Gabriele Moser

6.2k total citations · 2 hit papers
182 papers, 4.7k citations indexed

About

Gabriele Moser is a scholar working on Media Technology, Artificial Intelligence and Atmospheric Science. According to data from OpenAlex, Gabriele Moser has authored 182 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 115 papers in Media Technology, 44 papers in Artificial Intelligence and 44 papers in Atmospheric Science. Recurrent topics in Gabriele Moser's work include Remote-Sensing Image Classification (111 papers), Remote Sensing and Land Use (35 papers) and Remote Sensing in Agriculture (31 papers). Gabriele Moser is often cited by papers focused on Remote-Sensing Image Classification (111 papers), Remote Sensing and Land Use (35 papers) and Remote Sensing in Agriculture (31 papers). Gabriele Moser collaborates with scholars based in Italy, France and Switzerland. Gabriele Moser's co-authors include Sebastiano B. Serpico, Josiane Zerubia, Devis Tuia, Jón Atli Benediktsson, Gustau Camps‐Valls, Luis Gómez‐Chova, Vladimir A. Krylov, Grégoire Mercier, Andrea Giorgi and Stian Normann Anfinsen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Proceedings of the IEEE and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Gabriele Moser

172 papers receiving 4.6k citations

Hit Papers

align trajectories

What are hit papers?

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.

Multimodal Classification of Remote Sensing Images: A Review and Future Directions

2015 343 citations Devis Tuia, Gabriele Moser et al. profile →
New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

2018 273 citations Pedram Ghamisi, Emmanuel Maggiori et al. IEEE Geoscience and Remote Sensing Magazine profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Gabriele Moser Italy	35	2.9k	1.5k	1.2k	884	784	182	4.7k
Sebastiano B. Serpico Italy	38	3.6k 1.2×	1.9k 1.3×	1.3k 1.1×	1.2k 1.4×	880 1.1×	218	5.4k
Chen Wu China	35	2.7k 0.9×	1.7k 1.2×	1.7k 1.5×	877 1.0×	434 0.6×	164	5.2k
Lichao Mou Germany	36	3.8k 1.3×	2.1k 1.4×	2.2k 1.9×	1.1k 1.3×	634 0.8×	126	6.7k
Allan Aasbjerg Nielsen Denmark	24	2.1k 0.7×	1.3k 0.9×	527 0.4×	1.2k 1.4×	875 1.1×	124	3.9k
Melba M. Crawford United States	36	3.5k 1.2×	1.9k 1.3×	1.8k 1.5×	1.0k 1.1×	379 0.5×	183	5.9k
Fabio Pacifici United States	23	2.4k 0.8×	1.1k 0.7×	841 0.7×	880 1.0×	288 0.4×	68	3.3k
Yanfeng Gu China	31	4.3k 1.5×	2.5k 1.7×	2.2k 1.9×	755 0.9×	693 0.9×	204	6.2k
Pingxiang Li China	31	2.3k 0.8×	1.5k 1.1×	1.3k 1.1×	765 0.9×	682 0.9×	168	5.1k
Jonathan Cheung-Wai Chan Belgium	41	3.2k 1.1×	1.1k 0.8×	2.2k 1.9×	982 1.1×	301 0.4×	133	5.7k
Mattia Marconcini Germany	27	2.5k 0.9×	1.9k 1.3×	864 0.7×	946 1.1×	285 0.4×	106	5.2k

Countries citing papers authored by Gabriele Moser

Since Specialization

Citations

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

Fields of papers citing papers by Gabriele Moser

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gabriele Moser

This figure shows the co-authorship network connecting the top 25 collaborators of Gabriele Moser. A scholar is included among the top collaborators of Gabriele Moser 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 Gabriele Moser. Gabriele Moser is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Moser, Gabriele, et al.. (2025). Near Real-Time Photovoltaic Power Forecasting Through Recurrent Neural Network Using Timely Open-Access Data. Energies. 18(23). 6220–6220.

Gallo, Federico, et al.. (2025). Multisource methodology for traffic analysis zone definition based on the fusion of Remote Sensing, OpenStreetMap, and Floating Car Data. Journal of Transport Geography. 128. 104324–104324.

