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The intelligent interpretation of buildings plays a significant role in urban planning and management, macroeconomic analysis, population dynamics, etc.
Y. Zhang, “Optimisation of building detection in satellite images by combining multispectral classification and texture filtering,” ISPRS journal of photogrammetry and remote sensing , vol. 54, no. 1, pp. 50–60, 1999
1999
Earlier work this paper cites.
B. Sirmacek and C. Unsalan, “Building detection from aerial images using invariant color features and shadow information,” in 2008 23rd international symposium on computer and information sciences . IEEE, 2008, pp. 1–5
2008
Earlier work this paper cites.
S.-h. Zhong, J.-j. Huang, and W.-x. Xie, “A new method of building detection from a single aerial photograph,” in 2008 9th international conference on signal processing . IEEE, 2008, pp. 1219–1222
2008
Earlier work this paper cites.
Y. Li and H. Wu, “Adaptive building edge detection by combining lidar data and aerial images,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. 37, no. Part B1, pp. 197–202, 2008
2008
Earlier work this paper cites.
P. S. Tiwari and H. Pande, “Use of laser range and height texture cues for building identification,” Journal of the Indian Society of Remote Sensing , vol. 36, pp. 227–234, 2008
2008
Earlier work this paper cites.
G. Ferraioli, “Multichannel insar building edge detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 48, no. 3, pp. 1224–1231, 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
M. Awrangjeb, C. Zhang, and C. S. Fraser, “Improved building detection using texture information,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , vol. 38, pp. 143–148, 2013
2013
Earlier work this paper cites.
V. Mnih, Machine learning for aerial image labeling . University of Toronto (Canada), 2013
2013
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
M. Ghanea, P. Moallem, and M. Momeni, “Building extraction from high-resolution satellite images in urban areas: Recent methods and strategies against significant challenges,” International journal of remote sensing , vol. 37, no. 21, pp. 5234–5248, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
Earlier work this paper cites.
E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark,” in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE, 2017, pp. 3226–3229
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. C. Daudt, B. Le Saux, and A. Boulch, “Fully convolutional siamese networks for change detection,” in 2018 25th IEEE International Conference on Image Processing (ICIP) . IEEE, 2018, pp. 4063–4067
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
S. Ji, S. Wei, and M. Lu, “Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set,” IEEE Transactions on geoscience and remote sensing , vol. 57, no. 1, pp. 574–586, 2018
2018
Earlier work this paper cites.
A. Asokan and J. Anitha, “Change detection techniques for remote sensing applications: A survey,” Earth Science Informatics , vol. 12, pp. 143–160, 2019
2019
Earlier work this paper cites.
S. Wei, S. Ji, and M. Lu, “Toward automatic building footprint delineation from aerial images using cnn and regularization,” IEEE Transactions on Geoscience and Remote Sensing , vol. 58, no. 3, pp. 2178–2189, 2019
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
H. Chen and Z. Shi, “A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,” Remote Sensing , vol. 12, no. 10, p. 1662, 2020
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
W. Shi, M. Zhang, R. Zhang, S. Chen, and Z. Zhan, “Change detection based on artificial intelligence: State-of-the-art and challenges,” Remote Sensing , vol. 12, no. 10, p. 1688, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
H. Chen and Z. Shi, “A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,” Remote Sensing , vol. 12, no. 10, p. 1662, 2020
2020
Earlier work this paper cites.
M. Contributors, “MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark,” https://github.com/open-mmlab/mmsegmentation , 2020
2020
Cited alongside, same era.
H. Zhang, Y. Liao, H. Yang, G. Yang, and L. Zhang, “A local–global dual-stream network for building extraction from very-high-resolution remote sensing images,” IEEE transactions on neural networks and learning systems , vol. 33, no. 3, pp. 1269–1283, 2020
2020
Cited alongside, same era.
K. Yang, G.-S. Xia, Z. Liu, B. Du, W. Yang, M. Pelillo, and L. Zhang, “Semantic change detection with asymmetric siamese networks,” 2020
2020
Cited alongside, same era.
K. Chen, Z. Zou, and Z. Shi, “Building extraction from remote sensing images with sparse token transformers,” Remote Sensing , vol. 13, no. 21, p. 4441, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
P. Chen, B. Zhang, D. Hong, Z. Chen, X. Yang, and B. Li, “Fccdn: Feature constraint network for vhr image change detection,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 187, pp. 101–119, 2022
2022
Later among the works it cites.
2023
Later among the works it cites.
T. Bai, L. Wang, D. Yin, K. Sun, Y. Chen, W. Li, and D. Li, “Deep learning for change detection in remote sensing: a review,” Geo-spatial Information Science , vol. 26, no. 3, pp. 262–288, 2023
2023
Later among the works it cites.
