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The interpretation of multi-temporal remote sensing imagery is critical for monitoring Earth's dynamic processes-yet previous change detection methods, which produce binary or semantic masks, fall short of providing human-readable insights into changes.
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L. Song, M. Xia, L. Weng, H. Lin, M. Qian, and B. Chen, “Axial cross attention meets cnn: Bibranch fusion network for change detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 16, pp. 21–32, 2022
2022
Cited alongside, same era.
Z. Li, C. Yan, Y. Sun, and Q. Xin, “A densely attentive refinement network for change detection based on very-high-resolution bitemporal remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–18, 2022
2022
Cited alongside, same era.
Y. Feng, H. Xu, J. Jiang, H. Liu, and J. Zheng, “Icif-net: Intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2022
2022
Cited alongside, same era.
2024
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2024
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2024
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, 2024
2024
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L. Xu, H. Xie, F. L. Wang, X. Tao, W. Wang, and Q. Li, “Contrastive sentence representation learning with adaptive false negative cancellation,” Information Fusion , vol. 102, p. 102065, 2024
2024
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J. Zhang, J. Huang, S. Jin, and S. Lu, “Vision-language models for vision tasks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
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Z. Yuan, J. Cao, Z. Wang, and Z. Li, “Tsar-mvs: Textureless-aware segmentation and correlative refinement guided multi-view stereo,” Pattern Recognition , vol. 154, p. 110565, 2024
2024
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Z. Yuan, J. Cao, Z. Li, H. Jiang, and Z. Wang, “SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM Optimization,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, 2024, pp. 6871–6880
2024
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Z. Liu, H. Chen, L. Bai, W. Li, K. Chen, Z. Wang, W. Ouyang, Z. Zou, and Z. Shi, “Deriving accurate surface meteorological states at arbitrary locations via observation-guided continuous neural field modeling,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–20, 2024
2024
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2024
Closest in time.
K. Ni, Q. Wu, S. Li, Z. Zheng, and P. Wang, “Remote sensing scene classification via second-order differentiable token transformer network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–15, 2024
2024
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R. Wang, L. Ma, G. He, B. A. Johnson, Z. Yan, M. Chang, and Y. Liang, “Transformers for remote sensing: A systematic review and analysis,” Sensors , vol. 24, no. 11, p. 3495, 2024
2024
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2024
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2024
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X. Wang, S. Wang, Y. Ding, Y. Li, W. Wu, Y. Rong, W. Kong, J. Huang, S. Li, H. Yang, Z. Wang, B. Jiang, C. Li, Y. Wang, Y. Tian, and J. Tang, “State space model for new-generation network alternative to transformers: A survey,” 2024
2024
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2024
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2024
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F. Liu, D. Chen, Z. Guan, X. Zhou, J. Zhu, Q. Ye, L. Fu, and J. Zhou, “Remoteclip: A vision language foundation model for remote sensing,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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Z. Zhang, T. Zhao, Y. Guo, and J. Yin, “Rs5m and georsclip: A large scale vision-language dataset and a large vision-language model for remote sensing,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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S. Dong, L. Wang, B. Du, and X. Meng, “Changeclip: Remote sensing change detection with multimodal vision-language representation learning,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 208, pp. 53–69, 2024
2024
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Y. Chang, X. Wang, J. Wang, Y. Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y. Wang et al. , “A survey on evaluation of large language models,” ACM Transactions on Intelligent Systems and Technology , vol. 15, no. 3, pp. 1–45, 2024
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2024
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2024
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2024
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P. Wang, H. Hu, B. Tong, Z. Zhang, F. Yao, Y. Feng, Z. Zhu, H. Chang, W. Diao, Q. Ye et al. , “Ringmogpt: A unified remote sensing foundation model for vision, language, and grounded tasks,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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D. Hong, B. Zhang, X. Li, Y. Li, C. Li, J. Yao, N. Yokoya, H. Li, P. Ghamisi, X. Jia et al. , “Spectralgpt: Spectral remote sensing foundation model,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
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D. Wang, J. Zhang, M. Xu, L. Liu, D. Wang, E. Gao, C. Han, H. Guo, B. Du, D. Tao et al. , “Mtp: Advancing remote sensing foundation model via multi-task pretraining,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2024
2024
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Z. Yu, C. Liu, L. Liu, Z. Shi, and Z. Zou, “Metaearth: A generative foundation model for global-scale remote sensing image generation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
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2024
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J. Luo, Z. Pang, Y. Zhang, T. Wang, L. Wang, B. Dang, J. Lao, J. Wang, J. Chen, and Y. Tan, “Skysensegpt: A fine-grained instruction tuning dataset and model for remote sensing vision-language understanding,” 2024
2024
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M. Lan, C. Chen, Y. Zhou, J. Xu, Y. Ke, X. Wang, L. Feng, and W. Zhang, “Text4seg: Reimagining image segmentation as text generation,” 2024
