Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep learning-based road damage detection and classification for multiple countries
2021181 citationsDeeksha Arya, Hiroya Maeda et al.Automation in Constructionprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Alexander Mráz
Since
Specialization
Citations
This map shows the geographic impact of Alexander Mráz'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 Alexander Mráz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Mráz more than expected).
This network shows the impact of papers produced by Alexander Mráz. 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 Alexander Mráz. The network helps show where Alexander Mráz may publish in the future.
Co-authorship network of co-authors of Alexander Mráz
This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Mráz.
A scholar is included among the top collaborators of Alexander Mráz 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 Alexander Mráz. Alexander Mráz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
17 of 17 papers shown
1.
Arya, Deeksha, Hiroya Maeda, Sanjay Kumar Ghosh, et al.. (2021). Deep learning-based road damage detection and classification for multiple countries. Automation in Construction. 132. 103935–103935.181 indexed citations breakdown →
Mráz, Alexander, et al.. (2009). A semi-automated faulting measurement approach for rigid pavements using high speed inertial profiler data..1 indexed citations
Mráz, Alexander. (2008). Evaluation of Digital Imaging Systems Used in Highway Applications. Digital Commons - University of South Florida (University of South Florida).
12.
Mráz, Alexander, et al.. (2007). Innovative Method for Enhancing Pavement Crack Images. Transportation Research Board 86th Annual MeetingTransportation Research Board.4 indexed citations
Gunaratne, Manjriker, et al.. (2006). Evaluation and Validation of High-Speed Multi-Function System for Automated Pavement Condition Survey.3 indexed citations
Gunaratne, Manjriker, et al.. (2003). STUDY OF THE FEASIBILITY OF VIDEO LOGGING WITH PAVEMENT CONDITION EVALUATION.9 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.