covers 2001-2020, so the years 2016-2020 appear both in the configuration-selection criterion and in the reported benchmark scores. This makes the reported SOTA margins partly in-sample: IceMamba-4 and IceMamba-1-only-SIC were chosen among candidate input combinations for their performance on a period that includes five of the benchmark years. The authors should redo the variable selection using only data before 2016 (e.g., the 2011-2014 validation period), or use nested cross-validation so that selection and evaluation never touch the same years, and should report whether the benchmark ranking is robust to this choice.
minor comments (6)
  1. [Skill comparison with dynamical and statistical models (Fig. 5)] The sentence "IceMamba-4 achieved the lowest average RMSE ... in all tested models" appears immediately after Fig. 5, which only shows models with complete 20-year forecasts; please clarify whether this claim refers to all 25 models or to the 20-year subset, and report the mean scores of the 5 incomplete models if they are included in the comparison.
  2. [Forecast performance in extreme September sea ice events] The value 11.72% is attributed to IceMamba-1-only-SIC in the Results ("In September 2019, IceMamba-1-only-SIC recorded an RMSE of 11.72%, the best performance observed across all evaluated models") but later in the Discussion is attributed to IceMamba-4 ("IceMamba-4 achieving the lowest RMSE (11.72% in 2019)"); please correct this inconsistency.
  3. [Skill comparison with dynamical and statistical models] The reported RMSE for IceMamba-1-only-SIC at September 1 initialization is given as 14.8792% in one paragraph and as 15.2924% in the Discussion; please reconcile these numbers.
  4. [Overall Performance of IceMamba] Supplementary Tables 2-4 are cited both as the input-combination experiments and as the benchmark evaluation results; please disambiguate the table numbering.
  5. [Method: training scheme] There is a typo "January January Y-3" in the rolling-window description, and "forecats" appears in Section 2; both should be corrected.
  6. [Table 1 and Fig. 4/5 captions] Table 1 reports metrics over all non-land regions for the 2016-2022 test period, while Figs. 4-5 use the benchmark's >10% standard-deviation mask over 2001-2020; please state this distinction explicitly in the captions to avoid confusion.

Circularity Check

1 steps flagged · score 6.0 of 10

Input configuration is selected on the 2016-2022 test period, making the reported forecast skill and SOTA benchmark claim partially self-selected; the architecture derivation itself is self-contained.

  1. fitted input called prediction [Section 'Result' > 'Overall Performance of IceMamba'; Section 'Method' > 'Input variables of IceMamba']
    "For each variant, we tested multiple input combinations (as shown in Supplementary Tables 2 to 4) and the data combination with the best forecast performance is selected as the final training set for IceMamba-1, IceMamba-4, and IceMamba-6. ... The temporal coverage for the training, valid, and test sets, respectively, spans from Jan 1979 to Dec 2010 (32 years), Jan 2011 to Dec 2014 (4 years), and Jan 2015 to Dec 2022 (8 years). Note that IceMamba uses the monthly average SIC of the previous 12 months as input, the test period corresponding to the test set is 2016-2022."

    The input configuration is a hyperparameter chosen by best forecast performance on the 2016-2022 test period, and Table 1 as well as the benchmark skill (2001-2020, overlapping 2016-2020) are then reported as predictive skill. The reported 'predictions' therefore include the same years used to select the configuration, so the superiority of IceMamba-4 and IceMamba-1-only-SIC over comparison models is partly manufactured by selection on the evaluation set rather than demonstrated on independent data. This is a selection loop rather than a full derivation-from-input equality, but it does make the headline SOTA claim partially self-confirming.

full rationale

The only concrete circularity I can exhibit is the test-set-driven input configuration selection. The paper states that multiple input combinations were tested and the best-performing one on the forecast test period was chosen, and it later identifies that test period as 2016-2022. The same period is then used to report internal skill (Table 1) and overlaps the benchmark years 2016-2020 used for the SOTA claims in Section 'Skill comparison with dynamical and statistical models'. This fits the 'fitted input called prediction' pattern: the input combination is effectively fit to the evaluation period, and the resulting scores are presented as predictive skill. I do not find evidence of self-definitional circularity, self-citation load-bearing arguments, uniqueness imported from the authors, or ansatz smuggled in via citation. The RESSB-versus-VSSB comparison is an empirical ablation with identical configurations and datasets, not a circular reduction. The benchmark verification-mask and metric-consistency concerns are real correctness risks, but they are not circularity: they concern comparability of independently computed scores, not a derivation that reduces to its own inputs. Because the central SOTA claim depends in part on configuration selection using the very evaluation years reported, a score of 6 is appropriate; the rest of the paper's derivation chain is self-contained.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim does not rest on new physical conservation laws or invented entities; it rests on data quality, benchmark comparability, and the validity of the Mamba/VMamba machinery. The most consequential free parameter is the input variable subset, chosen on the test period. No invented entities are needed for the model to work.

