Donglin Di

675 citations
40 papers · 374 · h-index 10

Impact in

Papers in

Donglin Di

29 papers receiving 366 citations

Peers

Donglin Di
Comparison fields: 5 of 66
  • Computer Vision and Pattern Recognition 179
  • Health Informatics 8
  • Media Technology 50
  • Artificial Intelligence 140
  • Geology 20
Replace Langming Liu with:
Langming Liu China
Yude Wang China
Dmytro Mishkin Czechia
Zhibin Wang China
В. В. Старовойтов Belarus
Baigui Sun China
Amit Verma India
Xin Lai Hong Kong
Iqbaldeep Kaur India
Tao Lu China
Donglin Di relative to Langming Liu China Langming Liu's profile →
Citations per field
00.5×
Langming Liu · 1×
Citations per year

Countries citing papers authored by Donglin Di

Since Specialization
Citations

This map shows the geographic impact of Donglin Di'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 Donglin Di with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Donglin Di more than expected).

Fields of papers citing papers by Donglin Di

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Donglin Di. 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 Donglin Di. The network helps show where Donglin Di may publish in the future.

Co-authors

The 25 scholars most cited alongside Donglin Di, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Donglin Di Line = papers co-authored together Donglin Di links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201996
2 202054
3 202235
4 202231
5 202227
6 202217
7 202116
8 202215
9 202212
10 201911
11 20198
12 20218
13 20257
14 20226
15 20255
16 20195
17 20233
18 20253
19 20253
20 20252

About Donglin Di

Donglin Di is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Control and Systems Engineering and Computational Mechanics, having authored 40 papers that have together received 374 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (7 papers), Remote-Sensing Image Classification (7 papers), Human Pose and Action Recognition (6 papers), Advanced Image and Video Retrieval Techniques (5 papers), Anomaly Detection Techniques and Applications (4 papers), AI in cancer detection (4 papers), Advanced Image Fusion Techniques (4 papers) and 3D Shape Modeling and Analysis (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (179 citations), Health Informatics (8 citations), Media Technology (50 citations), Artificial Intelligence (140 citations) and Geology (20 citations). Donglin Di has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Weipeng Jing, Tat‐Seng Chua, Xindi Shang, Guangsheng Chen, Junbin Xiao, Xun Yang, Yu Cao, Linhui Li, Houbing Song and Yue Gao. Their work appears in journals such as IEEE Geoscience and Remote Sensing Letters, Pattern Recognition, Remote Sensing, IEEE Transactions on Instrumentation and Measurement and Knowledge-Based Systems.

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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