7th World Landslide Forum - Session 2.1: ’’Advances in EO-Based landslide detection and monitoring’’ (23-26 november 2026 - Faridabad, India) - scadenza invio abstract o full-paper: 31 marzo 2026
su invito del Socio Matteo Del Soldato, si inoltra la Call for Abstracts e Full Papers relativa alla Sessione 2.1 “Advances in EO-based Landslide Detection and Monitoring” del 7th World Landslide Forum, che si terrà dal 23 al 26 novembre 2026 a Faridabad (India).
Trovate ulteriori informazioni nel messaggio in calce.
We would like to invite you to submit an abstract or a full-paper ( ICL Open Access Book Series – Progress in Landslide Research and Technology (P-LRT), Volume 5, Issue 2 (2026) (Scopus indexed)) to our Session 2.1 - " ADVANCES IN EO-BASED LANDSLIDE DETECTION AND MONITORING" at the 7th World Landslide Forum (23-26 november 2026 - Faridabad, India) - https://wlf7.org/#
The submission deadline is March 31, 2026. Instructions for the abstract or full-paper submission can be found at the following link: https://wlf7.org/call-for-submissions/
Session description: Session 2.1: ADVANCES IN EO-BASED LANDSLIDE DETECTION AND MONITORING Landslides represent a pervasive geohazard with severe implications for infrastructure, human safety, and economic systems. In the context of increasing anthropogenic pressure on unstable slopes – often exacerbated by climate change and land-use transformations – the demand for robust, scalable, and operational tools for landslide mapping and monitoring has become critical. This session focuses on the exploitation of Earth Observation (EO) technologies for the detection, characterization, and temporal analysis of slope instabilities across diverse geomorphological contexts. In particular, we invite contributions leveraging spaceborne Synthetic Aperture Radar (SAR) time series (e.g., from satellite missions such as Sentinel-1, TerraSAR-X, SAOCOM), multispectral and hyperspectral imagery (e.g., from satellite missions such as Sentinel-2, PRISMA), and high-resolution optical data for both event based and long-term monitoring of gravitational processes. We encourage studies demonstrating methodological advances in SAR interferometry (InSAR), digital image correlation, pixel-level change detection, and segmentation-based approaches for landslide recognition and kinematic analysis. Submissions employing machine learning (ML), and hybrid physical-statistical models to fuse EO data with ancillary datasets (e.g., DEM derivatives, precipitation records, soil and lithological maps) are particularly welcome. Special emphasis will be placed on the integration of EO products with ground-based and in situ monitoring systems (e.g., GB-InSAR, LiDAR, GNSS, inclinometers), with the aim of developing near-real-time early warning protocols, deformation models, and hazard scenarios. Case studies demonstrating the operational uptake of EO-derived landslide information by civil protection authorities, risk governance entities, and engineering practice are strongly encouraged.
The Conveners: Matteo DEL SOLDATO, Earth Science Department (DST) of the University of Firenze, 50129 Firenze (Italy), matteo.delsoldato@unifi.it Lorenzo SOLARI, European Environment Agency (EEA), Copernicus Land Monitoring Service (CLMS), Kongens Nytorv 6, 1050 Copenhagen (Denmark), lorenzo.solari@eea.europa.eu John DEHLS, Geological Survey of Norway (NGU), Leiv Eirikssons vei 39, 7040 Trondheim (Norway), john.dehls@ngu.no Lorenzo NAVA, Departments of Earth Sciences and Geography of the University of Cambridge, Downing Street Cambridge Cambridgeshire CB2 3EQ (United Kingdom), lorenzo.nava@kcl.ac.uk Cristina REYES-CARMONA, Department of Earth and Environmental Sciences (DISAT) of the University of Milano-Bicocca, Piazza dell’Ateneo Nuovo, 1 - 20126, Milano (Italy), cristina.reyescarmona@unimib.it Lu PING, College of Surveying and Geo-Informatics, Tongji University, Shanghai (China), luping@tongji.edu.cn