free parameters (4)
  • per-variant input variable subset = IceMamba-1: ERA5+ohc300+mld001; IceMamba-4: ERA5+ohc300+mld003; IceMamba-6: ERA5+ohc700+mld001+mld003
    Selected as the combination with the best forecast performance on the internal test period (Section 'Overall Performance'), so the reported skill includes test-set selection.
  • training hyperparameters = learning rate 0.001 halved every 10 epochs; batch size 1; early stopping patience 10
    Set by hand with no sensitivity analysis; performance depends on them, but no claim of optimality is made.
  • climatology and anomaly baseline period = 1979-2010
    Used to compute anomalies and normalize reanalysis variables; chosen as the training period rather than the full record.
  • model architecture dimensions
    Channel counts and depths (448x304x50 input to 28x19x768 latent) are design choices; the paper reports them without ablations, except replacing RESSB with VSSB in one variant.
assumptions (4)
  • domain assumption The 25-model benchmark scores from Bushuk et al. [43] are directly comparable to the authors' own IceMamba scores, including verification mask, climatology, and metric implementations.
    The paper adopts the benchmark but does not demonstrate that its RMSE, ACC, and IIEE pipeline is identical to those used for the other models; this is load-bearing for the SOTA claim.
  • domain assumption NSIDC CDR SIC, ERA5, and ORAS5 reanalysis fields accurately represent Arctic sea ice and ocean and atmosphere state at 25 km monthly resolution.
    All training and verification depends on these datasets; the paper notes re-gridding and polar-hole interpolation can introduce biases.
  • standard math Mamba/VMamba selective state space equations (Eqs. 2-5) and the ECA mechanism behave as described in the cited papers.
    The model uses these blocks as building blocks; the paper restates the equations from references [41,89] without proving or altering them.
  • domain assumption Permutation-based feature importance reflects predictive dependence of the model, not just an artifact of the shuffling procedure.
    The explainability section shuffles each variable across temporal dimensions and interprets MAE degradation as physical importance; this is a standard but assumption-laden method.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Seasonal Forecasting of Pan-Arctic Sea Ice with State Space Model." pith.science (2026). https://pith.science/paper/5C4BDHUB

@misc{pith2026250510665,
  author       = {Pith},
  title        = {Pith review of: Seasonal Forecasting of Pan-Arctic Sea Ice with State Space Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5C4BDHUB}},
  note         = {Machine review of arXiv:2505.10665}
}
read the original abstract

The rapid decline of Arctic sea ice resulting from anthropogenic climate change poses significant risks to indigenous communities, ecosystems, and the global climate system. This situation emphasizes the immediate necessity for precise seasonal sea ice forecasts. While dynamical models perform well for short-term forecasts, they encounter limitations in long-term forecasts and are computationally intensive. Deep learning models, while more computationally efficient, often have difficulty managing seasonal variations and uncertainties when dealing with complex sea ice dynamics. In this research, we introduce IceMamba, a deep learning architecture that integrates sophisticated attention mechanisms within the state space model. Through comparative analysis of 25 renowned forecast models, including dynamical, statistical, and deep learning approaches, our experimental results indicate that IceMamba delivers excellent seasonal forecasting capabilities for Pan-Arctic sea ice concentration. Specifically, IceMamba outperforms all tested models regarding average RMSE and anomaly correlation coefficient (ACC) and ranks second in Integrated Ice Edge Error (IIEE). This innovative approach enhances our ability to foresee and alleviate the effects of sea ice variability, offering essential insights for strategies aimed at climate adaptation.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

99 extracted references · 67 canonical work pages

  1. [43]

    Bulletin of the American Meteorological Society (2024)

    Bushuk, M., Ali, S., Bailey, D.A., Bao, Q., Batt´ e, L., Bhatt, U.S., Blanchard- Wrigglesworth, E., Blockley, E., Cawley, G., Chi, J., et al.: Predicting september arctic sea ice: A multi-model seasonal skill comparison. Bulletin of the American Meteorological Society (2024)

    work page 2024

  2. [1]

    Nature communications12(1), 1519 (2021) 26

    Liu, Z., Risi, C., Codron, F., He, X., Poulsen, C.J., Wei, Z., Chen, D., Li, S., Bowen, G.J.: Acceleration of western arctic sea ice loss linked to the pacific north american pattern. Nature communications12(1), 1519 (2021) 26

    2021

  3. [2]

    Nature communications13(1), 727 (2022)

    Smith, D.M., Eade, R., Andrews, M., Ayres, H., Clark, A., Chripko, S., Deser, C., Dunstone, N., Garc´ ıa-Serrano, J., Gastineau, G.,et al.: Robust but weak winter atmospheric circulation response to future arctic sea ice loss. Nature communications13(1), 727 (2022)

    2022

  4. [3]

    Journal of Climate34(14), 5847–5867 (2021)

    Chripko, S., Msadek, R., Sanchez-Gomez, E., Terray, L., Bessi` eres, L., Moine, M.- P.: Impact of reduced arctic sea ice on northern hemisphere climate and weather in autumn and winter. Journal of Climate34(14), 5847–5867 (2021)

