Dino Ienco

3.4k total citations · 1 hit paper
103 papers, 2.0k citations indexed

About

Dino Ienco is a scholar working on Ecology, Artificial Intelligence and Media Technology. According to data from OpenAlex, Dino Ienco has authored 103 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Ecology, 32 papers in Artificial Intelligence and 24 papers in Media Technology. Recurrent topics in Dino Ienco's work include Remote Sensing in Agriculture (46 papers), Remote-Sensing Image Classification (23 papers) and Remote Sensing and LiDAR Applications (15 papers). Dino Ienco is often cited by papers focused on Remote Sensing in Agriculture (46 papers), Remote-Sensing Image Classification (23 papers) and Remote Sensing and LiDAR Applications (15 papers). Dino Ienco collaborates with scholars based in France, Italy and Germany. Dino Ienco's co-authors include Raffaele Gaetano, Roberto Interdonato, Ruggero G. Pensa, Dinh Ho Tong Minh, Kenji Osé, Rosa Meo, Pierre Maurel, Gloria Bordogna, Pascal Poncelet and Maguelonne Teisseire and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and Scientific Reports.

In The Last Decade

Dino Ienco

95 papers receiving 1.9k 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.

Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture

2019 240 citations Dino Ienco, Roberto Interdonato et al. ISPRS Journal of Photogrammetry and Remote Sensing profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dino Ienco France	22	822	556	488	361	357	103	2.0k
P. Blonda Italy	23	633 0.8×	372 0.7×	497 1.0×	222 0.6×	204 0.6×	84	1.7k
Xin Hu China	30	428 0.5×	793 1.4×	744 1.5×	199 0.6×	418 1.2×	69	2.8k
Andrea Baraldi Italy	25	717 0.9×	1.0k 1.8×	876 1.8×	319 0.9×	417 1.2×	93	3.0k
Jonathon Hare United Kingdom	19	355 0.4×	645 1.2×	559 1.1×	244 0.7×	301 0.8×	109	2.3k
Ribana Roscher Germany	20	317 0.4×	189 0.3×	558 1.1×	271 0.8×	147 0.4×	70	2.0k
Pierre Gançarski France	17	349 0.4×	324 0.6×	569 1.2×	142 0.4×	218 0.6×	66	1.9k
Imed Riadh Farah Tunisia	22	390 0.5×	548 1.0×	300 0.6×	350 1.0×	393 1.1×	137	2.1k
Peng Liu China	29	361 0.4×	739 1.3×	477 1.0×	333 0.9×	406 1.1×	183	2.8k
Alina Zare United States	26	516 0.6×	1.2k 2.2×	393 0.8×	386 1.1×	616 1.7×	154	2.5k
Wei Feng China	24	213 0.3×	318 0.6×	565 1.2×	138 0.4×	295 0.8×	103	2.1k

Countries citing papers authored by Dino Ienco

Since Specialization

Citations

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

Fields of papers citing papers by Dino Ienco

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dino Ienco

This figure shows the co-authorship network connecting the top 25 collaborators of Dino Ienco. A scholar is included among the top collaborators of Dino Ienco 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 Dino Ienco. Dino Ienco 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

Maleki, Saeideh, et al.. (2025). Sentinel-1 (S1) time series alignment method for rapeseed fields mapping. Frontiers in Remote Sensing. 5. 2 indexed citations

Ienco, Dino, et al.. (2025). SAHARA: Heterogeneous Semi-Supervised Transfer Learning With Adversarial Adaptation and Dynamic Pseudo-Labeling. IEEE Geoscience and Remote Sensing Letters. 23. 1–5.

Mena, Francisco, et al.. (2025). Multi-modal co-learning for Earth observation: enhancing single-modality models via modality collaboration. Machine Learning. 114(12).

Baghdadi, Nicolas, et al.. (2025). Rapeseed mapping using machine learning methods and Sentinel-1 time series coupled with growing degree-days information. Science of Remote Sensing. 11. 100244–100244.

Gaetano, Raffaele, et al.. (2025). Geographical context matters: Bridging fine and coarse spatial information to enhance continental land cover mapping. Science of Remote Sensing. 12. 100315–100315.

Rizzoli, Paola, et al.. (2024). Generation of country-scale canopy height maps over Gabon using deep learning and TanDEM-X InSAR data. Remote Sensing of Environment. 311. 114270–114270. 3 indexed citations

Maleki, Saeideh, et al.. (2024). Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images. Remote Sensing. 16(23). 4548–4548. 2 indexed citations

Benoît, Alexandre, et al.. (2024). Explaining the decisions and the functioning of a convolutional spatiotemporal land cover classifier with channel attention and redescription mining. ISPRS Journal of Photogrammetry and Remote Sensing. 215. 256–270. 1 indexed citations

Ienco, Dino, Raffaele Gaetano, & Roberto Interdonato. (2023). A constrastive semi-supervised deep learning framework for land cover classification of satellite time series with limited labels. Neurocomputing. 567. 127031–127031. 7 indexed citations

10.

Rizzoli, Paola, et al.. (2023). A Deep Learning Framework for the Estimation of Forest Height From Bistatic TanDEM-X Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 8334–8352. 8 indexed citations

11.

Durrieu, Sylvie, et al.. (2023). Enhancing Forest Attribute Prediction by Considering Terrain and Scan Angles From Lidar Point Clouds: A Neural Network Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 3531–3544. 1 indexed citations

12.

Maleki, Saeideh, et al.. (2023). Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 8884–8899. 9 indexed citations

13.

Esposito, Roberto, et al.. (2022). Dealing With Multipositive Unlabeled Learning Combining Metric Learning and Deep Clustering. IEEE Access. 10. 51839–51849. 2 indexed citations

14.

Andresini, Giuseppina, Annalisa Appice, Dino Ienco, & Donato Malerba. (2022). SENECA: Change detection in optical imagery using Siamese networks with Active-Transfer Learning. Expert Systems with Applications. 214. 119123–119123. 9 indexed citations

15.

Durrieu, Sylvie, et al.. (2022). Combining LiDAR Metrics and Sentinel-2 Imagery to Estimate Basal Area and Wood Volume in Complex Forest Environment via Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15. 4337–4348. 15 indexed citations

16.

Sallaberry, Arnaud, et al.. (2021). VERTIGo: A Visual Platform for Querying and Exploring Large Multilayer Networks. IEEE Transactions on Visualization and Computer Graphics. 28(3). 1634–1647. 8 indexed citations

17.

Ienco, Dino, et al.. (2021). Attentive Spatial Temporal Graph CNN for Land Cover Mapping From Multi Temporal Remote Sensing Data. IEEE Access. 9. 23070–23082. 21 indexed citations

18.

Ienco, Dino, Raffaele Gaetano, Roberto Interdonato, Kenji Osé, & Dinh Ho Tong Minh. (2018). Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based\n land cover classification. arXiv (Cornell University). 18 indexed citations

19.

Ienco, Dino, et al.. (2017). Object-oriented satellite image time series analysis using a graph-based representation. Ecological Informatics. 43. 52–64. 23 indexed citations

20.

Interdonato, Roberto, Andrea Tagarelli, Dino Ienco, Arnaud Sallaberry, & Pascal Poncelet. (2016). Détection de communautés locales dans des réseaux multicouches. HAL (Le Centre pour la Communication Scientifique Directe). 49 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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