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.
Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification
2016828 citationsEmmanuel Maggiori, Yuliya Tarabalka et al.IEEE Transactions on Geoscience and Remote Sensingprofile →
New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning
2018273 citationsPedram Ghamisi, Emmanuel Maggiori et al.IEEE Geoscience and Remote Sensing Magazineprofile →
Citations per year, relative to Emmanuel Maggiori Emmanuel Maggiori (= 1×)
peers
Erzhu Li
Countries citing papers authored by Emmanuel Maggiori
Since
Specialization
Citations
This map shows the geographic impact of Emmanuel Maggiori'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 Emmanuel Maggiori with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emmanuel Maggiori more than expected).
Fields of papers citing papers by Emmanuel Maggiori
This network shows the impact of papers produced by Emmanuel Maggiori. 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 Emmanuel Maggiori. The network helps show where Emmanuel Maggiori may publish in the future.
Co-authorship network of co-authors of Emmanuel Maggiori
This figure shows the co-authorship network connecting the top 25 collaborators of Emmanuel Maggiori.
A scholar is included among the top collaborators of Emmanuel Maggiori 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 Emmanuel Maggiori. Emmanuel Maggiori is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
8 of 8 papers shown
1.
Ghamisi, Pedram, Emmanuel Maggiori, Shutao Li, et al.. (2018). New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning. IEEE Geoscience and Remote Sensing Magazine. 6(3). 10–43.273 indexed citations breakdown →
Maggiori, Emmanuel, Yuliya Tarabalka, Guillaume Charpiat, & Pierre Alliez. (2016). Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 55(2). 645–657.828 indexed citations breakdown →
5.
Maggiori, Emmanuel, et al.. (2014). Towards Recovering Architectural Information from Images of Architectural Diagrams. El Servicio de Difusión de la Creación Intelectual (National University of La Plata).3 indexed citations
Maggiori, Emmanuel. (2013). Desarrollo de una gramática para aserciones simples en español y su implementación en Prolog. El Servicio de Difusión de la Creación Intelectual (National University of La Plata).2 indexed citations
8.
Maggiori, Emmanuel, et al.. (2012). FOLST: Una Herramienta Didáctica para la Lógica de Predicados de Primer Orden. El Servicio de Difusión de la Creación Intelectual (National University of La Plata).1 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.