    2021

  5. [4]

    Environmental Research Letters16(3), 034015 (2021)

    Cai, Q., Wang, J., Beletsky, D., Overland, J., Ikeda, M., Wan, L.: Accelerated decline of summer arctic sea ice during 1850–2017 and the amplified arctic warm- ing during the recent decades. Environmental Research Letters16(3), 034015 (2021)

    2021

  6. [5]

    Journal of Climate37(8), 2713–2725 (2024)

    Chung, P.-C., Feldl, N.: Sea ice loss, water vapor increases, and their interac- tions with atmospheric energy transport in driving seasonal polar amplification. Journal of Climate37(8), 2713–2725 (2024)

    2024

  7. [6]

    Overland, J.E., Ballinger, T.J., Cohen, J., Francis, J., Hanna, E., Jaiser, R., Kim, B.-M., Kim, S.-J., Ukita, J., Vihma, T.,et al.: How do intermittency and simul- taneous processes obfuscate the arctic influence on midlatitude winter extreme weather events? Environmental Research Letters16(4), 043002 (2021)

    2021

  8. [7]

    Journal of Climate (2024)

    Westen, R.M., Jacques-Dumas, V., Boot, A.A., Dijkstra, H.A.: The role of sea- ice insulation effects on the probability of amoc transitions. Journal of Climate (2024)

    2024

Show all 99 references
  1. [8]

    Nature Climate Change10(11), 983–992 (2020)

    Lannuzel, D., Tedesco, L., Van Leeuwe, M., Campbell, K., Flores, H., Delille, B., Miller, L., Stefels, J., Assmy, P., Bowman, J.,et al.: The future of arctic sea-ice biogeochemistry and ice-associated ecosystems. Nature Climate Change10(11), 983–992 (2020)

    2020

  2. [9]

    Bulletin of the American Meteorological Society97(9), 1631–1647 (2016)

    Jung, T., Gordon, N.D., Bauer, P., Bromwich, D.H., Chevallier, M., Day, J.J., Dawson, J., Doblas-Reyes, F., Fairall, C., Goessling, H.F.,et al.: Advancing polar prediction capabilities on daily to seasonal time scales. Bulletin of the American Meteorological Society97(9), 1631...

    2016

  3. [10]

    0: Model description and simulation features

    Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C.O., Dunne, J.P., Griffies, S.M., Hallberg, R., Harrison, M.J.,et al.: The gfdl global ocean and sea ice model om4. 0: Model description and simulation features. Journal of Advances in Modeling Earth Syst...

    2019

  4. [11]

    Science advances3(3), 1601191 (2017)

    Horvat, C., Jones, D.R., Iams, S., Schroeder, D., Flocco, D., Feltham, D.: The frequency and extent of sub-ice phytoplankton blooms in the arctic ocean. Science advances3(3), 1601191 (2017)

    2017

  5. [12]

    Quarterly Journal of the Royal Meteorological Society142(695), 659–671 (2016)

    Smith, G.C., Roy, F., Reszka, M., Surcel Colan, D., He, Z., Deacu, D., Belanger, 27 J.-M., Skachko, S., Liu, Y., Dupont, F.,et al.: Sea ice forecast verification in the canadian global ice ocean prediction system. Quarterly Journal of the Royal Meteorological Society142(695), ...

    2016

  6. [13]

    Geophysical Research Letters42(19), 8042–8048 (2015)

    Blanchard-Wrigglesworth, E., Cullather, R., Wang, W., Zhang, J., Bitz, C.: Model forecast skill and sensitivity to initial conditions in the seasonal sea ice outlook. Geophysical Research Letters42(19), 8042–8048 (2015)

    2015

  7. [14]

    Nature Climate Change4(5), 353–357 (2014)

    Schr¨ oder, D., Feltham, D.L., Flocco, D., Tsamados, M.: September arctic sea-ice minimum predicted by spring melt-pond fraction. Nature Climate Change4(5), 353–357 (2014)

    2014

  8. [15]

    Earth’s Future5(2), 254–263 (2017)

    Petty, A., Schr¨ oder, D., Stroeve, J., Markus, T., Miller, J., Kurtz, N.T., Feltham, D., Flocco, D.: Skillful spring forecasts of september arctic sea ice extent using passive microwave sea ice observations. Earth’s Future5(2), 254–263 (2017)

    2017

  9. [16]

    Weather and Forecasting35(3), 793–806 (2020)

    Gregory, W., Tsamados, M., Stroeve, J., Sollich, P.: Regional september sea ice forecasting with complex networks and gaussian processes. Weather and Forecasting35(3), 793–806 (2020)

    2020

  10. [17]

    Journal of Climate29(4), 1529–1543 (2016)

    Wang, L., Yuan, X., Ting, M., Li, C.: Predicting summer arctic sea ice concen- tration intraseasonal variability using a vector autoregressive model. Journal of Climate29(4), 1529–1543 (2016)

    2016

  11. [18]

    Journal of Climate29(22), 8151–8173 (2016)

