EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Deep Residual Learning for Image Recognition

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ImageNet classification with deep convolutional neural networks

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Going deeper with convolutions

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ImageNet Large Scale Visual Recognition Challenge

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Rethinking the Inception Architecture for Computer Vision

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A Practical Guide to Splines

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Backpropagation Applied to Handwritten Zip Code Recognition

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Identity mappings in deep residual networks

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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

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Learning Representations of Satellite Images From Metadata Supervision

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Multilabel-Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining

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Cascade Prompt Learning for Vision-Language Model Adaptation

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Frequency-Spatial Complementation: Unified Channel-Specific Style Attack for Cross-Domain Few-Shot Learning

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Parameter Disentanglement for Diverse Representations

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Bias in Deep Neural Networks in Land Use Characterization for International Development

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IndiaSat: A Pixel-Level Dataset for Land-Cover Classification on Three Satellite Systems - Landsat-7, Landsat-8, and Sentinel-2

Bansal, C., Ahlawat, H. O., Jain, M. K., Prakash, O., Mehta, S. A., Singh, D., Baheti, H., Singh, S., & Seth, A. (2021) · ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3460112.3471953 · Cited by 11

Multi-level Feature Extraction for Automated Land Cover Classification using Deep CNN with Long Short-Term Memory Network

Patel, S., Ganatra, N., & Patel, R. (2022) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei53556.2022.9777148 · Cited by 11

Variational Hyperparameter Inference for Few-Shot Learning Across Domains

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Deep Residual SVM: A Hybrid Learning Approach to obtain High Discriminative Feature for Land Use and Land Cover Classification

Kumari, N., & Minz, S. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.124 · Cited by 11

Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation

Islam, A., Lundell, B., Sawhney, H., Sinha, S. N., Morales, P., & Radke, R. J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00558 · Cited by 11

Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision

Bratić, G., Oxoli, D., & Brovelli, M. A. (2023) · Remote Sensing · doi:10.3390/rs15153774 · Cited by 12

Guided Recommendation for Model Fine-Tuning

Li, H., Fowlkes, C. C., Yang, H., Dabeer, O., Tu, Z., & Soatto, S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00354 · Cited by 12

Select and Distill: Selective Dual-Teacher Knowledge Transfer for Continual Learning on Vision-Language Models

Yu, Y.-C., Huang, C.-P., Chen, J. C., Chang, K.-P., Lai, Y.-H., Yang, F.-E., & Wang, Y.-C. F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73347-5_13 · Cited by 12

Land-Use Land-Cover Prediction from Satellite Images using Machine Learning Techniques

Das, T. K., Barik, D. K., & Kumar, K. V. G. R. (2022) · International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) · doi:10.1109/com-it-con54601.2022.9850602 · Cited by 11

Deep Ensembles for Low-Data Transfer LearningPreprint

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Cross-domain few-shot learning with unlabelled dataPreprint

Yao, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07899 · Cited by 15

On the Zero-shot Adversarial Robustness of Vision-Language Models: A Truly Zero-shot and Training-free Approach

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Domain Mapping Network for Remote Sensing Cross-Domain Few-Shot Classification

Lu, X., Gong, T., & Zheng, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3352908 · Cited by 10

Systematic Analysis of Effective Segmentation and Classification for Land Use Land Cover in Hyperspectral Image using Deep Learning Methods: A Review of the State of the Art : Reviewing Deep Learning Techniques for Land Use and Cover in Hyperspectral Images

Yele, V. P., Sedamkar, R. R., & Alegavi, S. (2024) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp61396.2024.10475229 · Cited by 10

A Distributed Conditional Wasserstein Deep Convolutional Relativistic Loss Generative Adversarial Network With Improved Convergence

Roy, A., & Dasgupta, D. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3386500 · Cited by 10

Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification

Li, S., Li, N., Jing, M., Ji, C., & Cheng, L. (2024) · Remote Sensing · doi:10.3390/rs16091501 · Cited by 10

Agroforestry mapping using multi temporal hybrid CNN+LSTM framework with landsat 8 satellite imagery and google earth engine

Jenila Vincent, M., & Varalakshmi, P. (2024) · Environmental Research Communications · doi:10.1088/2515-7620/ad549f · Cited by 10

