PDF 2 Deep learning approaches for landslide information

Deep Learning Approaches for Landslide Information Recognition

Researchers and Institutions

  • Naveen Chandra

    • Affiliation: Wadia Institute of Himalayan Geology, Dehradun, India

    • Corresponding Author: naveenchandra0408@gmail.com

  • Himadri Vaidya

    • Affiliation: Graphic Era Hill University & Graphic Era Deemed to be University, Dehradun, India

Key Points of Research

  • Remote Sensing and AI Advancements

    • Remote sensing is an essential tool for detecting and predicting landslides.

    • Progress in computing technologies significantly impacts AI research in landslide detection.

  • Database Overview

    • Research is based on a database of 204 published articles.

    • Approximately 50% focus on extracting landslide information through deep learning from satellite and UAV images.

    • Proposed methods are categorized into seven parts based on the applied models.

    • Evaluation methods discussed include:

      • Contributing nations

      • Key study locations

      • Distribution of datasets

      • Model utilization

Understanding Landslides

  • Landslide Definition and Impact

    • Landslides (mud-slips, landslips) are serious geological hazards resulting from slope movements (rockfalls, debris flows, mudflows).

    • Consequences include loss of life, property damage, environmental impact, and economic losses.

    • Triggering factors:

      • Natural: Intense rainfall, earthquakes, snowmelt.

      • Anthropogenic: Construction, deforestation, cultivation.

  • Categories of Landslides

    • Classified by:

      • Type of material (flows, slides, topple/falls, spreads).

      • Movement type (soil, debris, earth, mud, rock).

      • Size and velocity (e.g., huge, very large, large, medium, rapid).

Global Landslide Impact

  • Geographical Exposure

    • Approximately 3.7 million km² of the earth's surface is susceptible to landslides, affecting around 300 million people.

    • High mortality and economic loss recorded due to landslides in various regions:

      • Central and South America.

      • Asia, Europe (including Alps), and Turkey.

      • Major incidents:

        • 55,997 deaths from 4,862 landslide events (2004-2016).

        • US$ 40 billion economic impact from landslides in Germany (2021).

        • Significant landslide occurrences in China causing multi-billion yuan losses and fatalities.

Technological Approaches to Landslide Detection

  • Remote Sensing Technology

    • Enables understanding spatial distribution (location, extent, boundaries).

    • Various systems include Optical Imaging, SAR, and GNSS.

    • High-resolution satellite imagery is vital for disaster monitoring.

  • Machine Learning Applications

    • Techniques for landslide identification involve:

      • Supervised Learning: Includes methods such as SVM, RF, ANN.

      • Deep Learning: CNNs and its variants have gained traction in landslide detection.

      • Deep learning models address early warning systems and detection methodologies.

Challenges in Landslide Detection

  • Current Issues and Future Directions

    • The necessity for detailed understanding of landslide behaviors and triggers for prevention measures.

    • Enhancing mitigation requires effective analysis of diverse data from satellite imagery and UAVs.

    • Emphasis on creating robust EWS for public safety based on evolving technologies.

    • Identifying spatial data patterns and improving data accuracy through advanced deep learning methods is essential.

Conclusion and Implications

  • The study aims to synthesize existing knowledge, augment future research, and inspire enhancements in disaster management through technological innovation.

  • An increasing need for reliable deep learning algorithms for effective landslide prediction and recognition is presented.