    Yuan, X., Chen, D., Li, C., Wang, L., Wang, W.: Arctic sea ice seasonal prediction by a linear markov model. Journal of Climate29(22), 8151–8173 (2016)

    2016

  12. [19]

    The Cryosphere16(3), 1141–1156 (2022)

    Wang, Y., Yuan, X., Bi, H., Bushuk, M., Liang, Y., Li, C., Huang, H.: Reassessing seasonal sea ice predictability of the pacific-arctic sector using a markov model. The Cryosphere16(3), 1141–1156 (2022)

    2022

  13. [20]

    Climate Dynamics52, 4953–4971 (2019)

    Wang, L., Yuan, X., Li, C.: Subseasonal forecast of arctic sea ice concentration via statistical approaches. Climate Dynamics52, 4953–4971 (2019)

    2019

  14. [21]

    Journal of Climate 36(15), 4879–4897 (2023)

    Wang, Y., Yuan, X., Bi, H., Ren, Y., Liang, Y., Li, C., Li, X.: Understanding arctic sea ice thickness predictability by a markov model. Journal of Climate 36(15), 4879–4897 (2023)

    2023

  15. [22]

    Quarterly Journal of the Royal Meteorological Society142(695), 546–561 (2016)

    Guemas, V., Blanchard-Wrigglesworth, E., Chevallier, M., Day, J.J., D´ equ´ e, M., Doblas-Reyes, F.J., Fuˇ ckar, N.S., Germe, A., Hawkins, E., Keeley, S.,et al.: A review on arctic sea-ice predictability and prediction on seasonal to decadal time- scales. Quarterly Journal of ...

    2016

  16. [23]

    Remote Sensing9(12), 1305 (2017) 28

    Chi, J., Kim, H.-c.: Prediction of arctic sea ice concentration using a fully data driven deep neural network. Remote Sensing9(12), 1305 (2017) 28

    2017

  17. [24]

    Remote Sensing11(1), 19 (2018)

    Kim, J., Kim, K., Cho, J., Kang, Y.Q., Yoon, H.-J., Lee, Y.-W.: Satellite-based prediction of arctic sea ice concentration using a deep neural network with multi- model ensemble. Remote Sensing11(1), 19 (2018)

    2018

  18. [25]

    The Cryosphere14(3), 1083–1104 (2020)

    Kim, Y.J., Kim, H.-C., Han, D., Lee, S., Im, J.: Prediction of monthly arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks. The Cryosphere14(3), 1083–1104 (2020)

    2020

  19. [26]

    Remote Sensing13(17), 3413 (2021)

    Chi, J., Bae, J., Kwon, Y.-J.: Two-stream convolutional long-and short-term memory model using perceptual loss for sequence-to-sequence arctic sea ice prediction. Remote Sensing13(17), 3413 (2021)

    2021

  20. [27]

    Nature communications12(1), 5124 (2021)

    Andersson, T.R., Hosking, J.S., P´ erez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D.C., Wilkinson, J., Phillips, T.,et al.: Seasonal arctic sea ice forecasting with probabilistic deep learning. Nature communications12(1), 5124 (2021)

    2021

  21. [28]

    Journal of Geophysical Research: Atmospheres128(24), 2023–039521 (2023)

    Zhu, Y., Qin, M., Dai, P., Wu, S., Fu, Z., Chen, Z., Zhang, L., Wang, Y., Du, Z.: Deep learning-based seasonal forecast of sea ice considering atmospheric con- ditions. Journal of Geophysical Research: Atmospheres128(24), 2023–039521 (2023)

    2023

  22. [29]

    Remote Sensing11(9), 1071 (2019)

    Choi, M., De Silva, L.W.A., Yamaguchi, H.: Artificial neural network for the short-term prediction of arctic sea ice concentration. Remote Sensing11(9), 1071 (2019)

    2019

  23. [30]

    Journal of Geophysical Research: Oceans125(11), 2020–016277 (2020)

    Fritzner, S., Graversen, R., Christensen, K.H.: Assessment of high-resolution dynamical and machine learning models for prediction of sea ice concentration in a regional application. Journal of Geophysical Research: Oceans125(11), 2020–016277 (2020)

    2020

  24. [31]

    Frontiers in Marine Science8, 736429 (2021)

    Liu, Q., Zhang, R., Wang, Y., Yan, H., Hong, M.: Short-term daily prediction of sea ice concentration based on deep learning of gradient loss function. Frontiers in Marine Science8, 736429 (2021)

    2021

  25. [32]

    IEEE Transactions on Geoscience and Remote Sensing60, 1–19 (2022)

    Ren, Y., Li, X., Zhang, W.: A data-driven deep learning model for weekly sea ice concentration prediction of the pan-arctic during the melting season. IEEE Transactions on Geoscience and Remote Sensing60, 1–19 (2022)

    2022

  26. [33]

    IEEE Transactions on Geoscience and Remote Sensing (2023)

    Ren, Y., Li, X.: Predicting the daily sea ice concentration on a sub-seasonal scale of the pan-arctic during the melting season by a deep learning model. IEEE Transactions on Geoscience and Remote Sensing (2023)

