Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network
Marc Wieland, Yu Li, Sandro Martinis
Remote Sensing of Environment (2019) Vol. 230, pp. 111203-111203
Open Access | Times Cited: 178

Showing 1-25 of 178 citing articles:

A comprehensive review of deep learning applications in hydrology and water resources
Muhammed Sit, Bekir Zahit Demiray, Zhongrun Xiang, et al.
Water Science & Technology (2020) Vol. 82, Iss. 12, pp. 2635-2670
Open Access | Times Cited: 403
Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
Danfeng Hong, Bing Zhang, Hao Li, et al.
Remote Sensing of Environment (2023) Vol. 299, pp. 113856-113856
Open Access | Times Cited: 276
Remote sensing image segmentation advances: A meta-analysis
Ioannis Kotaridis, Μαρία Λαζαρίδου
ISPRS Journal of Photogrammetry and Remote Sensing (2021) Vol. 173, pp. 309-322
Closed Access | Times Cited: 202
Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning
Yansheng Li, Wei Chen, Yongjun Zhang, et al.
Remote Sensing of Environment (2020) Vol. 250, pp. 112045-112045
Closed Access | Times Cited: 165
Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2
Sergii Skakun, Jan Wevers, Carsten Brockmann, et al.
Remote Sensing of Environment (2022) Vol. 274, pp. 112990-112990
Open Access | Times Cited: 165
Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1
Derrick Bonafilia, Beth Tellman, Tyler Anderson, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020), pp. 835-845
Closed Access | Times Cited: 164
DABNet: Deformable Contextual and Boundary-Weighted Network for Cloud Detection in Remote Sensing Images
Qibin He, Xian Sun, Zhiyuan Yan, et al.
IEEE Transactions on Geoscience and Remote Sensing (2021) Vol. 60, pp. 1-16
Closed Access | Times Cited: 156
Towards global flood mapping onboard low cost satellites with machine learning
Gonzalo Mateo‐García, Joshua Veitch-Michaelis, Lewis Smith, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 147
Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects
Zhiwei Li, Huanfeng Shen, Qihao Weng, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2022) Vol. 188, pp. 89-108
Closed Access | Times Cited: 101
Global exponential synchronization of discrete-time high-order switched neural networks and its application to multi-channel audio encryption
Zeyu Dong, Xin Wang, Xian Zhang, et al.
Nonlinear Analysis Hybrid Systems (2022) Vol. 47, pp. 101291-101291
Closed Access | Times Cited: 85
Advances in solar forecasting: Computer vision with deep learning
Quentin Paletta, Guillermo Terrén-Serrano, Yuhao Nie, et al.
Advances in Applied Energy (2023) Vol. 11, pp. 100150-100150
Open Access | Times Cited: 44
Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure
Alwaseela Abdalla, Haiyan Cen, Liang Wan, et al.
Computers and Electronics in Agriculture (2019) Vol. 167, pp. 105091-105091
Closed Access | Times Cited: 133
Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN)
Michal Segal‐Rozenhaimer, Alan Li, Kamalika Das, et al.
Remote Sensing of Environment (2019) Vol. 237, pp. 111446-111446
Open Access | Times Cited: 132
A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data
Lisa Knopp, Marc Wieland, Michaela Rättich, et al.
Remote Sensing (2020) Vol. 12, Iss. 15, pp. 2422-2422
Open Access | Times Cited: 114
A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images
Miguel M. Pinto, Renata Libonati, Ricardo M. Trigo, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 160, pp. 260-274
Closed Access | Times Cited: 107
CDnetV2: CNN-Based Cloud Detection for Remote Sensing Imagery With Cloud-Snow Coexistence
Jianhua Guo, Jingyu Yang, Huanjing Yue, et al.
IEEE Transactions on Geoscience and Remote Sensing (2020) Vol. 59, Iss. 1, pp. 700-713
Closed Access | Times Cited: 96
A Modular Processing Chain for Automated Flood Monitoring from Multi-Spectral Satellite Data
Marc Wieland, Sandro Martinis
Remote Sensing (2019) Vol. 11, Iss. 19, pp. 2330-2330
Open Access | Times Cited: 91
Simultaneous Cloud Detection and Removal From Bitemporal Remote Sensing Images Using Cascade Convolutional Neural Networks
Shunping Ji, Peiyu Dai, Meng Lü, et al.
IEEE Transactions on Geoscience and Remote Sensing (2020) Vol. 59, Iss. 1, pp. 732-748
Open Access | Times Cited: 81
Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach
Osmar Luiz Ferreira de Carvalho, Osmar Abílio de Carvalho Júnior, Anesmar Olino de Albuquerque, et al.
Remote Sensing (2020) Vol. 13, Iss. 1, pp. 39-39
Open Access | Times Cited: 79
Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches
Jing Wei, Wei Huang, Zhanqing Li, et al.
Remote Sensing of Environment (2020) Vol. 248, pp. 112005-112005
Open Access | Times Cited: 78
Assessment of machine learning classifiers for global lake ice cover mapping from MODIS TOA reflectance data
Yuhao Wu, Claude Duguay, Linlin Xu
Remote Sensing of Environment (2020) Vol. 253, pp. 112206-112206
Closed Access | Times Cited: 78
Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network
Yanan Guo, Xiaoqun Cao, Bainian Liu, et al.
Symmetry (2020) Vol. 12, Iss. 6, pp. 1056-1056
Open Access | Times Cited: 76
Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset
Shuang Luo, Huifang Li, Huanfeng Shen
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 167, pp. 443-457
Closed Access | Times Cited: 73
Strip pooling channel spatial attention network for the segmentation of cloud and cloud shadow
Yi Qu, Min Xia, Yonghong Zhang
Computers & Geosciences (2021) Vol. 157, pp. 104940-104940
Closed Access | Times Cited: 72
Application of deep learning in ecological resource research: Theories, methods, and challenges
Qinghua Guo, Shichao Jin, Min Li, et al.
Science China Earth Sciences (2020) Vol. 63, Iss. 10, pp. 1457-1474
Closed Access | Times Cited: 71
Page 1 - Next Page

Scroll to top