Zhanlong Chen

2.4k total citations · 1 hit paper
79 papers, 1.9k citations indexed

About

Zhanlong Chen is a scholar working on Computer Vision and Pattern Recognition, Ocean Engineering and Atmospheric Science. According to data from OpenAlex, Zhanlong Chen has authored 79 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 17 papers in Ocean Engineering and 17 papers in Atmospheric Science. Recurrent topics in Zhanlong Chen's work include Geographic Information Systems Studies (14 papers), Automated Road and Building Extraction (13 papers) and Data Management and Algorithms (13 papers). Zhanlong Chen is often cited by papers focused on Geographic Information Systems Studies (14 papers), Automated Road and Building Extraction (13 papers) and Data Management and Algorithms (13 papers). Zhanlong Chen collaborates with scholars based in China, Pakistan and Italy. Zhanlong Chen's co-authors include Zhong Xie, Yongyang Xu, Liang Wu, Muhammad Afaq Hussain, Muhammad Shoaib, Wenhao Yu, Sheng Hu, Zhanjun He, Safeer Ullah Shah and Zhong Xie and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Zhanlong Chen

75 papers receiving 1.8k citations

Hit Papers

Building Extraction in Very High Resolution Remote Sensin... 2018 2026 2020 2023 2018 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Zhanlong Chen China 24 611 584 470 394 349 79 1.9k
Mohammad Javad Valadan Zoej Iran 23 709 1.2× 634 1.1× 833 1.8× 304 0.8× 255 0.7× 152 2.2k
Tinghua Ai China 29 311 0.5× 799 1.4× 475 1.0× 521 1.3× 717 2.1× 176 2.8k
Dongping Ming China 23 866 1.4× 303 0.5× 395 0.8× 309 0.8× 529 1.5× 101 1.8k
Jiasong Zhu China 28 1.0k 1.7× 254 0.4× 308 0.7× 648 1.6× 208 0.6× 93 2.5k
Manfred Ehlers Germany 22 939 1.5× 172 0.3× 371 0.8× 351 0.9× 298 0.9× 73 2.0k
Yongyang Xu China 24 855 1.4× 697 1.2× 752 1.6× 471 1.2× 325 0.9× 62 2.0k
Zhong Xie China 28 627 1.0× 562 1.0× 609 1.3× 568 1.4× 186 0.5× 116 2.7k
Hao Wu China 23 171 0.3× 207 0.4× 436 0.9× 128 0.3× 701 2.0× 157 2.0k
Zelang Miao China 23 693 1.1× 635 1.1× 665 1.4× 275 0.7× 241 0.7× 63 1.7k
Guojin He China 31 502 0.8× 253 0.4× 1000 2.1× 392 1.0× 1.4k 4.0× 179 3.0k

Countries citing papers authored by Zhanlong Chen

Since Specialization
Citations

This map shows the geographic impact of Zhanlong Chen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Zhanlong Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhanlong Chen more than expected).

Fields of papers citing papers by Zhanlong Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhanlong Chen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Zhanlong Chen. The network helps show where Zhanlong Chen may publish in the future.

Co-authorship network of co-authors of Zhanlong Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Zhanlong Chen. A scholar is included among the top collaborators of Zhanlong Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Zhanlong Chen. Zhanlong Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chen, Zhanlong, et al.. (2025). Groundwater vulnerability to arsenic in semi-arid region of Pakistan: Sources identification, spatial analysis, and public health risks evaluation. Desalination and Water Treatment. 324. 101481–101481. 1 indexed citations
2.
Hussain, Muhammad Afaq, et al.. (2025). Hybrid heterogeneous ensemble learning framework for flood susceptibility mapping in Balochistan, Pakistan. Journal of Hydrology Regional Studies. 61. 102718–102718. 1 indexed citations
3.
Hussain, Muhammad Afaq, et al.. (2025). Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas. Landslides. 22(6). 2089–2103. 7 indexed citations
4.
Hussain, Muhammad Afaq, et al.. (2025). Spatial analysis of flood susceptibility in Coastal area of Pakistan using machine learning models and SAR imagery. Environmental Earth Sciences. 84(5). 3 indexed citations
5.
Qiu, Qinjun, Tian Miao, Liufeng Tao, et al.. (2025). A deep learning architecture for aligning cross-domain geographic knowledge graph. International Journal of Geographical Information Systems. 39(8). 1823–1848. 1 indexed citations
6.
Chen, Zhanlong, et al.. (2024). Combining Contextual Information by Integrated Attention Mechanism in Convolutional Neural Networks for Digital Elevation Model Super-Resolution. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–16. 2 indexed citations
7.
Chen, Zhanlong, Rui Wang, & Yongyang Xu. (2024). Semi-Supervised Remote Sensing Building Change Detection with Joint Perturbation and Feature Complementation. Remote Sensing. 16(18). 3424–3424. 2 indexed citations
8.
Chen, Zhanlong, et al.. (2023). Intelligent terrain generation considering global information and terrain patterns. Computers & Geosciences. 182. 105482–105482.
11.
Li, Houpu, et al.. (2023). A Global-Information-Constrained Deep Learning Network for Digital Elevation Model Super-Resolution. Remote Sensing. 15(2). 305–305. 19 indexed citations
12.
Hussain, Muhammad Afaq, et al.. (2023). Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data. Remote Sensing. 15(19). 4703–4703. 36 indexed citations
13.
Chen, Zhanlong, et al.. (2021). Research on the interactive mode of residents’ behavior based on trajectory data mining. SHILAP Revista de lepidopterología. 1 indexed citations
14.
Fu, Ying, et al.. (2021). Translation of Aerial Image Into Digital Map via Discriminative Segmentation and Creative Generation. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–15. 10 indexed citations
15.
Chen, Zhanlong, et al.. (2021). Measuring the similarity of building patterns using Graph Fourier transform. Earth Science Informatics. 14(4). 1953–1971. 7 indexed citations
16.
Chen, Zhanlong, et al.. (2016). Establishment of the Comprehensive Shape Similarity Model for Complex Polygon Entity by Using Bending Mutilevel Chord Complex Function. SHILAP Revista de lepidopterología. 2 indexed citations
17.
Chen, Zhanlong, et al.. (2016). A Quantitative Calculation Method of Composite Spatial Direction Similarity Concerning Scale Differences. SHILAP Revista de lepidopterología. 3 indexed citations
18.
Chen, Zhanlong, et al.. (2015). A Quantitative Calculation Method of Spatial Direction Similarity Based on Direction Relation Matrix. SHILAP Revista de lepidopterología. 2 indexed citations
19.
Wu, Liang, et al.. (2015). A Geospatial Information Grid Framework for Geological Survey. PLoS ONE. 10(12). e0145312–e0145312. 9 indexed citations
20.
Xie, Zhong, et al.. (2010). The Research and Design of the Application Domain Building Based on GridGIS. Journal of Geographic Information System. 2(1). 32–39. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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