    2023

  27. [34]

    Remote Sensing14(12), 2889 (2022) 29

    Zheng, Q., Li, W., Shao, Q., Han, G., Wang, X.: A mid-and long-term arctic sea ice concentration prediction model based on deep learning technology. Remote Sensing14(12), 2889 (2022) 29

    2022

  28. [35]

    IEEE Transactions on Geoscience and Remote Sensing (2024)

    Zheng, Q., Wang, R., Han, G., Li, W., Wang, X., Shao, Q., Wu, X., Cao, L., Zhou, G., Hu, S.: A spatio-temporal multiscale deep learning model for subseasonal prediction of arctic sea ice. IEEE Transactions on Geoscience and Remote Sensing (2024)

    2024

  29. [36]

    Remote Sensing14(22), 5837 (2022)

    Grigoryev, T., Verezemskaya, P., Krinitskiy, M., Anikin, N., Gavrikov, A., Trofi- mov, I., Balabin, N., Shpilman, A., Eremchenko, A., Gulev, S.,et al.: Data-driven short-term daily operational sea ice regional forecasting. Remote Sensing14(22), 5837 (2022)

    2022

  30. [37]

    Supervised sequence labelling with recurrent neural networks, 37–45 (2012)

    Graves, A., Graves, A.: Long short-term memory. Supervised sequence labelling with recurrent neural networks, 37–45 (2012)

    2012

  31. [38]

    Advances in neural information processing systems30(2017)

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems30(2017)

    2017

  32. [39]

    Quarterly Journal of the Royal Meteorological Society146(730), 1999–2049 (2020)

    Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hor´ anyi, A., Mu˜ noz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,et al.: The era5 global reanal- ysis. Quarterly Journal of the Royal Meteorological Society146(730), 1999–2049 (2020)

    2020

  33. [40]

    Ocean science15(3), 779–808 (2019)

    Zuo, H., Balmaseda, M.A., Tietsche, S., Mogensen, K., Mayer, M.: The ecmwf operational ensemble reanalysis–analysis system for ocean and sea ice: a descrip- tion of the system and assessment. Ocean science15(3), 779–808 (2019)

    2019

  34. [41]

    arXiv preprint arXiv:2312.00752 (2023)

    Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)

    2023 arXiv

  35. [42]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: Efficient chan- nel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534–11542 (2020)

    2020

  36. [44]

    Nature communications5(1), 4646 (2014)

    Kim, B.-M., Son, S.-W., Min, S.-K., Jeong, J.-H., Kim, S.-J., Zhang, X., Shim, T., Yoon, J.-H.: Weakening of the stratospheric polar vortex by arctic sea-ice loss. Nature communications5(1), 4646 (2014)

    2014

  37. [45]

    Journal of Geophysical Research: Atmospheres129(15), 2023–040571 (2024) 30

    Xu, M., Screen, J.A., Tian, W., Zhang, J., Zhang, C., Yu, H.: Influence of regional sea ice loss on the arctic stratospheric polar vortex. Journal of Geophysical Research: Atmospheres129(15), 2023–040571 (2024) 30

    2024

  38. [46]

    Geophysical Research Letters44(10), 4953–4964 (2017)

    Bushuk, M., Msadek, R., Winton, M., Vecchi, G.A., Gudgel, R., Rosati, A., Yang, X.: Skillful regional prediction of arctic sea ice on seasonal timescales. Geophysical Research Letters44(10), 4953–4964 (2017)

    2017

  39. [47]

    Geophysical Research Letters 38(18) (2011)

    Blanchard-Wrigglesworth, E., Bitz, C., Holland, M.: Influence of initial conditions and climate forcing on predicting arctic sea ice. Geophysical Research Letters 38(18) (2011)

    2011

  40. [48]

    Journal of Climate31(23), 9771–9786 (2018)

    Ordo˜ nez, A.C., Bitz, C.M., Blanchard-Wrigglesworth, E.: Processes controlling arctic and antarctic sea ice predictability in the community earth system model. Journal of Climate31(23), 9771–9786 (2018)

    2018

  41. [49]

    Geophysical Research Letters45(18), 9731–9738 (2018)

    Zampieri, L., Goessling, H.F., Jung, T.: Bright prospects for arctic sea ice predic- tion on subseasonal time scales. Geophysical Research Letters45(18), 9731–9738 (2018)

    2018

  42. [50]

    Monthly Weather Review149(6), 1673–1693 (2021)

    Liu, Y., Bogaardt, L., Attema, J., Hazeleger, W.: Extended-range arctic sea ice forecast with convolutional long short-term memory networks. Monthly Weather Review149(6), 1673–1693 (2021)

    2021

  43. [51]

    Frontiers in Marine Science9, 860403 (2022)

    Wei, J., Hang, R., Luo, J.-J.: Prediction of pan-arctic sea ice using attention-based lstm neural networks. Frontiers in Marine Science9, 860403 (2022)

