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In geographical image segmentation, performance is often constrained by the limited availability of training data and a lack of generalizability, particularly for segmenting mobility infrastructure such as roads, sidewalks, and crosswalks.
U-net: Convolutional networks for biomedical image segmentation
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
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Scene parsing through ade20k dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
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Cambridge sidewalk
Cambridge GIS (2018) · 2018
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Encoder-decoder with atrous separable convolution for semantic image segmentation
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam · 2018
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Road segmentation in sar satellite images with deep fully convolutional neural networks
C. Henry, S. M. Azimi, and N. Merkle · 2018
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USGS EROS Archive - Aerial Photography - High Resolution Orthoimagery (HRO)
US Geological Survey (2018) · 2018
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Roads 2019
DC GIS (2019) · 2019
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Deep high-resolution representation learning for human pose estimation
K. Sun, B. Xiao, D. Liu, and J. Wang · 2019
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Geography-aware self-supervised learning
K. Ayush, B. Uzkent, C. Meng, K. Tanmay, M. Burke, D. Lobell, and S. Ermon · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
A. Dosovitskiy et al · 2021
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Deep learning-based object recognition in multispectral satellite imagery for real-time applications
P. Gudžius, O. Kurasova, V. Darulis, and E. Filatovas · 2021
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Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
A. Hatamizadeh, V. Nath, Y. Tang, D. Yang, H. R. Roth, and D. Xu · 2021
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nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
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Ga-net: A geometry prior assisted neural network for road extraction
X. Chen, Q. Sun, W. Guo, C. Qiu, and A. Yu · 2022
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Unetr: Transformers for 3d medical image segmentation
A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H. R. Roth, and D. Xu · 2022
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LoRA: Low-rank adaptation of large language models
E. J. Hu, yelong shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2022
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Conducting semantic segmentation on landcover satellite imagery through u-net architectures
A. Saha · 2022
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Ringmo: A remote sensing foundation model with masked image modeling
Ringmo-sam: A foundation model for segment anything in multimodal remote-sensing images
Z. Yan, J. Li, X. Li, R. Zhou, W. Zhang, Y. Feng, W. Diao, K. Fu, and X. Sun · 2023
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nnformer: Volumetric medical image segmentation via a 3d transformer
H.-Y. Zhou, J. Guo, Y. Zhang, X. Han, L. Yu, L. Wang, and Y. Yu · 2023
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A billion-scale foundation model for remote sensing images
K. Cha, J. Seo, and T. Lee · 2024
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Rsprompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model
K. Chen, C. Liu, H. Chen, H. Zhang, W. Li, Z. Zou, and Z. Shi · 2024
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Adapting segment anything model for change detection in vhr remote sensing images
L. Ding, K. Zhu, D. Peng, H. Tang, K. Yang, and L. Bruzzone · 2024
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X. Sun, P. Wang, W. Lu, Z. Zhu, X. Lu, Q. He, J. Li, X. Rong, Z. Yang, H. Chang, et al · 2022
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Variational prompt tuning improves generalization of vision-language foundation models
M. M. Derakhshani et al · 2023
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Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation
Y. He, V. Nath, D. Yang, Y. Tang, A. Myronenko, and D. Xu · 2023
Cited alongside, same era.
Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery
M. Hosseini, A. Sevtsuk, F. Miranda, R. M. Cesar Jr, and C. T. Silva · 2023
Cited alongside, same era.
Segment anything
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al · 2023
Cited alongside, same era.
OpenAI · 2023
Cited alongside, same era.
The segment anything model (sam) for remote sensing applications: From zero to one shot
L. P. Osco, Q. Wu, E. L. de Lemos, W. N. Gonçalves, A. P. M. Ramos, J. Li, and J. M. Junior · 2023
Cited alongside, same era.
W. Feng, F. Guan, C. Sun, and W. Xu · 2024
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Rsps-sam: A remote sensing image panoptic segmentation method based on sam
Z. Liu, Z. Li, Y. Liang, C. Persello, B. Sun, G. He, and L. Ma · 2024
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Sam-assisted remote sensing imagery semantic segmentation with object and boundary constraints
X. Ma, Q. Wu, X. Zhao, X. Zhang, M.-O. Pun, and B. Huang · 2024
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Samrs: Scaling-up remote sensing segmentation dataset with segment anything model
D. Wang, J. Zhang, B. Du, M. Xu, L. Liu, D. Tao, and L. Zhang · 2024
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Urbanclip: Learning text-enhanced urban region profiling with contrastive language-image pretraining from the web
Y. Yan, H. Wen, S. Zhong, W. Chen, H. Chen, Q. Wen, R. Zimmermann, and Y. Liang · 2024
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Mesam: Multiscale enhanced segment anything model for optical remote sensing images
X. Zhou, F. Liang, L. Chen, H. Liu, Q. Song, G. Vivone, and J. Chanussot · 2024
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Source Code
R. I. Sultan · 2025
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Tpp-sam: A trajectory points prompting segment anything model for zero-shot road extraction from high-resolution remote sensing imagery
T. Wu, Y. Hu, J. Qin, X. Lin, and Y. Wan · 2025
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