Who is at risk when mountains and people change together?

1. Integrating geomorphology-based terrain segmentation with machine learning for landslide susceptibility assessment in the Darjeeling Sikkim Himalaya, India. Singh, S., Raju, A., Rosi, A., Singh, R. P., Kumar, S., Floris, M., Meena, S. R. Geosciences, 2026. doi:10.3390/geosciences16100426

Preprints, not yet peer reviewed

1. Mapping landslide change and volume with repeat airborne LiDAR after the May 2023 Emilia-Romagna event: slope-dependent detection limits and lowered areas. Meena, S. R. EarthArXiv preprint, 2026. doi:10.31223/X58R5C

2. Brief communication: NISAR pixel offsets measure but do not statistically resolve pre-failure motion at the 26 August 2026 Bhote Koshi ice–rock avalanche, Nepal. Meena, S. R. EGUsphere preprint, under review for Natural Hazards and Earth System Sciences, 2026. doi:10.5194/egusphere-2026-5462

3. Masked autoencoding (MAE) outperforms joint-embedding prediction (JEPA) for frozen-probe very-high-resolution landslide segmentation. Meena, S. R., Tang, X., Catani, F. EarthArXiv preprint, 2026. doi:10.31223/X56N4B

Earlier work the project builds on

Selected papers by the Principal Investigator and collaborators, published before NATURA or outside its funding. They provide the methods and several of the study areas.

Landslide inventories and mapping

1. Landslide detection in the Himalayas using machine learning algorithms and U-Net. Sansar Raj Meena, Lucas Pedrosa Soares, Carlos Henrique Grohmann, C.J. van Westen, Kushanav Bhuyan, R. P. Singh, Mario Floris, Filippo Catani. Landslides, 2022. doi:10.1007/s10346-022-01861-3

2. Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach. Sansar Raj Meena, Omid Ghorbanzadeh, C.J. van Westen, Thimmaiah Gudiyangada Nachappa, Thomas Blaschke, R. P. Singh, Raju Sarkar. Landslides, 2021. doi:10.1007/s10346-020-01602-4

3. HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery. Sansar Raj Meena, Lorenzo Nava, Kushanav Bhuyan, Silvia Puliero, Lucas Pedrosa Soares, Helen Cristina Dias, Mario Floris, Filippo Catani. Earth System Science Data, 2023. doi:10.5194/essd-15-3283-2023

4. Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data. Kushanav Bhuyan, Hakan Tanyaş, Lorenzo Nava, Silvia Puliero, Sansar Raj Meena, Mario Floris, Cees van Westen, Filippo Catani. Scientific Reports, 2023. doi:10.1038/s41598-022-27352-y

5. Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the U-Net deep learning model. Kushanav Bhuyan, Sansar Raj Meena, Lorenzo Nava, C.J. van Westen, Mario Floris, Filippo Catani. GIScience & Remote Sensing, 2023. doi:10.1080/15481603.2023.2182057

6. Comparison of Earthquake-Triggered Landslide Inventories: A Case Study of the 2015 Gorkha Earthquake, Nepal. Sansar Raj Meena, Sepideh Tavakkoli Piralilou. Geosciences, 2019. doi:10.3390/geosciences9100437

7. UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks. Omid Ghorbanzadeh, Sansar Raj Meena, Thomas Blaschke, Jagannath Aryal. Remote Sensing, 2019. doi:10.3390/rs11172046

8. Enhancing landslide detection in Western Ghats of Kerala, India with deep learning and Explainable AI. Abhilash Sreekumar, Hemalatha Thirugnanam, Sansar Raj Meena, Maneesha Vinodini Ramesh. Scientific Reports, 2025. doi:10.1038/s41598-025-33065-9

Susceptibility and hazard

1. Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors. Zhilu Chang, Filippo Catani, Faming Huang, Gengzhe Liu, Sansar Raj Meena, Jinsong Huang, Chuangbing Zhou. Journal of Rock Mechanics and Geotechnical Engineering, 2022. doi:10.1016/j.jrmge.2022.07.009

2. Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy). Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, Mario Floris, Filippo Catani. Natural Hazards and Earth System Sciences, 2022. doi:10.5194/nhess-22-1395-2022

3. An updating of landslide susceptibility prediction from the perspective of space and time. Zhilu Chang, Faming Huang, Jinsong Huang, Shui‐Hua Jiang, Yuting Liu, Sansar Raj Meena, Filippo Catani. Geoscience Frontiers, 2023. doi:10.1016/j.gsf.2023.101619

4. Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data. Ujjwal Sur, Prafull Singh, Sansar Raj Meena. Geomatics, Natural Hazards and Risk, 2020. doi:10.1080/19475705.2020.1836038

5. A Hybrid Spatial Multi-Criteria Evaluation Method for Mapping Landslide Susceptible Areas in Kullu Valley, Himalayas. Sansar Raj Meena, Brijendra Kumar Mishra, Sepideh Tavakkoli Piralilou. Geosciences, 2019. doi:10.3390/geosciences9040156

6. Landslide displacement forecasting using deep learning and monitoring data across selected sites. Lorenzo Nava, Edoardo Carraro, Cristina Reyes‐Carmona, Silvia Puliero, Kushanav Bhuyan, Ascanio Rosi, Oriol Monserrat, Mario Floris, et al. Landslides, 2023. doi:10.1007/s10346-023-02104-9

Exposure and risk information

1. Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence. Kushanav Bhuyan, Cees Van Westen, Jiong Wang, Sansar Raj Meena. Natural Hazards, 2022. doi:10.1007/s11069-022-05612-4

2. Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal. Sansar Raj Meena, Florian Albrecht, Daniel Hölbling, Omid Ghorbanzadeh, Thomas Blaschke. Natural Hazards and Earth System Sciences, 2021. doi:10.5194/nhess-21-301-2021

Full publication record of the Principal Investigator: Google Scholar · ORCID.