Selective Vision-Language Subspace Projection for Few-shot CLIP

Zhu, X., Zhu, B., Tan, Y., Wang, S., Hao, Y., & Zhang, H. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680885 · Cited by 10

DataDream: Few-Shot Guided Dataset Generation

Kim, J. M., Bader, J., Alaniz, S., Schmid, C., & Akata, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73209-6_15 · Cited by 10

Developing a forest description from remote sensing: Insights from New Zealand

Pearse, G. D., Jayathunga, S., Camarretta, N., Palmer, M. E., Steer, B. S. C., Watt, M. S., Watt, P., & Holdaway, A. (2024) · Science of Remote Sensing · doi:10.1016/j.srs.2024.100183 · Cited by 10

Generalizable Prompt Learning via Gradient Constrained Sharpness-Aware Minimization

Liu, L., Wang, N., Zhou, D., Liu, D., Yang, X., Gao, X., & Liu, T. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521702 · Cited by 11

A Slim Prompt-Averaged Consistency prompt learning for vision–language model

He, S., Wang, S., & Long, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113011 · Cited by 10

Semantically-Aware Contrastive Learning for multispectral remote sensing images

Stival, L., Torres, R. D. S., & Pedrini, H. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.02.024 · Cited by 10

Progressive Multi-Prompt Learning for Vision-Language Models

Liu, J., Lu, Z., Luo, H., Lu, Z., & Zheng, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3557474 · Cited by 10

CoLeCLIP: Open-Domain Continual Learning via Joint Task Prompt and Vocabulary Learning

Li, Y., Pang, G., Suo, W., Jing, C., Xi, Y., Liu, L., Chen, H., Liang, G., & Wang, P. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3547882 · Cited by 10

Active Learning for Vision-Language Models

Safaei, B., & Patel, V. M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00480 · Cited by 11

DPA: Dual Prototypes Alignment for Unsupervised Adaptation of Vision-Language Models

Ali, E., Silva, S., & Khan, M. H. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00593 · Cited by 13

Model Compression Meets Resolution Scaling for Efficient Remote Sensing Classification

Shinde, T. S. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00133 · Cited by 10

GradCAM-PestDetNet: A deep learning-based hybrid model with explainable AI for pest detection and classification

Vimala, R., Mehrotra, S., Kumar, S., Kamat, P. V., Bongale, A. M., & Kotecha, K. V. (2025) · MethodsX · doi:10.1016/j.mex.2025.103533 · Cited by 10

A review of deep learning based agricultural remote sensing image segmentation

Ren, Q., Wu, Y., Zeng, Q., & Yang, N. (2025) · Journal of Agricultural Engineering · doi:10.4081/jae.2025.1954 · Cited by 10

Segmentation of Building Footprints with Xception and IoUloss

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A Dual-Channel Fully Convolutional Network for Land Cover Classification Using Multifeature Information

Liu, Z.-W., Wang, M., Wang, F., Ji, X., & Meng, Z. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3153287 · Cited by 10

Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine

Desai, G. T., & Gaikwad, A. N. (2022) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.13.10.6 · Cited by 10

Land classification in satellite images by injecting traditional features to CNN models

Aksoy, M. Ç., Sırmaçek, B., & Ünsalan, C. (2023) · Remote Sensing Letters · doi:10.1080/2150704x.2023.2167057 · Cited by 10

Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR

Mahmoud, H., Kurniawan, I. F., Aneiba, A., & Asyhari, A. T. (2024) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-024-01869-3 · Cited by 11

DeepVenom: Persistent DNN Backdoors Exploiting Transient Weight Perturbations in Memories

Cai, K., Chowdhuryy, M. H. I., Zhang, Z., & Yao, F. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00223 · Cited by 12

A Noisy Elephant in the Room: Is Your out-of-Distribution Detector Robust to Label Noise?

