Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for Local Climate Zone Classification

Dongli Xu; Guangjun He; Haiyan Lan; Hongning Liu; Jian Guan; Mingjie Xie; Pengming Feng; Shujun Li; Xuanjia Zhao

REVIEW 5 major objections 6 minor 20 references

Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for Local Climate Zone Classification

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A band-prompting framework claims to improve SAR and multispectral fusion for local climate zone classification by aligning textual descriptions of each band group with image features, raising accuracy by up to 7.40 percentage points over…

desk verdict Useful fusion idea with large reported gains, but the paper never isolates whether the prompt text itself or just the added contrastive branch drives the improvement. read the letter →

arxiv 2412.18235 v1 pith:R2QHT2ZX submitted 2024-12-24 cs.CV

Haiyan Lan , Shujun Li , Mingjie Xie , Xuanjia Zhao , Hongning Liu , Pengming Feng , Dongli Xu , Guangjun He

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Jian Guan

This is my paper · ORCID

classification cs.CV

keywords localclimatezoneclassificationremotesensingdatafusionSARmulti-spectralbandgrouppromptingpromptlearningcontrastiveSo2SatLCZ42

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

This paper claims that adding textual prompts to a SAR and multispectral fusion model improves local climate zone (LCZ) classification. The authors propose BP-LCZ, a framework that writes a short sentence for each band group, aligns those sentences with image features through contrastive learning, and uses a label-aware matrix to avoid treating same-class samples as negatives. Applied to two fusion baselines, the framework raises overall accuracy by 2.64 and 7.40 percentage points and the Kappa coefficient by 2.81 and 8.01 points. If true, this gives a cheap and interpretable way to improve urban land-cover mapping from satellite data.

What carries the argument

The central mechanism is the band group prompting (BGP) strategy, which generates a prompt of the form 'a photo of a {class} with {group} bands' for each of seven band groups and aligns image features with text features via contrastive learning. The multivariate supervised matrix (MSM) constructs a binary matrix $W_{j,k}=1$ when samples $j$ and $k$ share a label, and uses it as the target for a binary cross-entropy contrastive loss, so that all same-class pairs in a batch are treated as positives. The fused image features are a concatenation of the per-group image features, and the total loss combines a cross-entropy classification loss with a weighted contrastive loss.

What would settle it

Replace the extended class descriptions in Table I with bare class names (e.g., 'water' instead of the full sentence) and replace the band-group words with random letter strings, keeping all training hyperparameters fixed; if the overall accuracy does not drop from the reported 86.69%, then the semantic content of the prompts is not load-bearing.

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Extended reading notes

Core claim

The central claim is that band group prompting (BGP) and a multivariate supervised matrix (MSM) training strategy together let a model fuse SAR and multispectral data more effectively for LCZ classification. The paper reports that integrating BP-LCZ into the EB-CNN baseline raises overall accuracy from 79.29% to 86.69% and Kappa from 77.39% to 85.40%, while integrating it into ExViT raises accuracy from 77.21% to 79.85% and Kappa from 75.10% to 77.91%. The authors attribute the gain to the textual prompts, which encode both the physical meaning of each band group (e.g., 'vh', 'vegetation red edge') and the semantic content of the 17 LCZ classes, and to the MSM loss, which replaces the single positive diagonal of standard contrastive learning with all same-label pairs in a batch.

Load-bearing premise

The handwritten text descriptions of band groups and classes must carry the information that improves the model; if replacing them with random words does not change accuracy, then the prompt content is not doing the work.

Editorial extensions

If this is right

The BGP strategy can be applied to any fusion architecture that already processes band groups separately, since the textual prompts attach per group rather than to the whole image.
The MSM training strategy should transfer to other contrastive learning settings where multiple samples in a batch share a label, reducing the false-negative problem beyond LCZ classification.
Because the prompts are human-readable, the model's fusion decisions are partly inspectable: one can see which band-group words steer which categories in the similarity matrix.
The larger gain on EB-CNN than on ExViT suggests that lighter baseline architectures benefit more from the extra text-grounded supervision, which may guide where to apply the method in practice.
The reported improvements depend on the dataset split and fixed hyperparameters, so reproducing the exact numbers requires following the paper's setup of 1306 samples per class for training and 12117 for testing.

