Lorenzo Nava

904 total citations · 1 hit paper
21 papers, 502 citations indexed

About

Lorenzo Nava is a scholar working on Management, Monitoring, Policy and Law, Global and Planetary Change and Atmospheric Science. According to data from OpenAlex, Lorenzo Nava has authored 21 papers receiving a total of 502 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Management, Monitoring, Policy and Law, 11 papers in Global and Planetary Change and 8 papers in Atmospheric Science. Recurrent topics in Lorenzo Nava's work include Landslides and related hazards (15 papers), Flood Risk Assessment and Management (11 papers) and Cryospheric studies and observations (6 papers). Lorenzo Nava is often cited by papers focused on Landslides and related hazards (15 papers), Flood Risk Assessment and Management (11 papers) and Cryospheric studies and observations (6 papers). Lorenzo Nava collaborates with scholars based in Italy, Spain and China. Lorenzo Nava's co-authors include Filippo Catani, Oriol Monserrat, Kushanav Bhuyan, Sansar Raj Meena, Mario Floris, Francesco Gringoli, C.J. van Westen, Ascanio Rosi, Bo Han and Neil Spring and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Geoscience and Remote Sensing and Nature Geoscience.

In The Last Decade

Lorenzo Nava

19 papers receiving 488 citations

Hit Papers

align trajectories sort by overperformance

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.

Landslide displacement forecasting using deep learning and monitoring data across selected sites

2023 89 citations Lorenzo Nava, Cristina Reyes‐Carmona et al. Landslides profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lorenzo Nava Italy	12	330	162	139	80	66	21	502
Jiale Wang China	6	205 0.6×	137 0.8×	66 0.5×	22 0.3×	29 0.4×	14	336
Zeeshan Afzal China	9	121 0.4×	65 0.4×	67 0.5×	33 0.4×	28 0.4×	34	267
Yaozu Qin China	12	81 0.2×	113 0.7×	48 0.3×	24 0.3×	78 1.2×	27	365
Fengyan Wang China	14	102 0.3×	78 0.5×	118 0.8×	7 0.1×	28 0.4×	44	486
Tung‐Lin Tsai Taiwan	15	399 1.2×	63 0.4×	85 0.6×	29 0.4×	72 1.1×	33	680
Luis F. Robledo Chile	8	130 0.4×	105 0.6×	45 0.3×	7 0.1×	29 0.4×	16	346
V. Segor Italy	7	152 0.5×	35 0.2×	84 0.6×	15 0.2×	17 0.3×	17	334
Raquel Melo Portugal	11	419 1.3×	296 1.8×	141 1.0×	17 0.2×	13 0.2×	25	522
Wahyu Luqmanul Hakim South Korea	10	216 0.7×	139 0.9×	95 0.7×	5 0.1×	37 0.6×	19	416

Countries citing papers authored by Lorenzo Nava

Since Specialization

Citations

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

Fields of papers citing papers by Lorenzo Nava

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lorenzo Nava

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

Dramsch, Jesper, Miguel‐Ángel Fernández‐Torres, Andrea Toreti, et al.. (2025). Explainability can foster trust in artificial intelligence in geoscience. Nature Geoscience. 18(2). 112–114. 14 indexed citations

Tang, Xiaochuan, Xiaojun Yuan, Dongfen Li, et al.. (2025). Mamba for Landslide Detection: A Lightweight Model for Mapping Landslides With Very High-Resolution Images. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–17.

Nava, Lorenzo, Alessandro Novellino, Kushanav Bhuyan, et al.. (2025). Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan. Natural hazards and earth system sciences. 25(7). 2371–2377.

Bhuyan, Kushanav, Uğur Öztürk, Lorenzo Nava, et al.. (2024). Towards automatic delineation of landslide source and runout. Engineering Geology. 345. 107866–107866. 6 indexed citations

Nava, Lorenzo, et al.. (2024). Displacement residuals reveal landslide regime shifts. Landslides. 22(1). 1–16. 4 indexed citations

Wang, Xin, et al.. (2024). A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images. Earth system science data. 16(10). 4817–4842. 14 indexed citations

Liu, Yuting, Giordano Teza, Lorenzo Nava, et al.. (2024). Deformation evaluation and displacement forecasting of baishuihe landslide after stabilization based on continuous wavelet transform and deep learning. Natural Hazards. 120(11). 9649–9673. 5 indexed citations

Meena, Sansar Raj, Lorenzo Nava, Kushanav Bhuyan, et al.. (2023). HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery. Earth system science data. 15(7). 3283–3298. 27 indexed citations

Nava, Lorenzo, Cristina Reyes‐Carmona, Kushanav Bhuyan, et al.. (2023). Landslide displacement forecasting using deep learning and monitoring data across selected sites. Landslides. 20(10). 2111–2129. 89 indexed citations breakdown →

10.

Bhuyan, Kushanav, Sansar Raj Meena, Lorenzo Nava, et al.. (2023). Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the u-net deep learning model. GIScience & Remote Sensing. 60(1). 19 indexed citations

11.

Mirmazloumi, S. Mohammad, et al.. (2023). InSAR time series and LSTM model to support early warning detection tools of ground instabilities: mining site case studies. Bulletin of Engineering Geology and the Environment. 82(10). 20 indexed citations

12.

Bhuyan, Kushanav, Hakan Tanyaş, Lorenzo Nava, et al.. (2023). Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data. Scientific Reports. 13(1). 162–162. 49 indexed citations

13.

Nava, Lorenzo, Giovanni Gigli, William Frodella, et al.. (2023). Classification of rock slope cavernous weathering on UAV photogrammetric point clouds: The example of Hegra (UNESCO World Heritage Site, Kingdom of Saudi Arabia). Engineering Geology. 325. 107286–107286. 15 indexed citations

14.

Sharma, Sanjib, Lorenzo Nava, Rocky Talchabhadel, et al.. (2022). Natural Hazards Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science. Earth and Space Science. 9(1). 4 indexed citations

15.

Nava, Lorenzo, Kushanav Bhuyan, Sansar Raj Meena, Oriol Monserrat, & Filippo Catani. (2022). Rapid Mapping of Landslides on SAR Data by Attention U-Net. Remote Sensing. 14(6). 1449–1449. 61 indexed citations

16.

Frauenfelder, Regula, et al.. (2022). Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape. Remote Sensing. 14(10). 2301–2301. 34 indexed citations

17.

Nava, Lorenzo, Oriol Monserrat, & Filippo Catani. (2021). Improving landslide detection on SAR data through Deep Learning. Zenodo (CERN European Organization for Nuclear Research). 3 indexed citations

18.

Nava, Lorenzo, Oriol Monserrat, & Filippo Catani. (2021). Improving Landslide Detection on SAR Data Through Deep Learning. IEEE Geoscience and Remote Sensing Letters. 19. 1–5. 53 indexed citations

19.

Gringoli, Francesco, Lorenzo Nava, Alice Este, & Luca Salgarelli. (2012). MTCLASS: Enabling statistical traffic classification of multi-gigabit aggregates on inexpensive hardware. Institutional Research Information System (Università degli Studi di Brescia). 450–455. 9 indexed citations

20.

Han, Bo, Aaron Schulman, Francesco Gringoli, et al.. (2010). Maranello: practical partial packet recovery for 802.11. Networked Systems Design and Implementation. 14–14. 72 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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