Bertrand Le Saux

6.0k total citations · 5 hit papers
97 papers, 3.4k citations indexed

About

Bertrand Le Saux is a scholar working on Artificial Intelligence, Media Technology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Bertrand Le Saux has authored 97 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Artificial Intelligence, 41 papers in Media Technology and 31 papers in Computer Vision and Pattern Recognition. Recurrent topics in Bertrand Le Saux's work include Remote-Sensing Image Classification (37 papers), Advanced Image and Video Retrieval Techniques (17 papers) and Remote Sensing in Agriculture (13 papers). Bertrand Le Saux is often cited by papers focused on Remote-Sensing Image Classification (37 papers), Advanced Image and Video Retrieval Techniques (17 papers) and Remote Sensing in Agriculture (13 papers). Bertrand Le Saux collaborates with scholars based in Italy, France and Switzerland. Bertrand Le Saux's co-authors include Nicolas Audebert, Alexandre Boulch, Sébastien Lefèvre, Rodrigo Caye Daudt, Yann Gousseau, Naoto Yokoya, Ronny Hänsch, Joris Guerry, Saurabh Prasad and Devis Tuia and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Bertrand Le Saux

87 papers receiving 3.3k citations

Hit Papers

Deep Learning for Classification of Hyperspectral Data: A... 2017 2026 2020 2023 2019 2018 2017 2019 2019 100 200 300 400
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.

  1. Deep Learning for Classification of Hyperspectral Data: A Comparative Review
    2019 490 citations Nicolas Audebert, Bertrand Le Saux et al. IEEE Geoscience and Remote Sensing Magazine profile →
  2. Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
    2018 318 citations Rodrigo Caye Daudt, Bertrand Le Saux et al. arXiv (Cornell University) profile →
  3. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks
    2017 269 citations Alexandre Boulch, Bertrand Le Saux et al. profile →
  4. Multitask learning for large-scale semantic change detection
    2019 268 citations Rodrigo Caye Daudt, Bertrand Le Saux et al. profile →
  5. Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest
    2019 255 citations Naoto Yokoya, Bertrand Le Saux et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bertrand Le Saux Italy 25 1.6k 1.0k 822 775 620 97 3.4k
Claudio Persello Netherlands 32 1.7k 1.1× 823 0.8× 804 1.0× 863 1.1× 737 1.2× 116 3.7k
Wen Yang China 36 2.1k 1.3× 2.3k 2.3× 665 0.8× 584 0.8× 491 0.8× 260 4.9k
Haiyan Guan China 34 1.4k 0.9× 1.1k 1.1× 806 1.0× 2.1k 2.7× 280 0.5× 125 4.5k
Shunping Ji China 28 2.1k 1.3× 1.4k 1.4× 1.1k 1.3× 1.1k 1.4× 464 0.7× 90 4.3k
Melba M. Crawford United States 36 3.5k 2.1× 1.8k 1.8× 1.9k 2.3× 729 0.9× 1.1k 1.8× 183 5.9k
Alexandre Boulch France 20 726 0.4× 660 0.7× 361 0.4× 702 0.9× 219 0.4× 46 2.1k
Zilong Zhong Canada 11 1.8k 1.1× 736 0.7× 1.2k 1.4× 409 0.5× 245 0.4× 18 2.6k
Chao Tao China 32 1.7k 1.1× 1.3k 1.3× 562 0.7× 601 0.8× 582 0.9× 92 3.7k
Yanfeng Gu China 31 4.3k 2.6× 2.2k 2.2× 2.5k 3.0× 444 0.6× 762 1.2× 204 6.2k
Rongjun Qin United States 28 752 0.5× 738 0.7× 381 0.5× 1.1k 1.4× 157 0.3× 122 2.8k

Countries citing papers authored by Bertrand Le Saux

Since Specialization
Citations

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

Fields of papers citing papers by Bertrand Le Saux

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bertrand Le Saux. 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 Bertrand Le Saux. The network helps show where Bertrand Le Saux may publish in the future.