Reading between the lines

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

The improvement might partly come from the added text encoder and the extra contrastive loss rather than from the semantic content of the prompts; ablating with random or placeholder text would separate these effects.
The paper shows extended descriptions for only 3 of the 17 LCZ classes, so the quality and consistency of the remaining 14 prompts are untested and could be a source of sensitivity to wording.
The prompt template 'a photo of a ...' is borrowed from natural-image vision-language models, and remote sensing patches may need a different template to fully capture band physics.
The authors acknowledge domain shift from geographic variations, so the gains may shrink when the model is applied to cities whose land cover patterns differ from the training distribution.

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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

Alex,

Quick take: this is a competent empirical paper with a genuinely sensible combination of ideas, and the gains on So2Sat LCZ42 are large. But the central mechanism—that the semantic content of the hand-written prompts guides the fusion—is not actually tested. The ablation compares EB-CNN without the text branch against EB-CNN with the full text branch and contrastive loss. That adds parameters and an auxiliary objective at the same time, so you can't attribute the 6.86-point OA jump to the prompt wording.

What's new and good: the per-band-group textual prompts are a reasonable extension of band grouping, and the multivariate supervised matrix (treating same-class off-diagonal pairs as positives in the image-text similarity matrix) is a simple fix to a real CLIP-style limitation. The experimental setup on a standard benchmark with standard baselines is clean, and the reported numbers are meaningful if they reproduce. The authors clearly know the LCZ fusion literature and cite their own earlier EB-CNN work appropriately—that's not a problem.

Soft spots, in proportion: first, no code and only one seed. For a paper claiming a 7.4-point improvement, I'd want at least three seeds and a significance test. Second, only three of the seventeen class descriptions are shown, so I can't tell whether the results are sensitive to specific wording. Third, and most importantly, the paper never states whether the text encoder is initialized with a pretrained language model. If it's trained from scratch, then the tokens in "a photo of a bodies of water such as rivers..." carry no semantic content to the model; the prompt is just a learned class/group embedding. In that case the improvement is likely from the contrastive objective and added capacity, not from the physical attributes the abstract claims. That's a fixable flaw, not a fatal one. A control with random prompts or random tokens would settle it.

The math and losses are standard and the pipeline is clearly specified. There's no circularity: accuracy is measured against a fixed benchmark and the method isn't fit to the test set. I'd send this to peer review, but I'd require the random-prompt control and multi-seed results before acceptance. Worth a reading-group discussion on ablation design.

Verdict: deserves a serious referee and a conditional accept path.

Referee Report

5 major / 6 minor

Summary. The paper proposes BP-LCZ, a framework for local climate zone (LCZ) classification from SAR and multispectral imagery. It consists of (i) a band-grouping step that splits SAR and multispectral channels into seven groups, (ii) a Band Group Prompting (BGP) strategy that aligns image features from each group with text prompts via a contrastive loss, and (iii) a Multivariate Supervised Matrix (MSM) training strategy that treats all same-class pairs in a batch as positives. Experiments on So2Sat LCZ42 report OA/Kappa improvements when BP-LCZ is added to ExViT and EB-CNN baselines, with ablations showing +6.86% OA from BGP and +0.54% from MSM.

Significance. If the reported gains are real and causally attributable to the textual prompts, BP-LCZ would be a simple and potentially valuable technique for guided multimodal fusion in remote sensing. The manuscript clearly specifies the loss functions and architecture, and the problem is relevant. However, the experimental evidence provided is too thin to establish the central mechanistic claim: the BGP ablation is confounded with added capacity, no control for prompt content is reported, and all results come from a single seed. These issues can be addressed experimentally, so the work is promising but requires major revision.

major comments (5)