2024
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H. Guo, X. Su, C. Wu, B. Du, L. Zhang, and D. Li, “Remote sensing chatgpt: Solving remote sensing tasks with chatgpt and visual models,” in IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2024, pp. 11 474–11 478
2024
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2024
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L. Zhu, J. Wu, B. Wang, G. Zhang, J. Wang, S. Chen, and M. Tan, “Rs-agent: Large language models guided agent system for remote sensing image generation,” in IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2024, pp. 7020–7024
2024
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L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin et al. , “A survey on large language model based autonomous agents,” Frontiers of Computer Science , vol. 18, no. 6, p. 186345, 2024
2024
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2024
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Y. Mu, Q. Zhang, M. Hu, W. Wang, M. Ding, J. Jin, B. Wang, J. Dai, Y. Qiao, and P. Luo, “Embodiedgpt: Vision-language pre-training via embodied chain of thought,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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Y. Sun, Y. Qiu, M. Khan, F. Matsuzawa, and K. Iwata, “The stvchrono dataset: Towards continuous change recognition in time,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2024
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R. Awal, S. Ahmadi, L. Zhang, and A. Agrawal, “Vismin: Visual minimal-change understanding,” 2024
2024
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D. Rege Cambrin and P. Garza, “Quakeset: A dataset and low-resource models to monitor earthquakes through sentinel-1,” Proceedings of the International ISCRAM Conference , May 2024. [Online]. Available: http://dx.doi.org/10.59297/n89yc374
2024
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H. Zhang, H. Chen, C. Zhou, K. Chen, C. Liu, Z. Zou, and Z. Shi, “Bifa: Remote sensing image change detection with bitemporal feature alignment,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–17, 2024
2024
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2024
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2024
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S. Lei, X. Xiao, T. Zhang, H.-C. Li, Z. Shi, and Q. Zhu, “Exploring fine-grained image-text alignment for referring remote sensing image segmentation,” IEEE Transactions on Geoscience and Remote Sensing , 2025
2025
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X. Weng, C. Pang, and G.-S. Xia, “Vision-language modeling meets remote sensing: Models, datasets, and perspectives,” IEEE Geoscience and Remote Sensing Magazine , p. 2–50, 2025. [Online]. Available: http://dx.doi.org/10.1109/MGRS.2025.3572702
2025
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2025
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Z. Yu, C. Liu, C. Zhong, Z. Zou, and Z. Shi, “Multi-grained guided diffusion for quantity-controlled remote sensing object generation,” IEEE Geoscience and Remote Sensing Letters , 2025
2025
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2025
Closest in time.
H. Zhang, K. Chen, C. Liu, H. Chen, Z. Zou, and Z. Shi, “Cdmamba: Incorporating local clues into mamba for remote sensing image binary change detection,” IEEE Transactions on Geoscience and Remote Sensing , 2025
2025
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Y. Xing, Y. Jia, S. Gao, J. Hu, and R. Huang, “Frequency-enhanced mamba for remote sensing change detection,” IEEE Geoscience and Remote Sensing Letters , 2025
2025
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Y. Feng, L. Zhuo, H. Zhang, and J. Li, “Hybrid-mambacd: Hybrid mamba-cnn network for remote sensing image change detection with region-channel attention mechanism and iterative global-local feature fusion,” IEEE Transactions on Geoscience and Remote Sensing , 2025
2025
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Y. Zan, S. Ji, S. Chao, and M. Luo, “Open-vocabulary generative vision-language models for creating a large-scale remote sensing change detection dataset,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 225, pp. 275–290, 2025
2025
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2025
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R. Li, L. Li, J. Zhang, Q. Zhao, H. Wang, and C. Yan, “Region-aware difference distilling with attribute-guided contrastive regularization for change captioning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 5, 2025, pp. 4887–4895
2025
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X. Li, B. Sun, Z. Wu, S. Li, and H. Guo, “Cd4c: Change detection for remote sensing image change captioning,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2025
2025
Closest in time.
Q. Sun, Y. Wang, and X. Song, “Scene graph and dependency grammar enhanced remote sensing change caption network (sgd-rsccn),” in Proceedings of the 31st International Conference on Computational Linguistics , 2025, pp. 2121–2130
2025
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2025
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2025
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D. Sun, J. Yao, C. Zhou, X. Cao, and P. Ghamisi, “Mask approximation net: A novel diffusion model approach for remote sensing change captioning,” 2025
2025
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S. Zou, Y. Wei, Y. Xie, and X. Luan, “Frequency–spatial–temporal domain fusion network for remote sensing image change captioning,” Remote Sensing , 2025
2025
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2025
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2025
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2025
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2025
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Z. Yuan, Z. Yang, Y. Cai, K. Wu, M. Liu, D. Zhang, H. Jiang, Z. Li, and Z. Wang, “SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint,” IEEE Transactions on Circuits and Systems for Video Technology , 2025
2025
Closest in time.
