The DOI matches its Crossref record; 36 of the 39 references it cites were found in a registry or an index.
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Abstract
In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus. We present a novel dataset, based on these images that covers 13 spectral bands and is comprised of ten classes with a total of 27 000 labeled and geo-referenced images. Benchmarks are provided for this novel dataset with its spectral bands using state-of-the-art deep convolutional neural networks. An overall classification accuracy of 98.57% was achieved with the proposed novel dataset. The resulting classification system opens a gate toward a number of earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes, and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“the authors of Helber et al. (2019) achieve very high accuracy on EUROSAT, i.e. 98.56% using solely the RGB channels as input to a ResNet50 model.”
From Benchmarking and scaling of deep learning models for land cover image classification (2023) · cites this paper as Helber et al. (2019)
In the citing paper
“Moreover, their experiments show that one can achieve very high accuracy >90% while using only one band making the multispectral information redundant.”
From Benchmarking and scaling of deep learning models for land cover image classification (2023) · cites this paper as Helber et al. (2019)
In the citing paper
“reaching performances on par with Helber et al. [35], where the authors reached a 98.57% of overall accuracy, by using a model based on the ResNet-50.”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [35]
In the citing paper
“The experimental dataset under consideration is the”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [35]
In the citing paper
“the architecture proposed in this article is extremely less complex than the one proposed in [35], since the ResNet-50 is composed of 50 layers, while the proposed one is composed of six layers only: five classical and one quantum.”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [35]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
de Boor, C. (1978) · Applied mathematical sciences · doi:10.1007/978-1-4612-6333-3 · Cited by 12,085
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Research works printed but not linked: 1
- bischke, 0, The multimedia satellite task: Emergency response for flooding events, Proc MediaEval
Cited by
Sorted by citations
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Learning to Prompt for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01653-1 · Cited by 3,304
“Tuning the sentence structure could bring further improvements, e.g., putting “a type of flower” after the class token for Flowers102, keeping only “texture” in the context for DTD, and adding “centered” before “satellite photo” for EuroSAT.”
BackgroundInfluentialJia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B., & Lim, S.-N. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19827-4_41 · Cited by 1,965
Conditional Prompt Learning for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · 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/cvpr52688.2022.01631 · Cited by 2,069
Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.12.010 · Cited by 1,966
CLIP-Adapter: Better Vision-Language Models with Feature Adapters
Gao, P., Geng, S., Zhang, R., Ma, T., Fang, R., Zhang, Y., Li, H., & Qiao, Y. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01891-x · Cited by 1,589
Vision-Language Models for Vision Tasks: A Survey
Zhang, J., Huang, J., Jin, S., & Lu, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3369699 · Cited by 1,051
Cheng, G., Xie, X., Han, J., Guo, L., & Xia, G.-S. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3005403 · Cited by 1,059
MaPLe: Multi-modal Prompt Learning
Khattak, M. U., Rasheed, H., Maaz, M., Khan, S., & Khan, F. 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/cvpr52729.2023.01832 · Cited by 1,125
SpectralGPT: Spectral Remote Sensing Foundation Model
Hong, D., Zhang, B., Li, X., Li, Y., Li, C., Yao, J. T., Yokoya, N., Li, H., Ghamisi, P., Jia, X., et al. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3362475 · Cited by 1,112
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
Song, Y., Wang, T., Cai, P., Mondal, S. K., & Sahoo, J. P. (2023) · ACM Computing Surveys · doi:10.1145/3582688 · Cited by 721