[Section II.B.2, Table III] The BGP ablation in Table III adds the complete text-encoder branch and the contrastive loss Lcon along with the hand-written prompts, so the observed +6.86% OA gain is a joint effect of three changes: extra parameters, an auxiliary loss, and prompt content. Since Section III.A does not state whether the text encoder Et is pretrained, it is possible that the prompt tokens are random embeddings and the improvement is entirely due to the added capacity and the auxiliary objective. The paper's central claim—that textual prompts guide the model in learning physical attributes—requires a control where the prompts are replaced by random tokens or random class/group names while keeping the same text-encoder architecture and loss; without such a control, the claim is unsupported.
[Section III.A] The initialization and architecture of the text encoder Et are not specified. If Et is trained from scratch, the semantic content of the prompts is irrelevant because the tokens are arbitrary indices; if Et is initialized from a pretrained language model, the exact wording matters, and Table I shows only 3 of 17 class descriptions, which is insufficient for reproducibility and for assessing sensitivity to prompt phrasing. Please state the text-encoder initialization and provide the complete prompt template for all 17 classes in an appendix.
[Tables II and III] All experiments are reported from a single run with a fixed random seed (47). No standard deviation, confidence interval, or significance test is provided. The claimed improvements, particularly the +7.40% OA for EB-CNN, could be within run-to-run variability for contrastive training; the conclusion's statement that the results demonstrate 'robustness' (Section IV) is therefore not supported. Reporting at least five runs with mean and standard deviation, along with a paired significance test, is necessary to substantiate the central performance claim.
[Section III.A] The dataset split description is ambiguous: the sentence 'we reorganized the dataset for the experiments from the training set' suggests that the test set may be a subset of the original training set rather than the standard So2Sat LCZ42 test split. Because Table II compares against published baselines, an inconsistent split would render the comparison invalid. Please clarify the exact composition of the training and test sets, and if a new split is introduced, re-train all baselines under the same protocol.
[Section III.B, Table II] The comparison between EB-CNN and EB-CNN (BP-LCZ) does not control for model capacity, since the latter includes an additional text encoder and projection heads. Even though Table III ablates BGP on the baseline, that ablation still adds the entire text-branch; it does not separate the benefit of prompt content from the benefit of additional parameters. A capacity-matched control (e.g., an auxiliary branch with the same number of parameters but random prompts) would clarify whether the improvement is due to the semantic prompts or simply to a larger model.

minor comments (6)

[Table I] Table I provides only three example class descriptions; the remaining 14 should be included for reproducibility.
[Equation (11)] Equation (11) contains a typo: 'cross-entroy' should be 'cross-entropy'.
[Section I] In Section I, 'multimodel remote sensing data' should be 'multimodal remote sensing data'.
[Equations (3)-(4)] The weighting coefficient alpha in Eqs. (3)-(4) is presented without justification or sensitivity analysis; please clarify its role and report an ablation or at least a brief discussion.
[Fig. 3] The t-SNE visualization in Fig. 3 is qualitative; consider adding a quantitative cluster-quality metric to support the claim of better category separation.
[Reference [14]] The reference to the So2Sat LCZ42 dataset [14] cites an arXiv preprint; the published version (IEEE Transactions on Geoscience and Remote Sensing, 2020) should be cited.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the gains are measured on a fixed benchmark and the method is not fit to the test set; self-citations are prior baselines used as transparent comparisons, not as load-bearing proof.

full rationale

The paper reports empirical classification results on the fixed So2Sat LCZ42 benchmark, and its central claim is that adding the BGP and MSM components improves OA and Kappa over the EB-CNN and ExViT baselines (Table II). This is not circular: the gains are measured on a held-out test set, the method is not fitted to test labels, and the baseline architectures are fixed, published models. The band-grouping design follows the authors' prior work [5] and [13], and the comparison set includes the authors' own EB-CNN method, but these are used as transparent external baselines rather than as evidence that the new components work; no equation equates the predicted improvement to the input prompts or to a self-citation. A limitation is that the BGP ablation adds the whole text-encoder branch plus the contrastive loss, and no random-prompt control isolates whether the prompt content matters, so the mechanism attributed to physical/semantic prompts is not fully isolated; however, that is an experimental-design and correctness concern, not a circularity of the derivation. No self-definitional, fitted-input-called-prediction, uniqueness-imported, ansatz-smuggled, or renaming step is present.

