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.
Review on Convolutional Neural Networks (CNN) in vegetation remote sensing
20211.1k citationsJens Leitloff, Stefan Hinz et al.profile →
Citations per year, relative to Jens Leitloff Jens Leitloff (= 1×)
peers
Marco Körner
Countries citing papers authored by Jens Leitloff
Since
Specialization
Citations
This map shows the geographic impact of Jens Leitloff'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 Jens Leitloff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jens Leitloff more than expected).
This network shows the impact of papers produced by Jens Leitloff. 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 Jens Leitloff. The network helps show where Jens Leitloff may publish in the future.
Co-authorship network of co-authors of Jens Leitloff
This figure shows the co-authorship network connecting the top 25 collaborators of Jens Leitloff.
A scholar is included among the top collaborators of Jens Leitloff 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 Jens Leitloff. Jens Leitloff is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Reinartz, Peter, et al.. (2011). Real Time Airborne Monitoring for Disaster and Traffic Applications. elib (German Aerospace Center).2 indexed citations
10.
Kurz, Franz, et al.. (2011). Real time camera system for disaster and traffic monitoring. elib (German Aerospace Center).19 indexed citations
11.
Reinartz, Peter, Franz Kurz, Dominik Rosenbaum, Jens Leitloff, & Gintautas Palubinskas. (2010). IMAGE TIME SERIES FOR NEAR REAL TIME AIRBORNE MONITORING OF DISASTER SITUATIONS AND TRAFFIC APPLICATIONS. The international archives of the photogrammetry, remote sensing and spatial information sciences. 38.2 indexed citations
12.
Leitloff, Jens, et al.. (2010). AUTOMATIC VEHICLE DETECTION IN AERIAL IMAGE SEQUENCES OF URBAN AREAS USING 3D HOG FEATURES. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 38. 50–54.17 indexed citations
13.
Rosenbaum, Dominik, et al.. (2010). REAL-TIME IMAGE PROCESSING FOR ROAD TRAFFIC DATA EXTRACTION FROM AERIAL IMAGES. elib (German Aerospace Center).19 indexed citations
14.
Leitloff, Jens, Stefan Hinz, & Uwe Stilla. (2008). Inferring traffic activity from optical satellite images. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 332–337.7 indexed citations
Leitloff, Jens, Stefan Hinz, & Uwe Stilla. (2006). Detection of vehicle queues in Quickbird imagery of city areas. Photogrammetrie - Fernerkundung - Geoinformation. 2006(4). 315.9 indexed citations
17.
Leitloff, Jens, Stefan Hinz, & Uwe Stilla. (2006). Automatic vehicle detection in satellite images. The international archives of the photogrammetry, remote sensing and spatial information sciences. 36(3). 221.14 indexed citations
18.
Leitloff, Jens, Stefan Hinz, & Uwe Stilla. (2005). Vehicle queue detection in complex urban areas by extraction and analysis of linear features. The international archives of the photogrammetry, remote sensing and spatial information sciences. 36.2 indexed citations
19.
Leitloff, Jens, Stefan Hinz, & Uwe Stilla. (2005). AUTOMATIC VEHICLE DETECTION IN SPACE IMAGES SUPPORTED BY DIGITAL MAP DATA.4 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.