    2022

  44. [52]

    Geophysical research letters33(23) (2006)

    Holland, M.M., Bitz, C.M., Tremblay, B.: Future abrupt reductions in the summer arctic sea ice. Geophysical research letters33(23) (2006)

    2006

  45. [53]

    Climatic change 110, 1005–1027 (2012)

    Stroeve, J.C., Serreze, M.C., Holland, M.M., Kay, J.E., Malanik, J., Barrett, A.P.: The arctic’s rapidly shrinking sea ice cover: a research synthesis. Climatic change 110, 1005–1027 (2012)

    2012

  46. [54]

    Journal of Advances in Modeling Earth Systems11(12), 4147–4166 (2019)

    Kimmritz, M., Counillon, F., Smedsrud, L.H., Bethke, I., Keenlyside, N., Ogawa, F., Wang, Y.: Impact of ocean and sea ice initialisation on seasonal prediction skill in the arctic. Journal of Advances in Modeling Earth Systems11(12), 4147–4166 (2019)

    2019

  47. [55]

    Journal of Advances in Modeling Earth Systems11(7), 2177–2213 (2019)

    Voldoire, A., Saint-Martin, D., S´ en´ esi, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Gu´ er´ emy, J.-F., Michou, M., Moine, M.-P.,et al.: Evaluation of cmip6 deck experiments with cnrm-cm6-1. Journal of Advances in Modeling Earth Systems11(7), 2177–2213 (2019)

    2019

  48. [56]

    Monthly Weather Review141(4), 1375–1394 (2013)

    Wang, W., Chen, M., Kumar, A.: Seasonal prediction of arctic sea ice extent from a coupled dynamical forecast system. Monthly Weather Review141(4), 1375–1394 (2013)

    2013

  49. [57]

    Journal of climate27(6), 2185–2208 (2014) 31

    Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-y., Iredell, M.,et al.: The ncep climate forecast system version 2. Journal of climate27(6), 2185–2208 (2014) 31

    2014

  50. [58]

    In: 44th NOAA Annual Climate Diagnostics and Prediction Workshop, Durham, NC, NOAA, pp

    Liu, Y., Wang, W., Kumar, A., Collow, T.: Assessment of cpc sea ice initializa- tion system (csis) and cpc weekly experimental sea ice forecasts. In: 44th NOAA Annual Climate Diagnostics and Prediction Workshop, Durham, NC, NOAA, pp. 74–77 (2019)

    2019

  51. [59]

    Performance evaluations, improvement requirements, development updates, 39 (2020)

    Collow, T.W., Liu, Y., Wang, W., Kumar, A., DeWitt, D.: Develop improved seasonal and week 3/4 sea ice outlook. Performance evaluations, improvement requirements, development updates, 39 (2020)

    2020

  52. [60]

    Weather and Forecasting 35(4), 1317–1343 (2020)

    Lin, H., Merryfield, W.J., Muncaster, R., Smith, G.C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V.V.,et al.: The canadian seasonal to interannual prediction system version 2 (cansipsv2). Weather and Forecasting 35(4), 1317–1343 (2020)

    2020

  53. [61]

    2: description and validation of a new seamless earth system prediction model

    Hazeleger, W., Wang, X., Severijns, C., S ¸tef˘ anescu, S., Bintanja, R., Sterl, A., Wyser, K., Semmler, T., Yang, S., Hurk, B.,et al.: Ec-earth v2. 2: description and validation of a new seamless earth system prediction model. Climate dynamics 39, 2611–2629 (2012)

    2012

  54. [62]

    Geoscientific Model Development12(3), 1087–1117 (2019)

    Johnson, S.J., Stockdale, T.N., Ferranti, L., Balmaseda, M.A., Molteni, F., Mag- nusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G.,et al.: Seas5: the new ecmwf seasonal forecast system. Geoscientific Model Development12(3), 1087–1117 (2019)

    2019

  55. [63]

    Weather and Forecasting36(5), 1759–1778 (2021)

    Li, J., Bao, Q., Liu, Y., Wu, G., Wang, L., He, B., Wang, X., Yang, J., Wu, X., Shen, Z.: Dynamical seasonal prediction of tropical cyclone activity using the fgoals-f2 ensemble prediction system. Weather and Forecasting36(5), 1759–1778 (2021)

    2021

  56. [64]

    Journal of Geophysical Research: Oceans118(9), 4514–4524 (2013)

    Qiao, F., Song, Z., Bao, Y., Song, Y., Shu, Q., Huang, C., Zhao, W.: Development and evaluation of an earth system model with surface gravity waves. Journal of Geophysical Research: Oceans118(9), 4514–4524 (2013)

    2013

  57. [65]

    Science China Earth Sciences 59, 484–494 (2016)

    Chen, H., Yin, X., Bao, Y., Qiao, F.: Ocean satellite data assimilation experiments in fio-esm using ensemble adjustment kalman filter. Science China Earth Sciences 59, 484–494 (2016)

    2016

  58. [66]

    Acta Oceanologica Sinica40, 65–75 (2021)