Humblot-Renaux, G., Escalera, S., & Moeslund, T. B. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02135 · Cited by 13

SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification

Syrris, V., Pešek, O., & Soille, P. (2020) · Remote Sensing · doi:10.3390/rs12203358 · Cited by 10

Deep Learning Based Land Cover and Crop Type Classification: A Comparative Study

Khan, A. H., Fraz, M. M., & Shahzad, M. (2021) · International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt252288.2021.9441483 · Cited by 10

Classification of multi-spectral data with fine-tuning variants of representative models

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Accelerating Ecological Sciences from Above: Spatial Contrastive Learning for Remote Sensing

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An Open-source Tool for Hyperspectral Image Augmentation in TensorflowPreprint

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Rapidai4Eo: Mono-and Multi-Temporal Deep Learning Models for Updating the Corine land Cover Product

Bhugra, P., Bischke, B., Werner, C., Syrnicki, R., Packbier, C., Helber, P., Şenaras, Ç., Rana, A. S., Davis, T., Keersmaecker, W. D., et al. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9883198 · Cited by 9

A General Transitive Transfer Learning Framework for Cross-Optical Sensor Remote Sensing Image Scene Understanding

Tao, C., Xiao, R., Wang, Y., Qi, J., & Li, H. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3269852 · Cited by 9

Proto-Adapter: Efficient Training-Free CLIP-Adapter for Few-Shot Image Classification

Kato, N., Nota, Y., & Aoki, Y. (2024) · Sensors · doi:10.3390/s24113624 · Cited by 9

Kernel Masked Image Modeling Through the Lens of Theoretical Understanding

Qian, Y., Wang, Y., Zou, J., Lin, J., Pan, Y., Yao, T., Sun, Q., & Mei, T. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3443088 · Cited by 10

Multilingual Vision-Language Pre-training for the Remote Sensing Domain

Silva, J. D., Magalhães, J., Tuia, D., & Martins, B. (2024) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3678717.3691318 · Cited by 12

Unleash the Power of Vision-Language Models by Visual Attention Prompt and Multimodal Interaction

Zhang, W., Wu, L., Zhang, Z., Yu, T., Ma, C., Jin, X., Yang, X., & Zeng, W. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521785 · Cited by 14

Task-to-Instance Prompt Learning for Vision-Language Models at Test Time

Lu, Z., Bai, J., Li, X., Xiao, Z., & Wang, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3546840 · Cited by 11

MIF: Multi-source information fusion for few-shot classification with CLIP

Chen, J., Yuan, H., & Xie, B. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.03.025 · Cited by 9

TakuNet: An Energy-Efficient CNN for Real-Time Inference on Embedded UAV Systems in Emergency Response Scenarios

Rossi, D. D., Borghi, G., & Vezzani, R. (2025) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw65960.2025.00044 · Cited by 9

A Robust Multispectral Reconstruction Network from RGB Images Trained by Diverse Satellite Data and Application in Classification and Detection Tasks

Zhang, X., Peng, Z., Wang, Y., Fan, Y., Fu, T., & Zhang, H. (2025) · Remote Sensing · doi:10.3390/rs17111901 · Cited by 9

Transforming Earth Observation: An Extensive Evaluation of Vision Transformers for Satellite Images‐Based Land Cover Classification

Khan, F. A. (2025) · Expert Systems · doi:10.1111/exsy.70082 · Cited by 9

Enhancing Land Cover Classification: Fuzzy Similarity Approach Versus Random Forest

Bilotta, G., Barrile, V., Bibbò, L., Meduri, G. M., Versaci, M., & Angiulli, G. (2025) · Symmetry · doi:10.3390/sym17060929 · Cited by 9

Remote Sensing Tuning: A Survey

Yin, D., Zhao, T.-F., Fan, D.-P., Shutao Li, A., Du, B., Sun, X., & Hu, S. (2025) · Computational Visual Media · doi:10.26599/cvm.2025.9450490 · Cited by 12

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models

Wang, X., Chen, K., Zhang, J., Chen, J., & Ma, X. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01854 · Cited by 9

A survey of large language models for data challenges in graphs

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Hybrid ConvNeXt-Liquid Neural Network for Satellite Image Classification: A Benchmark on EuroSAT Dataset

Karunasri, G., Martha, S., & Bijalwan, V. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3615076 · Cited by 9

Deep learning framework for land cover and land use classification: five case studies with hyperspectral and RGB imagery

Arain, B., Ali, A. M. A., Alrashdi, I., Sallam, K. M., Radwan, I., & Abdel-Basset, M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11644-1 · Cited by 12