Co-authorship network of co-authors of Bertrand Le Saux

This figure shows the co-authorship network connecting the top 25 collaborators of Bertrand Le Saux. A scholar is included among the top collaborators of Bertrand Le Saux 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 Bertrand Le Saux. Bertrand Le Saux 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.
Naylor, Peter, et al.. (2025). Multitemporal Multispectral Dataset for Palaeochannels Segmentation (MAPS). IEEE Access. 13. 203113–203124.
2.
Serva, Federico, et al.. (2025). MUMUCD: A Multimodal Multiclass Change Detection Dataset. IEEE Geoscience and Remote Sensing Letters. 22. 1–5.
3.
Ceschini, Andrea, et al.. (2025). On hybrid quanvolutional neural networks optimization. Quantum Machine Intelligence. 7(1). 3 indexed citations
4.
Sebastianelli, Alessandro, et al.. (2025). Quanv4EO: Empowering Earth Observation by Means of Quanvolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–15. 4 indexed citations
5.
Gudmundsson, Lukas, Basil Kraft, Stijn Hantson, et al.. (2025). BuRNN (v1.0): A Data-Driven Fire Model. NERC Open Research Archive (Natural Environment Research Council).
6.
Saux, Bertrand Le, et al.. (2024). Identification of optimal Sentinel-1 SAR polarimetric parameters for forest monitoring in Czechia. SHILAP Revista de lepidopterología. 60(1). 46–60. 1 indexed citations
7.
Tuia, Devis, Konrad Schindler, Begüm Demir, et al.. (2024). Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward. IEEE Geoscience and Remote Sensing Magazine. 13(4). 119–141. 20 indexed citations
8.
Paletta, Quentin, Yuhao Nie, Yves‐Marie Saint‐Drenan, & Bertrand Le Saux. (2024). Improving cross-site generalisability of vision-based solar forecasting models with physics-informed transfer learning. Energy Conversion and Management. 309. 118398–118398. 7 indexed citations
9.
Sebastianelli, Alessandro, Federico Serva, Andrea Ceschini, et al.. (2024). Machine learning forecast of surface solar irradiance from meteo satellite data. Remote Sensing of Environment. 315. 114431–114431. 3 indexed citations
10.
Saux, Bertrand Le, et al.. (2023). Rapid training of quantum recurrent neural networks. Quantum Machine Intelligence. 5(2). 8 indexed citations
11.
Saux, Bertrand Le, et al.. (2023). Detection of Forest Fires through Deep Unsupervised Learning Modeling of Sentinel-1 Time Series. ISPRS International Journal of Geo-Information. 12(8). 332–332. 7 indexed citations
12.
Bonavita, Massimo, Rochelle Schneider, Rossella Arcucci, et al.. (2023). 2022 ECMWF-ESA workshop report: current status, progress and opportunities in machine learning for Earth System observation and prediction. npj Climate and Atmospheric Science. 6(1). 1 indexed citations
13.
Schneider, Rochelle, Massimo Bonavita, Alan Geer, et al.. (2022). ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction. npj Climate and Atmospheric Science. 5(1). 13 indexed citations
14.
Sebastianelli, Alessandro, et al.. (2021). Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing. IRIS Research product catalog (Sapienza University of Rome). 25 indexed citations
15.
Sebastianelli, Alessandro, et al.. (2021). On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15. 565–580. 86 indexed citations
16.
Saux, Bertrand Le, et al.. (2020). Semi-Supervised Semantic Segmentation in Earth Observation: The\n MiniFrance Suite, Dataset Analysis and Multi-task Network Study. arXiv (Cornell University). 60 indexed citations
17.
Daudt, Rodrigo Caye, Bertrand Le Saux, Alexandre Boulch, & Yann Gousseau. (2019). Guided Anisotropic Diffusion and Iterative Learning for Weakly\n Supervised Change Detection. arXiv (Cornell University). 20 indexed citations
18.
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
19.
Daudt, Rodrigo Caye, Bertrand Le Saux, Alexandre Boulch, & Yann Gousseau. (2018). High Resolution Semantic Change Detection.. arXiv (Cornell University). 10 indexed citations
20.
Daudt, Rodrigo Caye, Bertrand Le Saux, Alexandre Boulch, & Yann Gousseau. (2018). Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks. arXiv (Cornell University). 2115–2118. 318 indexed citations breakdown →

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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Claudio Persello Breakdown of academic impact, for papers by Wen Yang Breakdown of academic impact, for papers by Haiyan Guan Breakdown of academic impact, for papers by Shunping Ji Breakdown of academic impact, for papers by Melba M. Crawford Breakdown of academic impact, for papers by Alexandre Boulch Breakdown of academic impact, for papers by Zilong Zhong Breakdown of academic impact, for papers by Chao Tao Breakdown of academic impact, for papers by Yanfeng Gu Breakdown of academic impact, for papers by Rongjun Qin
Rankless by CCL
2026