Daniele De Martini
- Aerospace Engineering top 5%
- Robotics and Sensor-Based Localization 15
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- Remote Sensing and LiDAR Applications 4
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- Indoor and Outdoor Localization Technologies 7
- Electric Vehicles and Infrastructure 5
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- Anomaly Detection Techniques and Applications 5
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- Autonomous Vehicle Technology and Safety 5
- Advanced Battery Technologies Research 4
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- Fault Detection and Control Systems 4
- Co-authors
- Paul NewmanMatthew GaddTullio FacchinettiChangyang SheGuodong ZhaoShangzhe WuPaul A. NewmanP. R. Newman
- Journals
- SHILAP Revista de lepidopterología (1 paper)Scientific Reports (1 paper)IEEE Journal on Selected Areas in Communications (2 papers)
- Partner nations
- United KingdomItalyUnited States
In The Last Decade
Daniele De Martini
47 papers receiving 432 citations
Peers
Comparison fields: 5 of 74
- Aerospace Engineering 224
- Computer Vision and Pattern Recognition 137
- Instrumentation 22
- Geology 23
- Environmental Engineering 59
Countries citing papers authored by Daniele De Martini
This map shows the geographic impact of Daniele De Martini'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 Daniele De Martini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniele De Martini more than expected).
Fields of papers citing papers by Daniele De Martini
This network shows the impact of papers produced by Daniele De Martini. 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 Daniele De Martini. The network helps show where Daniele De Martini may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Daniele De Martini, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2024 | 9 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 1 | |
| 8 | 2024 | 3 | |
| 9 | 2023 | 4 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 0 | |
| 12 | 2023 | 1 | |
| 13 | 2023 | 1 | |
| 14 | 2023 | 0 | |
| 15 | 2022 | 23 | |
| 16 | 2021 | 22 | |
| 17 | 2021 | 1 | |
| 18 | Fool me once: robust selective segmentation via out-of-distribution detection with contrastive learning | 2021 | 5 |
| 19 | 2020 | 1 | |
| 20 | 2019 | 48 |
About Daniele De Martini
Daniele De Martini is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Aerospace Engineering, having authored 56 papers that have together received 451 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (15 papers), Indoor and Outdoor Localization Technologies (7 papers), Electric Vehicles and Infrastructure (5 papers), Anomaly Detection Techniques and Applications (5 papers), Autonomous Vehicle Technology and Safety (5 papers), Fault Detection and Control Systems (4 papers), Remote Sensing and LiDAR Applications (4 papers) and Advanced Battery Technologies Research (4 papers). The work is most often cited by research in Aerospace Engineering (224 citations), Computer Vision and Pattern Recognition (137 citations) and Instrumentation (22 citations). Daniele De Martini has collaborated with scholars based in United Kingdom, Italy and United States. Frequent co-authors include Paul Newman, Matthew Gadd, Tullio Facchinetti, Changyang She, Guodong Zhao, Shangzhe Wu, Paul A. Newman, P. R. Newman, Ingmar Posner and Lars Kunze. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Journal on Selected Areas in Communications.
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.