Moser, Gabriele, et al.. (2024). A Gaussian Process Regression Method to Nowcast Cloud-to-Ground Lightning From Remote Sensing and Numerical Weather Modeling Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 1963–1981. 3 indexed citations

Pastorino, Martina, et al.. (2024). A Multiresolution Fusion Framework based on Probabilistic Graphical Modeling for Burnt Zones Mapping from Satellite and UAV Imagery. SPIRE - Sciences Po Institutional REpository. 7919–7923.

Moser, Gabriele, et al.. (2024). Manifold Learning and Deep Generative Networks for Heterogeneous Change Detection From Hyperspectral and Synthetic Aperture Radar Images. IEEE Geoscience and Remote Sensing Letters. 22. 1–5. 1 indexed citations

Luppino, Luigi Tommaso, Michael Kampffmeyer, Filippo Maria Bianchi, et al.. (2022). Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images. IEEE Transactions on Neural Networks and Learning Systems. 35(1). 60–72. 100 indexed citations

Pastorino, Martina, Gabriele Moser, Sebastiano B. Serpico, & Josiane Zerubia. (2022). FULLY CONVOLUTIONAL AND FEEDFORWARD NETWORKS FOR THE SEMANTIC SEGMENTATION OF REMOTELY SENSED IMAGES. CINECA IRIS Institutial Research Information System (University of Genoa). 4 indexed citations

Luppino, Luigi Tommaso, Michael Kampffmeyer, Filippo Maria Bianchi, et al.. (2021). Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–22. 130 indexed citations

Pastorino, Martina, et al.. (2021). Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles. Remote Sensing. 13(5). 849–849. 24 indexed citations

10.

Bruzzone, Lorenzo, Francesca Bovolo, Pierre Defourny, et al.. (2019). CCI Essential Climate Variables: High Resolution Land Cover. Institutional Research Information System (Università degli Studi di Trento). 1 indexed citations

11.

Cenci, Luca, Andrea Giorgi, Giuseppe Squicciarino, et al.. (2019). Exploiting Sentinel 2 data for mapping wind storm damages in forested areas. Case Study: the event of October 2018 occurred in Northeast Italy. EGU General Assembly Conference Abstracts. 12691. 2 indexed citations

12.

Ibrahim, Alì, et al.. (2019). DCNN for Tactile Sensory Data Classification based on Transfer Learning. CINECA IRIS Institutial Research Information System (University of Genoa). 237–240. 17 indexed citations

13.

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 →

14.

Yokoya, Naoto, Pedram Ghamisi, Junshi Xia, et al.. (2018). Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 11(5). 1363–1377. 109 indexed citations

15.

Dell’Acqua, Fabio, et al.. (2017). The IEEE GRSS data and algorithm standard evaluation (DASE) website: Incrementally building a standardized assessment for algorithm performance. CINECA IRIS Institutial Research Information System (University of Genoa). 2 indexed citations

16.

Tuia, Devis, Gabriele Moser, Bertrand Le Saux, Benjamin Bechtel, & Linda See. (2017). 2017 IEEE GRSS Data Fusion Contest: Open Data for Global Multimodal Land Use Classification [Technical Committees]. IEEE Geoscience and Remote Sensing Magazine. 5(1). 70–73. 22 indexed citations

17.

Tuia, Devis & Gabriele Moser. (2015). Foreword to the special issue on data fusion in remote sensing - Published in: IEEE Geoscience and Remote Sensing Magazine. Zurich Open Repository and Archive (University of Zurich). 1 indexed citations

18.

Krylov, Vladimir A., Gabriele Moser, Sebastiano B. Serpico, et al.. (2011). Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields. AIP conference proceedings. 319–326. 1 indexed citations

19.

Moser, Gabriele & Sebastiano B. Serpico. (2009). Automatic Parameter Optimization for Support Vector Regression for Land and Sea Surface Temperature Estimation From Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing. 47(3). 909–921. 51 indexed citations

20.

Moser, Gabriele, Josiane Zerubia, & Sebastiano B. Serpico. (2006). SAR amplitude probability density function estimation based on a generalized Gaussian model. IEEE Transactions on Image Processing. 15(6). 1429–1442. 114 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.

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