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:

Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data
Zayd Mahmoud Hamdi, Melanie Brandmeier, Christoph Straub
Remote Sensing (2019) Vol. 11, Iss. 17, pp. 1976-1976
Open Access | Times Cited: 120

Showing 1-25 of 120 citing articles:

Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
Teja Kattenborn, Jens Leitloff, Felix Schiefer, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2021) Vol. 173, pp. 24-49
Open Access | Times Cited: 1178
A review on deep learning in UAV remote sensing
Lucas Prado Osco, José Marcato, Ana Paula Marques Ramos, et al.
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 102, pp. 102456-102456
Open Access | Times Cited: 340
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis
Lazhar Khelifi, Max Mignotte
IEEE Access (2020) Vol. 8, pp. 126385-126400
Open Access | Times Cited: 303
Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks
Felix Schiefer, Teja Kattenborn, Annett Frick, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2020) Vol. 170, pp. 205-215
Open Access | Times Cited: 266
Explainable identification and mapping of trees using UAV RGB image and deep learning
Masanori Onishi, Takeshi Ise
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 189
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications
Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer
Remote Sensing (2020) Vol. 12, Iss. 18, pp. 3053-3053
Open Access | Times Cited: 148
Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current approaches, open challenges, and future opportunities
Claudio Persello, Jan Dirk Wegner, Ronny Hänsch, et al.
IEEE Geoscience and Remote Sensing Magazine (2022) Vol. 10, Iss. 2, pp. 172-200
Open Access | Times Cited: 89
Comprehensive Survey of Artificial Intelligence Techniques and Strategies for Climate Change Mitigation
Zahra Mohtasham‐Amiri, Arash Heidari, Nima Jafari Navimipour
Energy (2024) Vol. 308, pp. 132827-132827
Closed Access | Times Cited: 27
Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery
Jonathan V. Solórzano, Jean‐François Mas, Yan Gao, et al.
Remote Sensing (2021) Vol. 13, Iss. 18, pp. 3600-3600
Open Access | Times Cited: 95
Forest 4.0: Digitalization of forest using the Internet of Things (IoT)
Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, et al.
Journal of King Saud University - Computer and Information Sciences (2021) Vol. 34, Iss. 8, pp. 5587-5601
Open Access | Times Cited: 91
Earth Observation Based Monitoring of Forests in Germany: A Review
Stefanie Holzwarth, Frank Thonfeld, Sahra Abdullahi, et al.
Remote Sensing (2020) Vol. 12, Iss. 21, pp. 3570-3570
Open Access | Times Cited: 79
Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics
Dyah Retno Panuju, David Paull, Amy L. Griffin
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1781-1781
Open Access | Times Cited: 72
Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region
Giandomenico De Luca, João M. N. Silva, Salvatore Di Fazio, et al.
European Journal of Remote Sensing (2022) Vol. 55, Iss. 1, pp. 52-70
Open Access | Times Cited: 66
Extending deep learning approaches for forest disturbance segmentation on very high‐resolution satellite images
Dmitry E. Kislov, Kirill A. Korznikov, Jan Altman, et al.
Remote Sensing in Ecology and Conservation (2021) Vol. 7, Iss. 3, pp. 355-368
Open Access | Times Cited: 62
Microplastics pollution in the soil mulched by dust-proof nets: A case study in Beijing, China
Yixiang Chen, Yihang Wu, Jin Ma, et al.
Environmental Pollution (2021) Vol. 275, pp. 116600-116600
Closed Access | Times Cited: 61
Deep learning for forest inventory and planning: a critical review on the remote sensing approaches so far and prospects for further applications
Alireza Hamedianfar, Cheikh Mohamedou, Annika Kangas, et al.
Forestry An International Journal of Forest Research (2022) Vol. 95, Iss. 4, pp. 451-465
Open Access | Times Cited: 43
Efficient High-Resolution Deep Learning: A Survey
Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
ACM Computing Surveys (2024) Vol. 56, Iss. 7, pp. 1-35
Open Access | Times Cited: 10
A Machine Learning Approach for Filling Long Gaps in Eddy Covariance Time Series Data in a Tropical Dry Forest
Mohammed Abdaki, Arturo Sánchez‐Azofeifa, Hendrik F. Hamann
Journal of Geophysical Research Biogeosciences (2025) Vol. 130, Iss. 1
Closed Access | Times Cited: 1
Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning
Dmitry E. Kislov, Kirill A. Korznikov
Remote Sensing (2020) Vol. 12, Iss. 7, pp. 1145-1145
Open Access | Times Cited: 56
Using U-Net-Like Deep Convolutional Neural Networks for Precise Tree Recognition in Very High Resolution RGB (Red, Green, Blue) Satellite Images
Kirill A. Korznikov, Dmitry E. Kislov, Jan Altman, et al.
Forests (2021) Vol. 12, Iss. 1, pp. 66-66
Open Access | Times Cited: 56
Mapping forest windthrows using high spatial resolution multispectral satellite images
Michele Dalponte, Sebastian Marzini, Yady Tatiana Solano‐Correa, et al.
International Journal of Applied Earth Observation and Geoinformation (2020) Vol. 93, pp. 102206-102206
Open Access | Times Cited: 52
Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms
Francesca Giannetti, Matteo Pecchi, Davide Travaglini, et al.
Forests (2021) Vol. 12, Iss. 6, pp. 680-680
Open Access | Times Cited: 44
Automatic Detection of Collapsed Buildings after the 6 February 2023 Türkiye Earthquakes Using Post-Disaster Satellite Images with Deep Learning-Based Semantic Segmentation Models
Kemal Hacıefendioğlu, Hasan Basri Başağa, Volkan Kahya, et al.
Buildings (2024) Vol. 14, Iss. 3, pp. 582-582
Open Access | Times Cited: 7
Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height
İlker Ercanlı
Forest Ecosystems (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 41
Satellite open data to monitor forest damage caused by extreme climate-induced events: a case study of the Vaia storm in Northern Italy
Gaia Vaglio Laurin, Saverio Francini, Tania Luti, et al.
Forestry An International Journal of Forest Research (2020) Vol. 94, Iss. 3, pp. 407-416
Open Access | Times Cited: 40
Page 1 - Next Page

Scroll to top