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

The central claim rests on a handful of domain assumptions about band-group validity, prompt effectiveness, and dataset representativeness, plus manually chosen hyperparameters. No new physical entities are introduced. The method is empirical, so the ledger mainly records hand-set weights and unverified design choices rather than derived constants.

free parameters (4)

alpha (similarity fusion weight) = 0.25
Weight for summing per-band-group similarity matrices in Eq. 3-4; set by hand, no sensitivity analysis given.
beta (loss balance weight) = 2
Weight balancing classification and contrastive loss in Eq. 10; set by hand, no sensitivity analysis given.
Prompt template and class descriptions = hand-written
The prompts in Section II.B and Table I are manually designed; only 3 of 17 class descriptions are shown, and the method's benefit depends on them.
Training hyperparameters (learning rate, batch size, epochs, seed) = 1e-4, 32, 200, 47
Standard training choices; fixed seed gives a single run with no error bars.

assumptions (4)

domain assumption The 7-group split of SAR and multispectral bands (3 SAR, 4 optical) is a physically valid and useful decomposition.
Section II.A adopts the grouping from [5] and [13]; no ablation tests alternate groupings.
domain assumption Textual prompts describing class semantics and band names can be aligned with image features in a shared embedding space.
Section II.B assumes CLIP-style contrastive training transfers semantic knowledge; no evidence that prompts are the causal factor vs. added parameters.
domain assumption The So2Sat LCZ42 split used here is representative and free of leakage.
Section III.A re-splits the training set randomly per class; the test set is stated to have no intersection, but exact split code is not provided.
standard math Binary cross-entropy on a supervised similarity matrix provides a valid contrastive objective.
Section II.C uses BCE; this is a standard loss, but the off-diagonal positive handling is borrowed from supervised contrastive learning without citation.

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Cite this review

Pith. "Pith review of Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for Local Climate Zone Classification." pith.science (2026). https://pith.science/paper/R2QHT2ZX

@misc{pith2026241218235,
  author       = {Pith},
  title        = {Pith review of: Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for Local Climate Zone Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R2QHT2ZX}},
  note         = {Machine review of arXiv:2412.18235}
}

read the original abstract

Local climate zone (LCZ) classification is of great value for understanding the complex interactions between urban development and local climate. Recent studies have increasingly focused on the fusion of synthetic aperture radar (SAR) and multi-spectral data to improve LCZ classification performance. However, it remains challenging due to the distinct physical properties of these two types of data and the absence of effective fusion guidance. In this paper, a novel band prompting aided data fusion framework is proposed for LCZ classification, namely BP-LCZ, which utilizes textual prompts associated with band groups to guide the model in learning the physical attributes of different bands and semantics of various categories inherent in SAR and multi-spectral data to augment the fused feature, thus enhancing LCZ classification performance. Specifically, a band group prompting (BGP) strategy is introduced to align the visual representation effectively at the level of band groups, which also facilitates a more adequate extraction of semantic information of different bands with textual information. In addition, a multivariate supervised matrix (MSM) based training strategy is proposed to alleviate the problem of positive and negative sample confusion by completing the supervised information. The experimental results demonstrate the effectiveness and superiority of the proposed data fusion framework.

Figures

Figures reproduced from arXiv: 2412.18235 by the authors.

**Figure 1.** The overall framework of the proposed method, which consists of four steps, i.e., band grouping, band group prompting [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 3.** The t-SNE visualization of EB-CNN and EB-CNN [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 2.** Confusion matrix of the classification results from EB [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Reference graph

Works this paper leans on

20 extracted references · 19 canonical work pages

[1]

Optimizing Local Climate Zones to Mitigate Urban Heat Island Effect in Human Settlements,

Jun Yang, Yichen Wang, Chunliang Xiu, Xiangming Xiao, Jianhong Xia, and Cui Jin, “Optimizing Local Climate Zones to Mitigate Urban Heat Island Effect in Human Settlements,” Journal of Cleaner Production , vol. 275, pp. 123767, 2020

work page 2020
[2]

Mapping Local Climate Zones for Cities: A Large Review,

Fan Huang, Sida Jiang, Wenfeng Zhan, Benjamin Bechtel, Zihan Liu, Matthias Demuzere, Yuan Huang, Yong Xu, Lei Ma, Wanjun Xia, et al., “Mapping Local Climate Zones for Cities: A Large Review,” Remote Sensing of Environment, vol. 292, pp. 113573, 2023

work page 2023
[3]

A “Local Climate Zone

Narein GR Perera and Rohinton Emmanuel, “A “Local Climate Zone” based Approach to Urban Planning in Colombo, Sri Lanka,” Urban Climate, vol. 23, pp. 188–203, 2018

work page 2018
[4]