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2021
Cited alongside, same era.
L. Luo, P. Li, and X. Yan, “Deep learning-based building extraction from remote sensing images: A comprehensive review,” Energies , vol. 14, no. 23, p. 7982, 2021
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems , vol. 34, pp. 12 077–12 090, 2021
2021
Cited alongside, same era.
D. Wen, X. Huang, F. Bovolo, J. Li, X. Ke, A. Zhang, and J. A. Benediktsson, “Change detection from very-high-spatial-resolution optical remote sensing images: Methods, applications, and future directions,” IEEE Geoscience and Remote Sensing Magazine , vol. 9, no. 4, pp. 68–101, 2021
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 4904–4916
2021
Cited alongside, same era.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
Cited alongside, same era.
2023
Later among the works it cites.
H. Chen, H. Zhang, K. Chen, C. Zhou, S. Chen, Z. Zou, and Z. Shi, “Continuous cross-resolution remote sensing image change detection,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
K. Chen, X. Jiang, Y. Hu, X. Tang, Y. Gao, J. Chen, and W. Xie, “Ovarnet: Towards open-vocabulary object attribute recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 23 518–23 527
2023
Later among the works it cites.
K. Chen, W. Li, S. Lei, J. Chen, X. Jiang, Z. Zou, and Z. Shi, “Continuous remote sensing image super-resolution based on context interaction in implicit function space,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
Y. Feng, J. Jiang, H. Xu, and J. Zheng, “Change detection on remote sensing images using dual-branch multilevel intertemporal network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, 2023
2023
Later among the works it cites.
S. Fang, K. Li, and Z. Li, “Changer: Feature interaction is what you need for change detection,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
OpenAI, “Gpt-4 technical report,” 2023
2023
Later among the works it cites.
N. Ding, Y. Qin, G. Yang, F. Wei, Z. Yang, Y. Su, S. Hu, Y. Chen, C.-M. Chan, W. Chen et al. , “Parameter-efficient fine-tuning of large-scale pre-trained language models,” Nature Machine Intelligence , vol. 5, no. 3, pp. 220–235, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
A. A. Aleissaee, A. Kumar, R. M. Anwer, S. Khan, H. Cholakkal, G.-S. Xia, and F. S. Khan, “Transformers in remote sensing: A survey,” Remote Sensing , vol. 15, no. 7, p. 1860, 2023
2023
Later among the works it cites.
Z. Li, C. Tang, X. Liu, W. Zhang, J. Dou, L. Wang, and A. Y. Zomaya, “Lightweight remote sensing change detection with progressive feature aggregation and supervised attention,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–12, 2023
2023
Later among the works it cites.
C. Pang, J. Wu, J. Ding, C. Song, and G.-S. Xia, “Detecting building changes with off-nadir aerial images,” Science China Information Sciences , vol. 66, no. 4, p. 140306, 2023
2023
Later among the works it cites.
“xview2,” https://www.xview2.org/ , accessed: 2023-11-10
2023
Later among the works it cites.
AIcrowd, “Mapping challenge,” https://www.aicrowd.com/challenges/mapping-challenge , 2023, accessed: 2023-11-10
2023
Later among the works it cites.
O. AI, “2018 open ai tanzania building footprint segmentation challenge,” https://competitions.codalab.org/competitions/20100 , 2018, accessed: 2023-11-10
2023
Later among the works it cites.
S. Holail, T. Saleh, X. Xiao, and D. Li, “Afde-net: Building change detection using attention-based feature differential enhancement for satellite imagery,” IEEE Geoscience and Remote Sensing Letters , 2023
2023
Later among the works it cites.
L. Xu, Y. Li, J. Xu, Y. Zhang, and L. Guo, “Bctnet: Bi-branch cross-fusion transformer for building footprint extraction,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–14, 2023
2023
Later among the works it cites.
H. Guo, X. Su, C. Wu, B. Du, and L. Zhang, “Decoupling semantic and edge representations for building footprint extraction from remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
C. Han, C. Wu, H. Guo, M. Hu, and H. Chen, “Hanet: A hierarchical attention network for change detection with bitemporal very-high-resolution remote sensing images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 16, pp. 3867–3878, 2023
2023
Later among the works it cites.
K. Li, X. Cao, and D. Meng, “A new learning paradigm for foundation model-based remote-sensing change detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–12, 2024
2024
Closest in time.
K. Chen, C. Liu, H. Chen, H. Zhang, W. Li, Z. Zou, and Z. Shi, “Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
Closest in time.
L. Salewski, S. Alaniz, I. Rio-Torto, E. Schulz, and Z. Akata, “In-context impersonation reveals large language models’ strengths and biases,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.