Reproducible Scaling Laws for Contrastive Language-Image Learning
Cherti, M., Beaumont, R., Wightman, R., Wortsman, M., Ilharco, G., Gordon, C., Schuhmann, C., Schmidt, L., & Jitsev, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00276 · Cited by 673
FLAVA: A Foundational Language And Vision Alignment Model
Singh, A., Hu, R., Goswami, V., Couairon, G., Galuba, W., Rohrbach, M., & Kiela, D. (2022) · 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/cvpr52688.2022.01519 · Cited by 678
Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding
Sümbül, G., Charfuelàn, M., Demir, B., & Markl, V. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8900532 · Cited by 623
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
Liu, F., Chen, D., Guan, Z., Zhou, X., Zhu, J., Ye, Q., Fu, L., & Zhou, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3390838 · Cited by 507
TinyViT: Fast Pretraining Distillation for Small Vision Transformers
Wu, K., Zhang, J., Peng, H., Liu, M., Xiao, B., Fu, J., & Yuan, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19803-8_5 · Cited by 521
Vali, A., Comai, S., & Matteucci, M. (2020) · Remote Sensing · doi:10.3390/rs12152495 · Cited by 507
Using satellite imagery to understand and promote sustainable development
Burke, M., Driscoll, A., Lobell, D. B., & Ermon, S. (2021) · Science · doi:10.1126/science.abe8628 · Cited by 518
Sun, Y., Ye, Y., Kang, J. S., Fernández-Beltrán, R., Yifang Ban, A., Li, X., Zhang, B., & Plaza, A. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3231215 · Cited by 467
Tip-Adapter: Training-Free Adaption of CLIP for Few-Shot Classification
Zhang, R., Zhang, W., Fang, R., Gao, P., Li, K., Dai, J., Qiao, Y., & Li, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19833-5_29 · Cited by 510
Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments
Saleem, M. H., Potgieter, J., & Arif, K. M. (2021) · Precision Agriculture · doi:10.1007/s11119-021-09806-x · Cited by 468
MobileNetV2 Model for Image Classification
Dong, K., Zhou, C., Ruan, Y., & Li, Y. (2020) · International Conference on Information Technology and Computer Application (ITCA) · doi:10.1109/itca52113.2020.00106 · Cited by 379
Self-Supervised Learning in Remote Sensing: A review
Wang, Y., Albrecht, C. M., Braham, N. A. A., Mou, L., & Zhu, X. X. (2022) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2022.3198244 · Cited by 364
A Broader Study of Cross-Domain Few-Shot Learning
Guo, Y., Codella, N., Karlinsky, L., Codella, J., Smith, J. R., Saenko, K., Rosing, T., & Feris, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_8 · Cited by 371
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Shen, C., Laloy, E., ELSHORBAGY, A. A., Albert, A., Bales, J. D., Chang, F., Ganguly, S., Hsu, K., Kifer, D., Fang, Z. N., et al. (2018) · Hydrology and earth system sciences · doi:10.5194/hess-22-5639-2018 · Cited by 353
Prompt-aligned Gradient for Prompt Tuning
Zhu, B., Niu, Y., Han, Y., Wu, Y., & Zhang, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01435 · Cited by 387
EVA-02: A visual representation for neon genesis
Fang, Y., Sun, Q., Wang, X., Huang, T., Wang, X., & Cao, Y. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105171 · Cited by 473
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Manas, O., Lacoste, A., Giro-i-Nieto, X., Vazquez, D., & Rodriguez, P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00928 · Cited by 354
Himeur, Y., Rimal, B., Tiwary, A., & Amira, A. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.06.003 · Cited by 318
SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery
Cong, Y., Khanna, S., Meng, C., Liu, P., Rozi, E., He, Y., Burke, M., Lobell, D., & Ermon, S. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-0015 · Cited by 303
de Bem, P. P., Júnior, O. A. D. C., Guimarães, R. F., & Gomes, R. A. T. (2020) · Remote Sensing · doi:10.3390/rs12060901 · Cited by 299
Scaling Vision with Sparse Mixture of ExpertsPreprint
Riquelme, C., Puigcerver, J., Mustafa, B., Neumann, M., Jenatton, R., Pinto, A. S., Keysers, D., & Houlsby, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05974 · Cited by 330
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language ModelingPreprint
Zhang, R., Fang, R., Zhang, W., Gao, P., Li, K., Dai, J., Qiao, Y., & Li, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03930 · Cited by 472