The Use of Local Climate Zones in the Urban Environment: A Systematic Review of Data Sources, Methods, and Themes,

Ayman Aslam and Irfan Ahmad Rana, “The Use of Local Climate Zones in the Urban Environment: A Systematic Review of Data Sources, Methods, and Themes,” Urban Climate, vol. 42, pp. 101120, 2022

work page 2022
[5]

Embranchment CNN based Local Climate Zone Classification Using SAR and Multispectral Remote Sensing Data,

Pengming Feng, Youtian Lin, Jian Guan, Yan Dong, Guangjun He, Zhenghuan Xia, and Huifeng Shi, “Embranchment CNN based Local Climate Zone Classification Using SAR and Multispectral Remote Sensing Data,” in Proc. International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2019, pp. 6344–6347

work page 2019
[6]

Deep Learning-based Local Climate Zone Classification Using Sentinel-1 SAR and Sentinel-2 Multispectral Imagery,

Lin Zhou, Zhenfeng Shao, Shugen Wang, and Xiao Huang, “Deep Learning-based Local Climate Zone Classification Using Sentinel-1 SAR and Sentinel-2 Multispectral Imagery,” Geo-Spatial Information Science, vol. 25, no. 3, pp. 383–398, 2022

work page 2022
[7]

On the Fusion Strategies of Sentinel-1 and Sentinel-2 Data for Local Climate Zone Classification,

Jakob Gawlikowski, Michael Schmitt, Anna Kruspe, and Xiao Xiang Zhu, “On the Fusion Strategies of Sentinel-1 and Sentinel-2 Data for Local Climate Zone Classification,” in Proc. International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2020, pp. 2081–2084

work page 2020
[8]

A Dynamic End-to-End Fusion Filter for Local Climate Zone Classification Using SAR and Multi-Spectrum Remote Sensing Data,

Pengming Feng, Youtian Lin, Guangjun He, Jian Guan, Jin Wang, and Huifeng Shi, “A Dynamic End-to-End Fusion Filter for Local Climate Zone Classification Using SAR and Multi-Spectrum Remote Sensing Data,” in Proc. International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2020, pp. 4231–4234

work page 2020

Show all 20 references

[9]

Synergic Use of SAR and Optical Data for Feature Extraction,

Antonio Mazza, Matteo Ciotola, Giovanni Poggi, and Giuseppe Scarpa, “Synergic Use of SAR and Optical Data for Feature Extraction,” in Proc. International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE, 2023, pp. 2061–2064

2023
[10]

Multimodal Contrastive Training for Visual Representation Learning,

Xin Yuan, Zhe Lin, Jason Kuen, Jianming Zhang, Yilin Wang, Michael Maire, Ajinkya Kale, and Baldo Faieta, “Multimodal Contrastive Training for Visual Representation Learning,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 6995– 7004

2021
[11]

MVP: Multimodality-Guided Visual Pre-Training,

Longhui Wei, Lingxi Xie, Wengang Zhou, Houqiang Li, and Qi Tian, “MVP: Multimodality-Guided Visual Pre-Training,” in Proc. European Conference on Computer Vision (ECCV) . Springer, 2022, pp. 337–353

2022
[12]

Learning Transferable Visual Models from Natural Language Supervision,

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al., “Learning Transferable Visual Models from Natural Language Supervision,” in Proc. International Conference on Machine Lea...

2021
[13]

SAR and Multi-Spectral Data Fusion for Local Climate Zone Classification with Multi-Branch Convolutional Neural Network,

Guangjun He, Zhe Dong, Jian Guan, Pengming Feng, Shichao Jin, and Xueliang Zhang, “SAR and Multi-Spectral Data Fusion for Local Climate Zone Classification with Multi-Branch Convolutional Neural Network,” Remote Sensing, vol. 15, no. 2, pp. 434, 2023

2023
[14]

So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification,

Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu, Yilei Shi, Jian Kang, Lichao Mou, Hossein Bagheri, Matthias H ¨aberle, Yuansheng Hua, Rong Huang, et al., “So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification,” arXiv preprint arXiv:1912.12171 , 2019

1912 arXiv
[15]

Large-Scale Machine Learning with Stochastic Gradient Descent,

L ´eon Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” in Proc. International Conference on Computational Statis- ticsParis France (COMPSTAT), 2010, pp. 177–186