Energy efficiency analysis of Spiking Neural Networks for space applications

Lunghi, P., Silvestrini, S., Dold, D., Meoni, G., Hadjiivanov, A., & Izzo, D. (2025) · Astrodynamics · doi:10.1007/s42064-024-0256-y · Cited by 9

Multispectral-to-RGB Knowledge Distillation for Remote Sensing Image Scene Classification

Shin, H.-K., Uhm, K.-H., Jung, S., & Ko, S.-J. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3245095 · Cited by 9

Transfer Learning in Deep Neural Network for Land Cover Classification

Sadia, A., Uddin, S. M. S., & Islam, R. (2022) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit57492.2022.10054990 · Cited by 9

A large-scale remote sensing scene dataset construction for semantic segmentation

Xu, L., Shi, S., Liu, Y., Zhang, H., Wang, D., Zhang, L., Liang, W., & Chen, H. (2023) · International Journal of Image and Data Fusion · doi:10.1080/19479832.2023.2199005 · Cited by 9

Evaluating the Effect of Land Use Land Cover Changes on Soil Loss Distribution in the Seybouse Basin, Northeastern Algeria

Bouzeria, H., Tachi, S. E., Bouguerra, H., Derdous, O., & Benmamar, S. (2023) · Doklady Earth Sciences · doi:10.1134/s1028334x23600081 · Cited by 9

Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques

Kumari, P., Das, S. K., Kannadasan, B., Sampathila, N., Saravanakumar, C., Anand, R., & Gupta, A. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-8865-3_55 · Cited by 9

Multi-Spectral Band Selection and Spatial Explanations Using XAI Algorithms in Remote Sensing Applications

Τέμενος, Α., Temenos, N., Kaselimi, M., Doulamis, A. D., & Doulamis, N. D. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10282565 · Cited by 9

Performance Comparison of Multispectral Channels for Land Use Classification

Nampally, T., Wu, J., & Dev, S. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10283090 · Cited by 11

Use of Machine Learning on Satellite and Drone Images for Agriculture Application

Tarique, A. H., Khalifa, O. O., Islam, M. R., R, H. K. G., & Singh, A. K. (2023) · International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM) · doi:10.1109/elexcom58812.2023.10370399 · Cited by 9

Feature Adaptation with CLIP for Few-shot Classification

Wu, G., Chen, J., Zhang, W., & Wang, R. (2023) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3595916.3626396 · Cited by 9

Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models

Zheng, K., Wu, W., Feng, R., Zhu, K., Liu, J., Zhao, D., Zha, Z.-J., Chen, W., & Shen, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01071 · Cited by 11

LaViP: Language-Grounded Visual Prompting

Kunananthaseelan, N., Zhang, J., & Harandi, M. T. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28064 · Cited by 10

Generative AI-based Land Cover Classification via Federated Learning CNNs: Sustainable Insights from UAV Imagery

Jockusch, O., Hossain, M. Z., Imteaj, A., & Shahid, A. R. (2024) · IEEE Conference on Technologies for Sustainability · doi:10.1109/sustech60925.2024.10553449 · Cited by 10

Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transfomers

Yang, S., Bai, J., Gao, K., Yang, Y., Li, Y., & Xia, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02306 · Cited by 9

Fine-grained multi-modal prompt learning for vision–language models

Liu, Y., Deng, Y., Liu, A., Liu, Y., & Li, S. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130028 · Cited by 9

Urban region function classification via fusing optical imagery and social media data: A spatio-temporal Transformer interaction approach

Sun, R., Su, X., Yuan, Q., Jiao, H., He, J., & Li, Z. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103140 · Cited by 9

Test-Time Low Rank Adaptation via Confidence Maximization for Zero-Shot Generalization of Vision-Language Models

Imam, R., Gani, H., Huzaifa, M., & Nandakumar, K. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00532 · Cited by 17

Deep Learning and Transfer Learning applied to Sentinel-1 DInSAR and Sentinel-2 optical satellite imagery for change detection

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Neural network surgery: Combining training with topology optimization

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Learning Multi-Granularity Neural Network Encoding Image Classification Using DCNNs for Easter Africa Community Countries

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DRIIS: Research on Image Classification of Art Education System Based on Deep Learning

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