Geoffrey Pascoe

2.0k citations
9 papers · 1.2k · 1 hit paper · h-index 9

Impact in

Papers in

Geoffrey Pascoe

9 papers receiving 1.2k citations

Geoffrey Pascoe's Hit Papers

1 year, 1000 km: The Oxford RobotCar dataset 2016 · 985 citations
9850+3+6Years since publication250500750

Peers

Geoffrey Pascoe
Comparison fields: 5 of 73
  • Computer Vision and Pattern Recognition 870
  • Geology 196
  • Aerospace Engineering 751
  • Automotive Engineering 177
  • Environmental Engineering 166
Replace Will Maddern with:
Will Maddern United Kingdom
Lionel Heng Switzerland
Shenghai Yuan Singapore
Henning Lategahn Germany
Abhinav Valada Germany
Tianwei Yin United States
Michel Devy France
Cédric Demonceaux France
Frank Moosmann Germany
Oleg Naroditsky United States
Geoffrey Pascoe relative to Will Maddern United Kingdom Will Maddern's profile →
Citations per field
00.5×1.5×
Will Maddern · 1×
Citations per year

Countries citing papers authored by Geoffrey Pascoe

Since Specialization
Citations

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

Fields of papers citing papers by Geoffrey Pascoe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 14 scholars most cited alongside Geoffrey Pascoe, 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 Geoffrey Pascoe Line = papers co-authored together Geoffrey Pascoe links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1
1 year, 1000 km: The Oxford RobotCar dataset
Hit paper breakdown →
2016985
2 201544
3 201739
4 201539
5 201838
6 201529
7 201526
8 201418
9 20229

About Geoffrey Pascoe

Geoffrey Pascoe is a scholar working on Aerospace Engineering, Computer Vision and Pattern Recognition, Environmental Engineering, Geology and Molecular Biology, having authored 9 papers that have together received 1.2k indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (8 papers), Advanced Vision and Imaging (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Remote Sensing and LiDAR Applications (3 papers), 3D Surveying and Cultural Heritage (2 papers), Video Surveillance and Tracking Methods (1 paper), Gene Regulatory Network Analysis (1 paper) and Ecosystem dynamics and resilience (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (870 citations), Geology (196 citations), Aerospace Engineering (751 citations), Automotive Engineering (177 citations) and Environmental Engineering (166 citations). Geoffrey Pascoe has collaborated with scholars based in United Kingdom, Australia and United States. Frequent co-authors include Paul Newman, Will Maddern, Dan Barnes, Ingmar Posner, Michael G. Tanner, Pedro Piniés, Alexander D. Stewart, Bernd Meyer, Tanner Schmidt and Richard Szeliski. Their work appears in journals such as The International Journal of Robotics Research, PLoS ONE, Oxford University Research Archive (ORA) (University of Oxford) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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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