Z. Yuan, C. Liu, F. Shen, Z. Li, J. Luo, T. Mao, and Z. Wang, “MSP-MVS: Multi-granularity segmentation prior guided multi-view stereo,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, 2025, pp. 9753–9762
2025
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Z. Yuan, J. Luo, F. Shen, Z. Li, C. Liu, T. Mao, and Z. Wang, “DVP-MVS: Synergize depth-edge and visibility prior for multi-view stereo,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, 2025, pp. 9743–9752
2025
Closest in time.
Z. Li, Y. Wang, H. Feng, C. Chen, D. Xu, T. Zhao, Y. Gao, and Z. Zhao, “Local to global: A sparse transformer-based small object detector for remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , 2025
2025
Closest in time.
W. Jing, W. Zhang, D. Di, C. Li, M. Emam, and A. Mian, “Hypergraph biformer for semantic segmentation of high-resolution remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 63, pp. 1–15, 2025
2025
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F. Yang, M. Li, W. Shu, A. Qin, T. Song, C. Gao, and G.-S. Xia, “Convformer-cd: Hybrid cnn–transformer with temporal attention for detecting changes in remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 63, pp. 1–15, 2025
2025
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2025
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2025
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Q. Bai and X. Wang, “Cross-temporal remote sensing image change captioning: A manifold mapping and bayesian diffusion approach for land use monitoring,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , pp. 1–11, 2025
2025
Closest in time.
H. Huang, Q. Cheng, D. Zhu, X. Huang, and Q. Zhao, “Textscd: Leveraging text-based semantic guidance for remote sensing image semantic change detection,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. X-G-2025, pp. 383–389, 2025. [Online]. Available: https://isprs-annals.copernicus.org/articles/X-G-2025/383/2025/
2025
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2025
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2025
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2025
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2025
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2025
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2025
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2025
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D. Wang, M. Hu, Y. Jin, Y. Miao, J. Yang, Y. Xu, X. Qin, J. Ma, L. Sun, C. Li et al. , “Hypersigma: Hyperspectral intelligence comprehension foundation model,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025
2025
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2025
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C. Pang, X. Weng, J. Wu, J. Li, Y. Liu, J. Sun, W. Li, S. Wang, L. Feng, G.-S. Xia et al. , “Vhm: Versatile and honest vision language model for remote sensing image analysis,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 6, 2025, pp. 6381–6388
2025
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2025
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2025
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Y. Zhan, Z. Xiong, and Y. Yuan, “Skyeyegpt: Unifying remote sensing vision-language tasks via instruction tuning with large language model,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 221, pp. 64–77, 2025
2025
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C. Wei, Y. Zhang, X. Zhao, Z. Zeng, Z. Wang, J. Lin, Q. Guan, and W. Yu, “Geotool-gpt: a trainable method for facilitating large language models to master gis tools,” International Journal of Geographical Information Science , vol. 39, no. 4, pp. 707–731, 2025
2025
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Z. Liu, D. Zhao, B. Yuan, and Z. Jiang, “Rescueadi: Adaptive disaster interpretation in remote sensing images with autonomous agents,” IEEE Transactions on Geoscience and Remote Sensing , 2025
2025
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2025
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2025
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K. Yang, J. Wei, C. Chen, Z. Wang, J. Lan, X. Li, D. Hua, D. Xue, and Y. Wu, “Restricted supervised cascade information network for remote sensing change captioning with serial sentences,” International Journal of Applied Earth Observation and Geoinformation , vol. 142, p. 104686, 2025
2025
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G. Astruc, N. Gonthier, C. Mallet, and L. Landrieu, “Omnisat: Self-supervised modality fusion forearth observation,” in European Conference on Computer Vision , 2025
2025
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C. Yang, W. Xie, and A. Zisserman, “Made toorder: Discovering monotonic temporal changes viaself-supervised video ordering,” in European Conference on Computer Vision , 2025
2025
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F. Wang, H. Wang, Z. Guo, D. Wang, Y. Wang, M. Chen, Q. Ma, L. Lan, W. Yang, J. Zhang et al. , “Xlrs-bench: Could your multimodal llms understand extremely large ultra-high-resolution remote sensing imagery?” in Proceedings of the Computer Vision and Pattern Recognition Conference , 2025, pp. 14 325–14 336
2025
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2025
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Y. Tang, J. Bi, S. Xu, L. Song, S. Liang, T. Wang, D. Zhang, J. An, J. Lin, R. Zhu et al. , “Video understanding with large language models: A survey,” IEEE Transactions on Circuits and Systems for Video Technology , 2025
2025
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2025
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Y. Liu, Y. Zhong, F. Fei, Q. Zhu, and Q. Qin, “Scene classification based on a deep random-scale stretched convolutional neural network,” Remote Sensing , vol. 10, no. 3, 2018. [Online]. Available: https://www.mdpi.com/2072-4292/10/3/444
2072
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