Europe PMC
open access
Remote sensing-based landslide prediction and risk assessment using a hybrid CNN-LSTM deep learning model
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Hybrid Deep Learning and Object Based Image Analysis Framework for Enhanced Landslide Detection#374722
- From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning#987551
- Detecting Landslides Beyond Hydrology: Spectral Indices and Geochemical Controls#686965
- Explainable AI (xAI) for Landslide Susceptibility Modeling: A Comparative Analysis of Machine Learning and Deep Learning Approaches#443646
- Prediction of changes in landslide rate induced by rainfalls: from the use of a black box model to a 1D mechanical model#417910
Record · ID 5814
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive