Fetching the paper…
Reading the bibliography…
This paper provides insights on the effectiveness of the zero shot, prompt-based Segment Anything Model (SAM) and its updated versions, SAM 2 and SAM 2.1, along with the non-promptable conventional neural network (CNN), for segmenting solar panels in RGB aerial remote sensing imagery.
2015
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
L. Ma, Y. Liu, X. Zhang, Y. Ye, G. Yin, and B. A. Johnson, “Deep learning in remote sensing applications: A meta-analysis and review,” ISPRS Journal of Photogrammetry and Remote Sensing
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Waveland Press, Incorporated, 2020
F. Sabins and J. Ellis, Remote Sensing: Principles, Interpretation, and Applications · 2020
Earlier work this paper cites.
B. Baheti, S. Innani, S. Gajre, and S. Talbar, “Eff-unet: A novel architecture for semantic segmentation in unstructured environment,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
2020
Earlier work this paper cites.
P. Sharma, Y. P. S. Berwal, and W. Ghai, “Performance analysis of deep learning cnn models for disease detection in plants using image segmentation,” Information Processing in Agriculture
2020
Earlier work this paper cites.
T. Lüddecke and A. Ecker, “Image segmentation using text and image prompts,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2022
Cited alongside, same era.
M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, “Segment anything model for medical image analysis: An experimental study,” Medical Image Analysis
2023
Cited alongside, same era.
S. Pratt, I. Covert, R. Liu, and A. Farhadi, “What does a platypus look like? generating customized prompts for zero-shot image classification,” 2023
2023
Cited alongside, same era.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment anything,” 2023
2023
Cited alongside, same era.
L. P. Osco, Q. Wu, E. L. de Lemos, W. N. Gonçalves, A. P. M. Ramos, J. Li, and J. M. Junior, “The segment anything model (sam) for remote sensing applications: From zero to one shot,” 2023
Q. Wu and L. P. Osco, “samgeo: A python package for segmenting geospatial data with the segment anything model (sam),” Journal of Open Source Software
2023
Later among the works it cites.
Available: https://github.com/ultralytics/ultralytics
G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics yolo,” jan 2023 · 2023
Later among the works it cites.
Y. Zhu, Q. Yang, and L. Xu, “Active learning enabled low-cost cell image segmentation using bounding box annotation,” 2024
2024
Closest in time.
A. Moghimi, M. Welzel, T. Celik, and T. Schlurmann, “A comparative performance analysis of popular deep learning models and segment anything model (sam) for river water segmentation in close-range remote sensing imagery,” IEEE Access
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
D. Wang, J. Zhang, B. Du, M. Xu, L. Liu, D. Tao, and L. Zhang, “Samrs: Scaling-up remote sensing segmentation dataset with segment anything model,” 2023
2023
Cited alongside, same era.
S. Shankar, L. A. Stearns, and C. van der Veen, “Semantic segmentation of glaciological features across multiple remote sensing platforms with the segment anything model (sam),” Journal of Glaciology
2023
Cited alongside, same era.
2024
Closest in time.
2024
Closest in time.