    Shu, Q., Qiao, F., Liu, J., Song, Z., Chen, Z., Zhao, J., Yin, X., Song, Y.: Arctic sea ice concentration and thickness data assimilation in the fio-esm climate forecast system. Acta Oceanologica Sinica40, 65–75 (2021)

    2021

  59. [67]

    Geophysical Research Letters 41(14), 5208–5215 (2014)

    Msadek, R., Vecchi, G.A., Winton, M., Gudgel, R.G.: Importance of initial condi- tions in seasonal predictions of arctic sea ice extent. Geophysical Research Letters 41(14), 5208–5215 (2014)

    2014

  60. [68]

    Journal of Climate 35(13), 4207–4231 (2022)

    Bushuk, M., Zhang, Y., Winton, M., Hurlin, B., Delworth, T., Lu, F., Jia, L., Zhang, L., Cooke, W., Harrison, M.,et al.: Mechanisms of regional arctic sea 32 ice predictability in two dynamical seasonal forecast systems. Journal of Climate 35(13), 4207–4231 (2022)

    2022

  61. [69]

    Journal of Climate 35(13), 4233–4252 (2022)

    Zhang, Y.-F., Bushuk, M., Winton, M., Hurlin, B., Delworth, T., Harrison, M., Jia, L., Lu, F., Rosati, A., Yang, X.: Subseasonal-to-seasonal arctic sea ice forecast skill improvement from sea ice concentration assimilation. Journal of Climate 35(13), 4233–4252 (2022)

    2022

  62. [70]

    In: EGU General Assembly Conference Abstracts, pp

    Molod, A.: The gmao high resolution coupled model and assimilation system for seasonal prediction. In: EGU General Assembly Conference Abstracts, pp. 21–12759 (2021)

    2021

  63. [71]

    Monthly Weather Review131(5), 845–861 (2003)

    Zhang, J., Rothrock, D.A.: Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Monthly Weather Review131(5), 845–861 (2003)

    2003

  64. [72]

    Geophysical Research Letters35(8) (2008)

    Zhang, J., Steele, M., Lindsay, R., Schweiger, A., Morison, J.: Ensemble 1-year predictions of arctic sea ice for the spring and summer of 2008. Geophysical Research Letters35(8) (2008)

    2008

  65. [73]

    Journal of Climate30(15), 5729–5753 (2017)

    Cassano, J.J., DuVivier, A., Roberts, A., Hughes, M., Seefeldt, M., Brunke, M., Craig, A., Fisel, B., Gutowski, W., Hamman, J.,et al.: Development of the regional arctic system model (rasm): Near-surface atmospheric climate sensitivity. Journal of Climate30(15), 5729–5753 (2017)

    2017

  66. [74]

    Climate Dynamics51, 1585–1603 (2018)

    Barth´ elemy, A., Goosse, H., Fichefet, T., Lecomte, O.: On the sensitivity of antarctic sea ice model biases to atmospheric forcing uncertainties. Climate Dynamics51, 1585–1603 (2018)

    2018

  67. [75]

    Polar Geography44(4), 297–314 (2021)

    Horvath, S., Stroeve, J., Rajagopalan, B.: A linear mixed effects model for seasonal forecasts of arctic sea ice retreat. Polar Geography44(4), 297–314 (2021)

    2021

  68. [76]

    Frontiers in Marine Science10, 1260047 (2023)

    Lin, Y., Yang, Q., Li, X., Yang, C.-Y., Wang, Y., Wang, J., Liu, J., Chen, S., Liu, J.: Optimization of the k-nearest-neighbors model for summer arctic sea ice prediction. Frontiers in Marine Science10, 1260047 (2023)

    2023

  69. [77]

    Geophysical Research Letters50(8), 2022–102115 (2023)

    Zeng, J., Yang, Q., Li, X., Yuan, X., Bushuk, M., Chen, D.: Reducing the spring barrier in predicting summer arctic sea ice concentration. Geophysical Research Letters50(8), 2022–102115 (2023)

    2023

  70. [78]

    In: 2021 IEEE International Conference on Big Data (Big Data), pp

    Kim, E., Kruse, P., Lama, S., Bourne, J., Hu, M., Ali, S., Huang, Y., Wang, J.: Multi-task deep learning based spatiotemporal arctic sea ice forecasting. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 1847–1857 (2021). IEEE

    2021

  71. [79]

    Oxford Uni- versity Press, ??? (2006)

    Dool, H.: Empirical Methods in Short-Term Climate Prediction. Oxford Uni- versity Press, ??? (2006). https://doi.org/10.1093/oso/9780199202782.001.0001 . 33 https://doi.org/10.1093/oso/9780199202782.001.0001

    2006

  72. [80]

    The Cryosphere 17(7), 2965–2991 (2023)

    Finn, T.S., Durand, C., Farchi, A., Bocquet, M., Chen, Y., Carrassi, A., Dansereau, V.: Deep learning subgrid-scale parametrisations for short-term fore- casting of sea-ice dynamics with a maxwell elasto-brittle rheology. The Cryosphere 17(7), 2965–2991 (2023)