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
Khattak, M. U., Wasim, S. T., Naseer, M., Khan, S. A., Yang, M., & Khan, F. S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01394 · Cited by 304
Visual-Language Prompt Tuning with Knowledge-Guided Context Optimization
Yao, H., Zhang, R., & Xu, C. (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/cvpr52729.2023.00653 · Cited by 310
Lu, Y., Liu, J., Zhang, Y., Liu, Y., & Tian, X. (2022) · 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/cvpr52688.2022.00514 · Cited by 285
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
Chiu, M. T., Xu, X., Wei, Y., Huang, Z., Schwing, A. G., Brunner, R., Khachatrian, H., Karapetyan, H., Dozier, I., Rose, G., et al. (2020) · 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/cvpr42600.2020.00290 · Cited by 270
Helber, P., Bischke, B., Dengel, A. R., & Borth, D. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8519248 · Cited by 253
Self-citationOn Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250
A Large-scale Study of Representation Learning with the Visual Task Adaptation BenchmarkPreprint
Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lučić, M., Djolonga, J., Pinto, A. S., Neumann, M., Dosovitskiy, A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04867 · Cited by 521
Adegun, A. A., Viriri, S., & Tapamo, J. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00772-x · Cited by 262
Τέμενος, Α., Temenos, N., Kaselimi, M., Doulamis, A. D., & Doulamis, N. D. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3251652 · Cited by 227
Zhang, W., Cai, M., Zhang, T., Zhuang, Y., & Mao, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3409624 · Cited by 265
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study
Naushad, R., Kaur, T., & Ghaderpour, E. (2021) · Sensors · doi:10.3390/s21238083 · Cited by 201
Farhadpour, S., Warner, T. A., & Maxwell, A. E. (2024) · Remote Sensing · doi:10.3390/rs16030533 · Cited by 183
Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review
Arshad, B., Ogie, R., Barthélemy, J., Pradhan, B., Verstaevel, N., & Perez, P. (2019) · Sensors · doi:10.3390/s19225012 · Cited by 187
Prompt, Generate, Then Cache: Cascade of Foundation Models Makes Strong Few-Shot Learners
Zhang, R., Hu, X., Li, B., Huang, S., Deng, H., Qiao, Y., Gao, P., & Li, H. (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/cvpr52729.2023.01460 · Cited by 201
Ganesh Babu, R., Kumarasamy, U. M., Zarro, C., Divakarachari, P. B., & Ullo, S. L. (2020) · Remote Sensing · doi:10.3390/rs12244135 · Cited by 175
Yang, S., Zhou, Y., Zheng, Z., Wang, Y., Zhu, L., & Wu, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611709 · Cited by 197
Shi, Q., Liu, M., Marinoni, A., & Liu, X. (2023) · Earth system science data · doi:10.5194/essd-15-555-2023 · Cited by 167
On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3134785 · Cited by 162
Zhang, Z., Zhao, T., Guo, Y., & Yin, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3449154 · Cited by 208
Plant disease detection using machine learning approaches
Ahmed, I., & Yadav, P. K. (2022) · Expert Systems · doi:10.1111/exsy.13136 · Cited by 152
Masolele, R. N., Sy, V. D., Herold, M., Marcos, D., Verbesselt, J., Gieseke, F. C., Mullissa, A., & Martius, C. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112600 · Cited by 147
Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models
Lin, Z., Yu, S., Kuang, Z., Pathak, D., & Ramanan, D. (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/cvpr52729.2023.01852 · Cited by 154
Wang, Y., Braham, N. A. A., Xiong, Z., Liu, C., Albrecht, C. M., & Zhu, X. X. (2023) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2023.3281651 · Cited by 141
Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters
Yu, J., Zhuge, Y., Zhang, L., Hu, P., Wang, D., Lu, H., & He, Y. (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/cvpr52733.2024.02191 · Cited by 173
Land Use and Land Cover Classification Meets Deep Learning: A Review
Zhao, S., Tu, K., Ye, S., Tang, H., Hu, Y., & Xie, C. (2023) · Sensors · doi:10.3390/s23218966 · Cited by 152
Self-Supervised Learning for Few-Shot Image Classification
Chen, D., Chen, Y., Li, Y., Mao, F., He, Y., & Xue, H. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413783 · Cited by 136
MTP: Advancing Remote Sensing Foundation Model via Multitask Pretraining
