Postdoctoral Research Associate in Multi-Hazard Remote Sensing
I am a Postdoctoral Research Associate in multi-hazard remote sensing, holding a joint appointment in the Departments of Geography and Earth Sciences. My research examines hillslope processes and slope stability. In addition, I serve as the Chair of the Working Group for Educational Materials under the UN Global Initiative on Resilience to Natural Hazards through AI Solutions.
Biography
Prior to joining Cambridge, I was a PhD at the University of Padova (Italy). I earned my MSc at the University of Florence and my BSc at the University of Genova (Italy).
Publications
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Nava, L., Tordesillas, A., Qian, G., & Catani, F. (2024). Displacement residuals reveal landslide regime shifts. Landslides, 1–16.
https://doi.org/10.1007/s10346-024-02353-2 -
Nava, L., Carraro, E., Reyes-Carmona, C., Puliero, S., Bhuyan, K., Rosi, A., ... & Catani, F. (2023). Landslide displacement forecasting using deep learning and monitoring data across selected sites. Landslides, 1–19.
https://doi.org/10.1007/s10346-023-02104-9 -
Nava, L., Cuevas, M., Meena, S. R., Catani, F., & Monserrat, O. (2022). Artisanal and small-scale mine detection in semi-desertic areas by improved U-Net. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
https://doi.org/10.1109/LGRS.2022.3220487 -
Nava, L., Bhuyan, K., Meena, S. R., Monserrat, O., & Catani, F. (2022). Rapid Mapping of Landslides on SAR Data by Attention U-Net. Remote Sensing, 14(6), 1449.
https://doi.org/10.3390/rs14061449 -
Nava, L., Monserrat, O., & Catani, F. (2022). Improving landslide detection on SAR data through deep learning. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
https://doi.org/10.1109/LGRS.2021.3127073
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Dramsch, J., Kuglitsch, M. M., Fernández-Torres, M.-Á., Toreti, A., Albayrak, R. A., Nava, L., Ghaffarian, S., Cheng, X., Ma, J., Samek, W., Venguswamy, R., Koul, A., Muthuregunathan, R., & Essenfelder, A. H. (2025). Explainability can foster trust in artificial intelligence in geoscience. Nature Geoscience, 1–3.
https://doi.org/10.1038/s41561-025-01639-x -
Bhuyan, K., Rana, K., Ozturk, U., Nava, L., Rosi, A., Meena, S. R., ... & Catani, F. (2025). Towards automatic delineation of landslide source and runout. Engineering Geology, 345, 107866.
https://doi.org/10.1016/j.enggeo.2024.107866 -
Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., ... & Catani, F. (2024). A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images. Earth System Science Data, 2024, 1–42.
https://doi.org/10.5194/essd-16-4817-2024 -
Liu, Y., Teza, G., Nava, L., Chang, Z., Shang, M., Xiong, D., & Cola, S. (2024). Deformation evaluation and displacement forecasting of Baishuihe landslide after stabilization based on continuous wavelet transform and deep learning. Natural Hazards, 1–25.
https://doi.org/10.1007/s11069-024-06580-7 -
Beni, T., Nava, L., Gigli, G., Frodella, W., Catani, F., Casagli, N., ... & Spizzichino, D. (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.
https://doi.org/10.1016/j.enggeo.2023.107286 -
Mirmazloumi, S. M., Wassie, Y., Nava, L., Cuevas-González, M., Crosetto, M., & Monserrat, O. (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), 374.
https://doi.org/10.1007/s10064-023-03388-w -
Meena, S. R., Nava, L., Bhuyan, K., Puliero, S., Soares, L. P., Dias, H. C., ... & Catani, F. (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.
https://doi.org/10.5194/essd-15-3283-2023 -
Bhuyan, K., Meena, S. R., Nava, L., van Westen, C., Floris, M., & Catani, F. (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), 2182057.
https://doi.org/10.1080/15481603.2023.2182057 -
Bhuyan, K., Tanyaş, H., Nava, L., Puliero, S., Meena, S. R., Floris, M., ... & Catani, F. (2023). Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data. Scientific Reports, 13(1), 162.
https://doi.org/10.1038/s41598-022-27352-y -
Lindsay, E., Frauenfelder, R., Rüther, D., Nava, L., Rubensdotter, L., Strout, J., & Nordal, S. (2022). Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape. Remote Sensing, 14, 2301.
https://doi.org/10.3390/rs14102301 -
Sharma, S., Dahal, K., Nava, L., Gouli, M. R., Talchabhadel, R., Panthi, J., ... & Ghimire, G. R. (2022). Natural Hazards Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science. Earth and Space Science, e2021EA002114.
https://doi.org/10.1029/2021EA002114
Book Chapters
- Catani, F., Nava, L., & Bhuyan, K. (2025). Artificial intelligence applications for landslide mapping and monitoring on EO data. In Earth Observation Applications to Landslide Mapping, Monitoring and Modeling (pp. 119–145). Elsevier.
https://doi.org/10.1016/B978-0-12-823868-4.00007-6