    2023

  73. [81]

    Journal of Advances in Modeling Earth Systems16(10), 2024–004395 (2024)

    Finn, T.S., Durand, C., Farchi, A., Bocquet, M., Rampal, P., Carrassi, A.: Gen- erative diffusion for regional surrogate models from sea-ice simulations. Journal of Advances in Modeling Earth Systems16(10), 2024–004395 (2024)

    2024

  74. [82]

    Journal of Advances in Modeling Earth Systems15(10), 2023–003757 (2023)

    Gregory, W., Bushuk, M., Adcroft, A., Zhang, Y., Zanna, L.: Deep learning of systematic sea ice model errors from data assimilation increments. Journal of Advances in Modeling Earth Systems15(10), 2023–003757 (2023)

    2023

  75. [83]

    Geophysical Research Letters51(3), 2023–106776 (2024)

    Gregory, W., Bushuk, M., Zhang, Y., Adcroft, A., Zanna, L.: Machine learning for online sea ice bias correction within global ice-ocean simulations. Geophysical Research Letters51(3), 2023–106776 (2024)

    2024

  76. [84]

    Journal of Geophysical Research: Atmospheres89(D4), 5355–5369 (1984)

    Cavalieri, D.J., Gloersen, P., Campbell, W.J.: Determination of sea ice parameters with the nimbus 7 smmr. Journal of Geophysical Research: Atmospheres89(D4), 5355–5369 (1984)

    1984

  77. [85]

    Journal of Geophysical Research: Oceans91(C1), 975– 994 (1986)

    Comiso, J.C.: Characteristics of arctic winter sea ice from satellite multispectral microwave observations. Journal of Geophysical Research: Oceans91(C1), 975– 994 (1986)

    1986

  78. [86]

    Copernicus Climate Change Service (C3S) Climate Data Store (CDS)10, 252–266 (2019)

    Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor´ anyi, A., Mu˜ noz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,et al.: Era5 monthly averaged data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)10, 2...

    2019

  79. [87]

    Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2019)

    Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor´ anyi, A., Mu˜ noz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., et al.: Era5 monthly averaged data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2019)

    2019

  80. [88]

    NASA National Snow and Ice Data Center Distributed Active Archive Center (2022)

    DiGirolamo, N., Parkinson, C., Cavalieri, D., Gloersen, P., Zwally, H.: Sea ice concentrations from nimbus-7 smmr and dmsp ssm/i-ssmis passive microwave data, version 2. NASA National Snow and Ice Data Center Distributed Active Archive Center (2022)

    2022

  81. [89]

    arXiv preprint arXiv:2401.10166 (2024)

    Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., Liu, Y.: Vmamba: Visual state space model. arXiv preprint arXiv:2401.10166 (2024)

    2024 arXiv

  82. [90]

    arXiv preprint 34 arXiv:2404.03425 (2024)

    Chen, H., Song, J., Han, C., Xia, J., Yokoya, N.: Changemamba: Remote sens- ing change detection with spatio-temporal state space model. arXiv preprint 34 arXiv:2404.03425 (2024)

    2024 arXiv

  83. [91]

    arXiv preprint arXiv:2403.02148 (2024)

    Chen, T., Tan, Z., Gong, T., Chu, Q., Wu, Y., Liu, B., Ye, J., Yu, N.: Mim- istd: Mamba-in-mamba for efficient infrared small target detection. arXiv preprint arXiv:2403.02148 (2024)

    2024 arXiv

  84. [92]

    IEEE Geoscience and Remote Sensing Letters (2024)

    Ma, X., Zhang, X., Pun, M.-O.: Rs 3 mamba: Visual state space model for remote sensing image semantic segmentation. IEEE Geoscience and Remote Sensing Letters (2024)

    2024

  85. [93]

    arXiv preprint arXiv:2403.05246 (2024)

    Liao, W., Zhu, Y., Wang, X., Pan, C., Wang, Y., Ma, L.: Lightm-unet: Mamba assists in lightweight unet for medical image segmentation. arXiv preprint arXiv:2403.05246 (2024)

    2024 arXiv

  86. [94]

    IEEE Geoscience and Remote Sensing Letters (2024)

    Chen, K., Chen, B., Liu, C., Li, W., Zou, Z., Shi, Z.: Rsmamba: Remote sensing image classification with state space model. IEEE Geoscience and Remote Sensing Letters (2024)

    2024

  87. [95]

    arXiv preprint arXiv:2403.03849 (2024)

    Yue, Y., Li, Z.: Medmamba: Vision mamba for medical image classification. arXiv preprint arXiv:2403.03849 (2024)

    2024 arXiv

  88. [96]

    arXiv preprint arXiv:1607.06450 (2016)

    Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)

    2016 arXiv

  89. [97]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    2017

  90. [98]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recogni- tion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    2016

  91. [99]

    arXiv preprint arXiv:1412.6980 (2014) 35

    Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014) 35

    2014 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.