Thomas Esch

1.6k citations
35 papers · 1.2k · 1 hit paper · h-index 13

Impact in

Papers in

Thomas Esch

31 papers receiving 1.1k citations

Thomas Esch's Hit Papers

Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks 2015 · 493 citations
4930+3+7Years since publication100200300400

Peers

Thomas Esch
Comparison fields: 5 of 107
  • Media Technology 434
  • Global and Planetary Change 319
  • Environmental Engineering 202
  • Atmospheric Science 234
  • Computer Vision and Pattern Recognition 214
Replace Zhiling Guo with:
Zhiling Guo Japan
Zhiqiang Du China
Yan Jia China
Rui Cao China
Thierry Ranchin France
Piero Boccardo Italy
Wen Liu Japan
M. Chica-Rivas Spain
Chao‐Hung Lin Taiwan
Lu Xu China
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Countries citing papers authored by Thomas Esch

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Esch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Thomas Esch, 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 Thomas Esch Line = papers co-authored together Thomas Esch links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks
Hit paper breakdown →
2015493
2 2014144
3 201187
4 201079
5 202371
6 201459
7 202056
8
Heat transfer predictions based on two-equation turbulence models with advanced wall treatment
200351
9 201519
10 199118
11 200217
12 202413
13 201212
14 202511
15 20009
16 20198
17 20255
18 20204
19 20244
20
EXTENSION AND VALIDATION OF THE CAB DROPLET BREAKUP MODEL TO A WIDE WEBER NUMBER RANGE
20084

About Thomas Esch

Thomas Esch is a scholar working on Automotive Engineering, Media Technology, Mechanical Engineering, Electrical and Electronic Engineering and Global and Planetary Change, having authored 35 papers that have together received 1.2k indexed citations. Recurring topics across this work include Fuel Cells and Related Materials (7 papers), Remote-Sensing Image Classification (7 papers), Land Use and Ecosystem Services (5 papers), Remote Sensing and Land Use (4 papers), Electric and Hybrid Vehicle Technologies (4 papers), Date Palm Research Studies (4 papers), Impact of Light on Environment and Health (4 papers) and Advanced Battery Technologies Research (3 papers). The work is most often cited by research in Media Technology (434 citations), Global and Planetary Change (319 citations), Environmental Engineering (202 citations), Atmospheric Science (234 citations) and Computer Vision and Pattern Recognition (214 citations). Thomas Esch has collaborated with scholars based in Germany, Australia and Brazil. Frequent co-authors include Uwe Stilla, Dimitrios Marmanis, Mihai Datcu, Stefan Dech, Hannes Taubenböck, Mattia Marconcini, Andreas Felbier, Michael Wiesner, Hermann Johann Heinrich Kux and Tessio Novack. Their work appears in journals such as International Journal of Hydrogen Energy, SAE technical papers on CD-ROM/SAE technical paper series, Applied Geography, MTZ - Motortechnische Zeitschrift and IEEE Geoscience and Remote Sensing Letters.

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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