Wang, D., Zhang, J., Xu, M., Liu, L., Wang, D., Gao, E., Han, C., Guo, H., Du, B., Tao, D., et al. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3408154 · Cited by 170
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
Sarukkai, V., Jain, A., Uzkent, B., & Ermon, S. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093564 · Cited by 135
Foundation Models for Remote Sensing and Earth Observation: A survey
Xiao, A., Xuan, W., Wang, J., Huang, J., Tao, D., Lu, S., & Yokoya, N. (2025) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2025.3576766 · Cited by 186
Deep Learning for Land Cover Change Detection
Sefrin, O., Riese, F. M., & Keller, S. (2020) · Remote Sensing · doi:10.3390/rs13010078 · Cited by 133
CALIP: Zero-Shot Enhancement of CLIP with Parameter-Free Attention
Guo, Z., Zhang, R., Qiu, L., Ma, X., Miao, X., He, X., & Cui, B. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25152 · Cited by 143
Accounting for Training Data Error in Machine Learning Applied to Earth Observations
Elmes, A., Alemohammad, H., Avery, R., Caylor, K. K., Eastman, J. R., Lewis Fishgold, A., Friedl, M. A., Jain, M., Kohli, D., Bayas, J. C. L., et al. (2020) · Remote Sensing · doi:10.3390/rs12061034 · Cited by 131
Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding
Stojnić, V., & Risojević, V. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00129 · Cited by 125
Towards the next generation of Geospatial Artificial Intelligence
Mai, G., Xie, Y., Jia, X., Lao, N., Rao, J., Zhu, Q., Liu, Z., Chiang, Y., & Jiao, J. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104368 · Cited by 145
The potential for AI to revolutionize conservation: a horizon scan
Reynolds, S. A., Beery, S., Burgess, N. D., Burgman, M. A., Butchart, S. H. M., Cooke, S. J., Coomes, D. A., Danielsen, F., Minin, E. D., Durán, A. P., et al. (2024) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2024.11.013 · Cited by 139
Task Residual for Tuning Vision-Language Models
Yu, T., Lu, Z., Jin, X., Chen, Z., & Wang, X. (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/cvpr52729.2023.01049 · Cited by 143
Filho, W. L., Mbah, M. F., Dinis, M. A. P., Trevisan, L. V., de Lange, D. E., Mishra, A., Rebelatto, B. G., Hassen, T. B., & Aina, Y. A. (2024) · Cities · doi:10.1016/j.cities.2024.105021 · Cited by 133
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
Chan, L., Hosseini, M. S., & Plataniotis, K. N. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01373-4 · Cited by 118
A Lightweight Model of VGG-16 for Remote Sensing Image Classification
Mu, Y., Ni, R., Chang, Z., Gong, H., Hu, T., Li, S., Sun, Y., Zhang, T., & Ying, G. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3090085 · Cited by 118
Tao, C., Qi, J., Lu, W., Wang, H., & Li, H. (2020) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3038420 · Cited by 116
SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
Wang, Z., Prabha, R., Huang, T., Wu, J., & Rajagopal, R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28393 · Cited by 146
Debiased Learning from Naturally Imbalanced Pseudo-Labels
Wang, X., Wu, Z., Lian, L., & Yu, S. X. (2022) · 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/cvpr52688.2022.01424 · Cited by 120
Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works
Tao, C., Qi, J., Guo, M., Zhu, Q., & Li, H. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3276853 · Cited by 116
Kang, J. S., Fernández-Beltrán, R., Duan, P., Liu, S., & Plaza, A. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3007029 · Cited by 109
Fake it Till You Make it: Learning Transferable Representations from Synthetic ImageNet Clones
Sarıyıldız, M. B., Alahari, K., Larlus, D., & Kalantidis, Y. (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/cvpr52729.2023.00774 · Cited by 120
Glacial Lakes Mapping Using Multi Satellite PlanetScope Imagery and Deep Learning
Qayyum, N., Ghuffar, S., Ahmad, H. M., Yousaf, A., & Shahid, I. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9100560 · Cited by 109
Dimitrovski, I., Kitanovski, I., Kocev, D., & Simidjievski, N. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.01.014 · Cited by 109
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
Xing, Y., Wu, Q., Cheng, D., Zhang, S., Liang, G., Wang, P., & Zhang, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3291588 · Cited by 129
LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model
