Marco Seeland

2.0k total citations
30 papers, 856 citations indexed

About

Marco Seeland is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Ecological Modeling. According to data from OpenAlex, Marco Seeland has authored 30 papers receiving a total of 856 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Electrical and Electronic Engineering, 7 papers in Artificial Intelligence and 6 papers in Ecological Modeling. Recurrent topics in Marco Seeland's work include Organic Electronics and Photovoltaics (11 papers), Thin-Film Transistor Technologies (8 papers) and Species Distribution and Climate Change (6 papers). Marco Seeland is often cited by papers focused on Organic Electronics and Photovoltaics (11 papers), Thin-Film Transistor Technologies (8 papers) and Species Distribution and Climate Change (6 papers). Marco Seeland collaborates with scholars based in Germany, Switzerland and Austria. Marco Seeland's co-authors include Patrick Mäder, Jana Wäldchen, Michael Rzanny, Harald Hoppe, Roland Rösch, David Boho, Hans Christian Wittich, Lars Müller‐Meskamp, Hannes Klumbies and Martin Hermenau and has published in prestigious journals such as Applied Physics Letters, PLoS ONE and Journal of Applied Physics.

In The Last Decade

Marco Seeland

28 papers receiving 822 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Seeland Germany 14 227 218 164 139 115 30 856
Huiqin Ma China 22 979 4.3× 66 0.3× 38 0.2× 714 5.1× 24 0.2× 44 1.5k
Jianming Du China 23 455 2.0× 137 0.6× 8 0.0× 89 0.6× 103 0.9× 85 1.6k
Xingyao Zhang China 18 483 2.1× 185 0.8× 12 0.1× 155 1.1× 48 0.4× 124 998
Roberto Alves Braga Brazil 24 435 1.9× 44 0.2× 5 0.0× 48 0.3× 40 0.3× 110 1.6k
Henrik Skov Midtiby Denmark 14 688 3.0× 21 0.1× 21 0.1× 248 1.8× 19 0.2× 36 1.0k
I.J. Bennett Netherlands 19 293 1.3× 267 1.2× 8 0.0× 45 0.3× 67 0.6× 95 1.1k
Dan Jeric Arcega Rustia Taiwan 11 251 1.1× 29 0.1× 20 0.1× 55 0.4× 60 0.5× 23 483
Xinjie Zhang China 14 39 0.2× 138 0.6× 4 0.0× 65 0.5× 27 0.2× 64 925
Dujuan Zhang China 14 83 0.4× 10 0.0× 19 0.1× 200 1.4× 20 0.2× 40 580
Tsu‐Wei Chen Germany 18 623 2.7× 459 2.1× 7 0.0× 64 0.5× 70 0.6× 44 2.0k

Countries citing papers authored by Marco Seeland

Since Specialization
Citations

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

Fields of papers citing papers by Marco Seeland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Seeland

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Seeland. A scholar is included among the top collaborators of Marco Seeland 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 Marco Seeland. Marco Seeland 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.
Mäder, Patrick, et al.. (2025). Privacy preserving federated learning with convolutional variational bottlenecks. Cybersecurity. 8(1). 2 indexed citations
2.
Mäder, Patrick, et al.. (2024). Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data. Applied Artificial Intelligence. 38(1). 3 indexed citations
3.
Seeland, Marco, et al.. (2023). Dropout Is NOT All You Need to Prevent Gradient Leakage. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9733–9741. 8 indexed citations
4.
Mäder, Patrick, David Boho, Michael Rzanny, et al.. (2021). The Flora Incognita app – Interactive plant species identification. Methods in Ecology and Evolution. 12(7). 1335–1342. 79 indexed citations
5.
Mahecha, Miguel D., Michael Rzanny, Guido Kraemer, et al.. (2021). Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients. Ecography. 44(8). 1131–1142. 19 indexed citations
6.
Seeland, Marco & Patrick Mäder. (2021). Multi-view classification with convolutional neural networks. PLoS ONE. 16(1). e0245230–e0245230. 82 indexed citations
7.
Boho, David, Michael Rzanny, Jana Wäldchen, et al.. (2020). Flora Capture: a citizen science application for collecting structured plant observations. BMC Bioinformatics. 21(1). 576–576. 32 indexed citations
8.
Seeland, Marco, Michael Rzanny, David Boho, Jana Wäldchen, & Patrick Mäder. (2019). Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinformatics. 20(1). 4–4. 39 indexed citations
9.
Wäldchen, Jana, Michael Rzanny, Marco Seeland, & Patrick Mäder. (2018). Automated plant species identification—Trends and future directions. PLoS Computational Biology. 14(4). e1005993–e1005993. 205 indexed citations
10.
Hofmann, Martin, Marco Seeland, & Patrick Mäder. (2018). Efficiently Annotating Object Images with Absolute Size Information Using Mobile Devices. International Journal of Computer Vision. 127(2). 207–224. 9 indexed citations
11.
Züfle, Simon, Marco Seeland, Roland Roesch, et al.. (2018). Aluminum Electrode Insulation Dynamics via Interface Oxidation by Reactant Diffusion in Organic Layers. physica status solidi (a). 215(23). 3 indexed citations
12.
Wittich, Hans Christian, Marco Seeland, Jana Wäldchen, Michael Rzanny, & Patrick Mäder. (2018). Recommending plant taxa for supporting on-site species identification. BMC Bioinformatics. 19(1). 190–190. 20 indexed citations
13.
Seeland, Marco, et al.. (2017). Plant species classification using flower images—A comparative study of local feature representations. PLoS ONE. 12(2). e0170629–e0170629. 65 indexed citations
14.
Rzanny, Michael, Marco Seeland, Jana Wäldchen, & Patrick Mäder. (2017). Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods. 13(1). 97–97. 48 indexed citations
15.
Seeland, Marco & Harald Hoppe. (2015). Comparison of distributed vs. lumped series resistance modeling of thin-film solar cells and modules: Influence on the geometry-dependent efficiency. physica status solidi (a). 212(9). 1991–2000. 13 indexed citations
16.
17.
Roesch, Roland, Marco Seeland, Maik Bärenklau, G. Gobsch, & Harald Hoppe. (2013). Stability of polymer solar cells: Dependence on working pressure. Solar Energy Materials and Solar Cells. 111. 212–215. 8 indexed citations
18.
Seeland, Marco, Roland Rösch, & Harald Hoppe. (2012). Quantitative analysis of electroluminescence images from polymer solar cells. Journal of Applied Physics. 111(2). 18 indexed citations
19.
Hoppe, Harald, et al.. (2011). Optimal geometric design of monolithic thin-film solar modules: Architecture of polymer solar cells. Solar Energy Materials and Solar Cells. 97. 119–126. 44 indexed citations
20.
Seeland, Marco, Roland Rösch, & Harald Hoppe. (2011). Luminescence imaging of polymer solar cells: Visualization of progressing degradation. Journal of Applied Physics. 109(6). 51 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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