J. Kahn

1.6k total citations
11 papers, 51 citations indexed

About

J. Kahn is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Geology. According to data from OpenAlex, J. Kahn has authored 11 papers receiving a total of 51 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Geology. Recurrent topics in J. Kahn's work include 3D Surveying and Cultural Heritage (3 papers), Particle physics theoretical and experimental studies (2 papers) and Advanced Neural Network Applications (2 papers). J. Kahn is often cited by papers focused on 3D Surveying and Cultural Heritage (3 papers), Particle physics theoretical and experimental studies (2 papers) and Advanced Neural Network Applications (2 papers). J. Kahn collaborates with scholars based in Germany, United States and France. J. Kahn's co-authors include Markus Götz, Rebekka Volk, Yu Hou, Frank Schultmann, Achim Streit, Marc Röger, Robert Pitz‐Paal, Tobias Hirsch, Christian Büscher and G. Dujany and has published in prestigious journals such as SHILAP Revista de lepidopterología, Solar Energy and Automation in Construction.

In The Last Decade

J. Kahn

10 papers receiving 49 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
J. Kahn Germany	5	16	11	10	7	7	11	51
Keno Fischer United States	4	27 1.7×	6 0.5×	12 1.2×	5 0.7×		5	74
Xinyi Zhao China	3	7 0.4×	12 1.1×	3 0.3×	6 0.9×	7 1.0×	6	51
D. Yu United States	5	5 0.3×	5 0.5×	12 1.2×	1 0.1×	6 0.9×	13	50
Tomáš Pětříček Czechia	4	12 0.8×	7 0.6×	3 0.3×	6 0.9×	2 0.3×	13	35
Himashi Peiris Australia	2	11 0.7×	29 2.6×	22 2.2×	9 1.3×	1 0.1×	7	70
Mehmet Gündüz Türkiye	2	5 0.3×	29 2.6×	16 1.6×	3 0.4×	1 0.1×	2	65
Samuel Müller United Kingdom	2	3 0.2×	5 0.5×	16 1.6×	5 0.7×		3	109
Lennart Purucker Germany	2	3 0.2×	5 0.5×	18 1.8×	4 0.6×		3	108
Han Guo China	4	7 0.4×	2 0.2×	5 0.5×	7 1.0×		14	37
YuXuan Liu United States	3	3 0.2×	9 0.8×	13 1.3×	2 0.3×		4	22

Countries citing papers authored by J. Kahn

Since Specialization

Citations

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

Fields of papers citing papers by J. Kahn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Kahn

This figure shows the co-authorship network connecting the top 25 collaborators of J. Kahn. A scholar is included among the top collaborators of J. Kahn 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 J. Kahn. J. Kahn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Kahn, J., et al.. (2025). Enhancing UAS-Based Multispectral Semantic Segmentation Through Feature Engineering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 6206–6216. 3 indexed citations

2.

Kahn, J., et al.. (2024). Organising AI for safety: Identifying structural vulnerabilities to guide the design of AI-enhanced socio-technical systems. Safety Science. 184. 106731–106731. 1 indexed citations

3.

Kahn, J., Markus Götz, Yu Hou, et al.. (2023). Thermal Bridges on Building Rooftops. Scientific Data. 10(1). 4 indexed citations

4.

Kahn, J., et al.. (2023). Soiling determination for parabolic trough collectors based on operational data analysis and machine learning. Solar Energy. 259. 257–276. 6 indexed citations

5.

Götz, Markus, et al.. (2022). Accelerating neural network training with distributed asynchronous and selective optimization (DASO). Journal Of Big Data. 9(1). 5 indexed citations

6.

Kahn, J., et al.. (2022). Deep learning approaches to building rooftop thermal bridge detection from aerial images. Automation in Construction. 146. 104690–104690. 22 indexed citations

7.

Kahn, J., G. Dujany, P. Goldenzweig, et al.. (2022). Learning tree structures from leaves for particle decay reconstruction. Machine Learning Science and Technology. 3(3). 35012–35012. 5 indexed citations

8.

Kahn, J., et al.. (2021). AI-based thermal bridge detection of building rooftops on district scale using aerial images. Repository KITopen (Karlsruhe Institute of Technology). 3 indexed citations

9.

Kahn, J., et al.. (2020). Selective background Monte Carlo simulation at Belle II. SHILAP Revista de lepidopterología. 245. 2028–2028. 1 indexed citations

10.

Kahn, J.. (2019). Hadronic tag sensitivity study of B → K(*)vˉv and selective background Monte Carlo Simulation at Belle II. Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München). 1 indexed citations

11.

Kahn, J.. (2016). The Belle II Experiment. CERN Bulletin. 1. 45–45.

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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