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.
An interpretable attention-based deep learning method for landslide prediction based on multi-temporal InSAR time series: A case study of Xinpu landslide in the TGRA
202518 citationsChao Zhou, Chunbo Luo et al.Remote Sensing of Environmentprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
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Countries citing papers authored by Jan‐Peter Müller
Since
Specialization
Citations
This map shows the geographic impact of Jan‐Peter Müller'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 Jan‐Peter Müller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan‐Peter Müller more than expected).
Fields of papers citing papers by Jan‐Peter Müller
This network shows the impact of papers produced by Jan‐Peter Müller. 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 Jan‐Peter Müller. The network helps show where Jan‐Peter Müller may publish in the future.
Co-authorship network of co-authors of Jan‐Peter Müller
This figure shows the co-authorship network connecting the top 25 collaborators of Jan‐Peter Müller.
A scholar is included among the top collaborators of Jan‐Peter Müller 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 Jan‐Peter Müller. Jan‐Peter Müller 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.
Zhou, Chao, et al.. (2025). An interpretable attention-based deep learning method for landslide prediction based on multi-temporal InSAR time series: A case study of Xinpu landslide in the TGRA. Remote Sensing of Environment. 318. 114580–114580.18 indexed citations breakdown →
Müller, Jan‐Peter, et al.. (2019). 3D imaging tools and geospatial services from joint European-USA collaborations.. EPSC. 2019.3 indexed citations
4.
Sidiropoulos, Panagiotis, et al.. (2018). Deep Learning-Based Anomaly Detection to Find Changes over the Martian South Pole. European Planetary Science Congress.1 indexed citations
5.
Müller, Jan‐Peter, et al.. (2017). Automated dynamic feature tracking of RSLs on the Martian surface through HiRISE super-resolution restoration and 3D reconstruction techniques. EPSC.1 indexed citations
6.
Müller, Jan‐Peter, et al.. (2017). Production of Arctic Sea-ice Albedo by fusion of MISR and MODIS data. EGUGA. 18918.1 indexed citations
Paar, Gerhard, Jan‐Peter Müller, Yu Tao, et al.. (2015). PRoViDE: Planetary Robotics Vision Data Processing and Fusion. elib (German Aerospace Center).2 indexed citations
9.
Traxler, Christoph, et al.. (2015). A virtual environment for the accurate geologic analysis of Martian terrain. EGU General Assembly Conference Abstracts. 10346.2 indexed citations
10.
Sidiropoulos, Panagiotis & Jan‐Peter Müller. (2015). Identifying dynamic features on Mars through multi-instrument co-registration of orbital images. European Planetary Science Congress.
11.
Müller, Jan‐Peter, et al.. (2014). Automated navigation of Mars rovers using HiRISE-CTX-HRSC co-registered orthorectified images and DTMs. EGU General Assembly Conference Abstracts. 3958.
12.
Gupta, Sanjeev, David M. Rubin, M. S. Rice, et al.. (2014). Making sense of martian sediments at the Kimberley, Gale crater. 2014 AGU Fall Meeting. 2014.3 indexed citations
13.
Kıncal, Cem, Andrew Singleton, Peng Liu, et al.. (2010). Mass Movement Susceptibility Mapping Using Satellite Optical Imagery Compared With INSAR Monitoring: Zigui County, Three Gorges Region, China. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 684. 68.2 indexed citations
14.
Kim, Jung‐Rack, et al.. (2007). Geometric Ground Control of Very High Resolution Imagery Using HRSC Intersection Points and a Non-Rigorous Camera Model. LPI. 1811.6 indexed citations
15.
Balme, M. R., et al.. (2007). Morphological evidence for a sea-ice origin for Elysium Planitia platy terrain. Open Research Online (The Open University). 2202.1 indexed citations
16.
Li, Zhenhong, Paul Cross, & Jan‐Peter Müller. (2005). Successful Application of GPS-derived Water Vapor to the Improvement of the Estimation of Surface Deformation from InSAR. UCL Discovery (University College London). 2468–2476.1 indexed citations
17.
Müller, Jan‐Peter, Kim, & Jeremy Morley. (1999). Quality assessment of global cartographically-derived DEMs using spaceborne altimetry. UCL Discovery (University College London).1 indexed citations
18.
Müller, Jan‐Peter, et al.. (1987). Transputer arrays for real-time feature point stereo matching. UCL Discovery (University College London).1 indexed citations
19.
Müller, Jan‐Peter. (1972). Pollen morphological evidence for subdivision and affinities of Lecythidaceae. Blumea - Biodiversity Evolution and Biogeography of Plants. 20(2). 351–355.18 indexed citations
20.
Müller, Jan‐Peter. (1970). Pollen morphology of the genus Lepisanthes (Sapindaceae) in relation to its taxonomy. Blumea - Biodiversity Evolution and Biogeography of Plants. 18(2). 507–561.6 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.