Muhtar, D., Li, Z., Gu, F., Zhang, X., & Xiao, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72904-1_26 · Cited by 145
Land Use and Land Cover Mapping in the Era of Big Data
Zhang, C., & Li, X. (2022) · Land · doi:10.3390/land11101692 · Cited by 101
On Robustness and Transferability of Convolutional Neural Networks
Djolonga, J., Yung, J., Tschannen, M., Romijnders, R., Beyer, L., Kolesnikov, A., Puigcerver, J., Minderer, M., D'Amour, A., Moldovan, D., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01619 · Cited by 98
Change-Aware Sampling and Contrastive Learning for Satellite Images
Mall, U., Hariharan, B., & Bala, K. (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/cvpr52729.2023.00509 · Cited by 110
Peng, F., Yang, X., Xiao, L., Wang, Y., & Xu, C. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3311646 · Cited by 110
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
Fu, Y., Xie, Y., Fu, Y., & Jiang, Y. (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/cvpr52729.2023.02354 · Cited by 97
Learning Disentangled Representations via Mutual Information Estimation
Sanchez, E. H., Serrurier, M., & Ortner, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58542-6_13 · Cited by 96
A Robust Feature Downsampling Module for Remote-Sensing Visual Tasks
Lu, W., Chen, S.-B., Tang, J., Ding, C. H. Q., & Luo, B. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3282048 · Cited by 98
SuS-X: Training-Free Name-Only Transfer of Vision-Language Models
Udandarao, V., Gupta, A., & Albanie, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00257 · Cited by 120
de Carvalho, O. L. F., de Carvalho, O. L. F., de Albuquerque, A. O., de Bem, P. P., Silva, C. R., Ferreira, P. H. G., de Moura, R. D. S., Gomes, R. A. T., Guimarães, R. F., & Borges, D. L. (2020) · Remote Sensing · doi:10.3390/rs13010039 · Cited by 102
An Empirical Study of Adversarial Examples on Remote Sensing Image Scene Classification
Chen, L., Xu, Z., Li, Q., Peng, J., Wang, S., & Li, H. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3051641 · Cited by 95
How computer vision can facilitate flood management: A systematic review
Iqbal, U., Perez, P., Li, W., & Barthélemy, J. (2021) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2020.102030 · Cited by 92
MMA: Multi-Modal Adapter for Vision-Language Models
Yang, L., Zhang, R., Wang, Y., & Xie, X. (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/cvpr52733.2024.02249 · Cited by 108
Weikmann, G., Paris, C., & Bruzzone, L. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3073965 · Cited by 91
Monitoring land use in cities using satellite imagery and deep learning
Banquet, A., Delbouve, P., Daams, M. N., & Veneri, P. (2022) · OECD regional development papers · doi:10.1787/dc8e85d5-en · Cited by 90
Wu, W., Li, H., Li, X., Guo, H., & Zhang, L. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2886559 · Cited by 88
EarthNets: Empowering artificial intelligence for Earth observation
Xiong, Z., Zhang, F., Wang, Y., Shi, Y., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3466998 · Cited by 108
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
Feng, C.-M., Yu, K., Liu, Y., Khan, S. H., & Zuo, W. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00255 · Cited by 116
Benchmarking and scaling of deep learning models for land cover image classification
Papoutsis, I., Bountos, N. I., Zavras, A., Michail, D., & Tryfonopoulos, C. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.11.012 · Cited by 86
Metadata
- Authors
- Helber, P., Bischke, B., Dengel, A., & Borth, D.
- Affiliations
- University of Kaiserslautern (DE)
- University of St.Gallen (CH)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2019-07
- Volume
- 12
- Issue
- 7
- Pages
- 2217-2226
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2964194231
- Cited by
- 1,951
- References
- 39
- Citations by year
- 2026: 430 · 2025: 505 · 2024: 352 · 2023: 257 · 2022: 167 · 2021: 140 · 2020: 77 · 2019: 19
- FWCI
- 67.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- Funders
- BMBF Project DeFuseNN (01IW17002)
- Nvidia (xxx)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- land use and land cover classification, EuroSAT dataset, Sentinel-2, satellite imagery, deep learning benchmark, convolutional neural networks, patch-based classification, geo-referenced images, Copernicus
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
