Árpád Csámer

25 papers receiving 519 citations

Árpád Csámer's Hit Papers

Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy 2025 · 21 citations
210Years since publication5101520

Peers

Árpád Csámer
Comparison fields: 5 of 58
  • Media Technology 220
  • Artificial Intelligence 406
  • Environmental Engineering 161
  • Geophysics 137
  • Radiological and Ultrasound Technology 16
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Sabreen Gad United States
Enton Bedini Denmark
Safaa M. Hassan Egypt
Milad Sekandari Iran
Jonas Didero Takodjou Wambo Cameroon
Alan J Mauger Australia
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Mohammed Raji Morocco
Catarina Labouré Bemfica Toledo Brazil
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Citations per year

Countries citing papers authored by Árpád Csámer

Since Specialization
Citations

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

Fields of papers citing papers by Árpád Csámer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 16 scholars most cited alongside Árpád Csámer, 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 Árpád Csámer Line = papers co-authored together Árpád Csámer links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202153
2 202350
3 202147
4 202246
5 202145
6 202239
7 202335
8 202132
9 202428
10 202223
11
Comparative assessment of machine learning models for landslide susceptibility mapping: a focus on validation and accuracy
Hit paper breakdown →
202521
12 202120
13 202218
14 202414
15 202312
16 20249
17 20238
18 20257
19 20246
20 20213

About Árpád Csámer

Árpád Csámer is a scholar working on Artificial Intelligence, Media Technology, Environmental Engineering, Global and Planetary Change and Mechanics of Materials, having authored 30 papers that have together received 527 indexed citations. Recurring topics across this work include Geochemistry and Geologic Mapping (20 papers), Remote-Sensing Image Classification (15 papers), Soil Geostatistics and Mapping (8 papers), Mineral Processing and Grinding (4 papers), Flood Risk Assessment and Management (4 papers), Groundwater and Watershed Analysis (4 papers), Hydrocarbon exploration and reservoir analysis (4 papers) and Landslides and related hazards (4 papers). The work is most often cited by research in Media Technology (220 citations), Artificial Intelligence (406 citations), Environmental Engineering (161 citations), Geophysics (137 citations) and Radiological and Ultrasound Technology (16 citations). Árpád Csámer has collaborated with scholars based in Hungary, Egypt and Sudan. Frequent co-authors include Ali Shebl, Timothy Kusky, Sayed O. Elkhateeb, Yasushi Watanabe, Mohamed Abdelkader, Mohamed A. Abd El‐Wahed, Hosni Ghazala, Sultan Awad Sultan Araffa, Mahmoud M. El-Rahmany and P. Rózsa. Their work appears in journals such as Remote Sensing Applications Society and Environment, The Egyptian Journal of Remote Sensing and Space Science, Scientific Reports, Ore Geology Reviews and IEEE Transactions on Geoscience and Remote Sensing.

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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