- Article
(3039 KB) - Metadata XML
- July 03, 2024
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B1-2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-85-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-85-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
06 Aug 2020
| 06 Aug 2020
SPATIAL RESOLUTION ENHANCEMENT OF LAND COVER MAPPING USING DEEP CONVOLUTIONAL NETS
Q. Yu
Dept. of Geography and Environmental Management, University of Waterloo, Canada
W. Liu
Virtual Reality and Interactive Techniques Institute, East China Jiaotong University, Jiangxi, China
J. Li
Dept. of Geography and Environmental Management, University of Waterloo, Canada
Related authors
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Mahdieh Shirmohammadi, Saied Pirasteh, Jie Shen, and Jonathan Li
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-2024, 213–218, https://doi.org/10.5194/isprs-annals-X-1-2024-213-2024, https://doi.org/10.5194/isprs-annals-X-1-2024-213-2024, 2024
A. Nurunnabi, Y. Sadahiro, F. N. Teferle, D. F. Laefer, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 27–34, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-27-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-27-2023, 2023
Y. Zang, G. Huang, W. Liu, L. Chen, D. Wu, C. Wang, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 71–76, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-71-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-71-2023, 2023
Kristie Hu, Jing Du, Xinghan Gong, Lingfei Ma, and Jonathan Li
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 4, 18, https://doi.org/10.5194/ica-adv-4-18-2023, https://doi.org/10.5194/ica-adv-4-18-2023, 2023
Yiping Chen, Huifang Feng, and Jonathan Li
Abstr. Int. Cartogr. Assoc., 6, 38, https://doi.org/10.5194/ica-abs-6-38-2023, https://doi.org/10.5194/ica-abs-6-38-2023, 2023
Kristie Hu and Jonathan Li
Abstr. Int. Cartogr. Assoc., 6, 94, https://doi.org/10.5194/ica-abs-6-94-2023, https://doi.org/10.5194/ica-abs-6-94-2023, 2023
S. Zhang, W. Huang, Y. Chen, S. Zhang, W. Zhang, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 571–578, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-571-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-571-2023, 2023
A. Nurunnabi, F. N. Teferle, D. F. Laefer, F. Remondino, I. R. Karas, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W3-2022, 111–118, https://doi.org/10.5194/isprs-archives-XLVIII-4-W3-2022-111-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W3-2022-111-2022, 2022
S. N. Fatholahi, C. Pan, L. Wang, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 111–116, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-111-2022, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-111-2022, 2022
H. He, K. Gao, W. Tan, L. Wang, S. N. Fatholahi, N. Chen, M. A. Chapman, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 31–37, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-31-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-31-2022, 2022
Z. He, H. He, J. Li, M. A. Chapman, and H. Ding
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 39–44, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-39-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-39-2022, 2022
A. Nurunnabi, F. N. Teferle, R. C. Lindenbergh, J. Li, and S. Zlatanova
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 59–66, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-59-2022, 2022
L. Wang, W. Tan, H. Xu, H. He, N. Chen, D. Li, M. A. Chapman, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 73–78, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-73-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-73-2022, 2022
M. Liu, G. Zhou, Z. Liu, L. Han, and J. Li
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 297–302, https://doi.org/10.5194/isprs-annals-V-3-2022-297-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-297-2022, 2022
A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and S. Parvaz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W5-2021, 397–404, https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-397-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-397-2021, 2021
Hongjie He, Zijian Jiang, Weikai Tan, Yuwei Cai, Sarah Narges Fatholahi, Kyle Gao, Hongzhang Xu, Bingxu Hu, Liyuan Qing, and Jonathan Li
Abstr. Int. Cartogr. Assoc., 3, 105, https://doi.org/10.5194/ica-abs-3-105-2021, https://doi.org/10.5194/ica-abs-3-105-2021, 2021
A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and A. Hunegnaw
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 31–38, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021, 2021
W. Zhu, W. Tan, L. Ma, D. Zhang, J. Li, and M. A. Chapman
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 39–44, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-39-2021, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-39-2021, 2021
S. Pirasteh, G. Shamsipour, G. Liu, R. Hajibagheri, A. Ghasemzadeh, M. Jokar, H. Zarbakhsh, F. Behnamfar, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1705–1708, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1705-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1705-2020, 2020
H. Guan, Y. Yu, D. Li, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 599–603, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-599-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-599-2020, 2020
S. N. Fatholahi, Y. Gu, M. Liu, L. Ma, Y. Chen, and J. Li
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 71–79, https://doi.org/10.5194/isprs-annals-V-1-2020-71-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-71-2020, 2020
H. Wu, Z. Xie, C. Wen, C. Wang, and J. Li
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 207–213, https://doi.org/10.5194/isprs-annals-V-1-2020-207-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-207-2020, 2020
Lingfei Ma, Tianyu Wu, Ying Li, Jonathan Li, Yiping Chen, and Michael Chapman
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 1, 12, https://doi.org/10.5194/ica-adv-1-12-2019, https://doi.org/10.5194/ica-adv-1-12-2019, 2019
H. Guan, Y. Yu, W. Yan, D. Li, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 989–993, https://doi.org/10.5194/isprs-archives-XLII-2-W13-989-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-989-2019, 2019
L. Ma, Z. Chen, Y. Li, D. Zhang, J. Li, and M. A. Chapman
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 79–86, https://doi.org/10.5194/isprs-archives-XLII-2-W13-79-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-79-2019, 2019
F. Chen, S. Lou, Q. Fan, J. Li, C. Wang, and M. Claverie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2619–2624, https://doi.org/10.5194/isprs-archives-XLII-3-2619-2018, https://doi.org/10.5194/isprs-archives-XLII-3-2619-2018, 2018
Weihong Cui, Guofeng Wang, Chenyi Feng, Yiwei Zheng, Jonathan Li, and Yi Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 195–203, https://doi.org/10.5194/isprs-archives-XLI-B7-195-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-195-2016, 2016
Shanxin Zhang, Cheng Wang, Zhuang Yang, Yiping Chen, and Jonathan Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 615–619, https://doi.org/10.5194/isprs-archives-XLI-B5-615-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-615-2016, 2016
Yuenan Li, Jianjun Wang, Cheng Chen, Yifei Chen, and Jonathan Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 721–727, https://doi.org/10.5194/isprs-archives-XLI-B2-721-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-721-2016, 2016
Yifei Chen, Yuenan Li, and Jonathan Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 695–702, https://doi.org/10.5194/isprs-archives-XLI-B2-695-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-695-2016, 2016
Haocheng Zhang, Jonathan Li, Ming Cheng, and Cheng Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 717–723, https://doi.org/10.5194/isprs-archives-XLI-B1-717-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-717-2016, 2016
Xinqu Chen and Jonathan Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 545–549, https://doi.org/10.5194/isprs-archives-XLI-B1-545-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-545-2016, 2016
H. Yan, Z. Wang, and J. Li
Geosci. Model Dev., 6, 1591–1599, https://doi.org/10.5194/gmd-6-1591-2013, https://doi.org/10.5194/gmd-6-1591-2013, 2013
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
Useful Links
Useful External Links
Our Contact
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org