2010
[16]

Deep Residual Learning for Image Recognition,

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Deep Residual Learning for Image Recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778

2016
[17]

Aggregated Residual Transformations for Deep Neural Networks,

Saining Xie, Ross Girshick, Piotr Doll ´ar, Zhuowen Tu, and Kaiming He, “Aggregated Residual Transformations for Deep Neural Networks,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1492–1500

2017
[18]

A ConvNet for the 2020s,

Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie, “A ConvNet for the 2020s,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 11976–11986

2022
[19]

Densely Connected Convolutional Networks,

Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, “Densely Connected Convolutional Networks,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 4700–4708

2017
[20]

Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework,

Jing Yao, Bing Zhang, Chenyu Li, Danfeng Hong, and Jocelyn Chanus- sot, “Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework,” IEEE Transactions on Geoscience and Remote Sensing , 2023

2023

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Optimizing Local Climate Zones to Mitigate Urban Heat Island Effect in Human Settlements,

Jun Yang, Yichen Wang, Chunliang Xiu, Xiangming Xiao, Jianhong Xia, and Cui Jin, “Optimizing Local Climate Zones to Mitigate Urban Heat Island Effect in Human Settlements,” Journal of Cleaner Production , vol. 275, pp. 123767, 2020

work page 2020

[2] [2]

Mapping Local Climate Zones for Cities: A Large Review,

Fan Huang, Sida Jiang, Wenfeng Zhan, Benjamin Bechtel, Zihan Liu, Matthias Demuzere, Yuan Huang, Yong Xu, Lei Ma, Wanjun Xia, et al., “Mapping Local Climate Zones for Cities: A Large Review,” Remote Sensing of Environment, vol. 292, pp. 113573, 2023

work page 2023

[3] [3]

A “Local Climate Zone

Narein GR Perera and Rohinton Emmanuel, “A “Local Climate Zone” based Approach to Urban Planning in Colombo, Sri Lanka,” Urban Climate, vol. 23, pp. 188–203, 2018

work page 2018

[4] [4]

The Use of Local Climate Zones in the Urban Environment: A Systematic Review of Data Sources, Methods, and Themes,

Ayman Aslam and Irfan Ahmad Rana, “The Use of Local Climate Zones in the Urban Environment: A Systematic Review of Data Sources, Methods, and Themes,” Urban Climate, vol. 42, pp. 101120, 2022

work page 2022

[5] [5]

Embranchment CNN based Local Climate Zone Classification Using SAR and Multispectral Remote Sensing Data,

Pengming Feng, Youtian Lin, Jian Guan, Yan Dong, Guangjun He, Zhenghuan Xia, and Huifeng Shi, “Embranchment CNN based Local Climate Zone Classification Using SAR and Multispectral Remote Sensing Data,” in Proc. International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2019, pp. 6344–6347

work page 2019

[6] [6]

Deep Learning-based Local Climate Zone Classification Using Sentinel-1 SAR and Sentinel-2 Multispectral Imagery,

Lin Zhou, Zhenfeng Shao, Shugen Wang, and Xiao Huang, “Deep Learning-based Local Climate Zone Classification Using Sentinel-1 SAR and Sentinel-2 Multispectral Imagery,” Geo-Spatial Information Science, vol. 25, no. 3, pp. 383–398, 2022

work page 2022

[7] [7]

On the Fusion Strategies of Sentinel-1 and Sentinel-2 Data for Local Climate Zone Classification,

Jakob Gawlikowski, Michael Schmitt, Anna Kruspe, and Xiao Xiang Zhu, “On the Fusion Strategies of Sentinel-1 and Sentinel-2 Data for Local Climate Zone Classification,” in Proc. International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2020, pp. 2081–2084

work page 2020

[8] [8]

A Dynamic End-to-End Fusion Filter for Local Climate Zone Classification Using SAR and Multi-Spectrum Remote Sensing Data,

Pengming Feng, Youtian Lin, Guangjun He, Jian Guan, Jin Wang, and Huifeng Shi, “A Dynamic End-to-End Fusion Filter for Local Climate Zone Classification Using SAR and Multi-Spectrum Remote Sensing Data,” in Proc. International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2020, pp. 4231–4234

work page 2020