Chooralmala-Landslide Prediction Using Computer Vision and Its Verification Using Edge Impulse

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 11

Year of Publication : 2025

Authors : Rakesh Rajendran, Narmadha Chakravarthi, Ilangovan Pandian, Meena Krishnamoorthy, Rajanandhini Chandrasekaran, Shiva Kumar Natarajan

10.14445/23488352/IJCE-V12I11P122

How to Cite?

Rakesh Rajendran, Narmadha Chakravarthi, Ilangovan Pandian, Meena Krishnamoorthy, Rajanandhini Chandrasekaran, Shiva Kumar Natarajan, "Chooralmala-Landslide Prediction Using Computer Vision and Its Verification Using Edge Impulse," SSRG International Journal of Civil Engineering, vol. 12,  no. 11, pp. 293-303, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I11P122

Abstract:

In India, landslides are one of the common natural disasters happening across many regions, whose occurrence remains unpredictable. Though most of the landslides are natural, the root cause of such happenings may be linked with manmade disasters like deforestation, rise in population, urbanization over the hills, creating imbalance in the ecosystem, etc. Recently, such a landslide occurred in Chooralmala –Wayanadu on July 30th, 2024. This massive landslide resulted in a greater rate of fatalities and loss for the people living in that region. In this era of technology, using machine learning models and computer vision, natural disasters, especially landslides, could be predicted much earlier than their occurrence. This will pave the way for earlier warning or alarming messages to save lives in those particular red zone areas. In this article, the usage of software like EOSDA-Land viewer, which is an EOMS software, is considered to take the satellite image on a varied timescale over the Chooralmala-Wayanaud district. A machine learning model using Edge Impulse software has been developed to check the anomaly detection using k k-means algorithm, comparing the satellite images taken between the years 2014 and 2024. The satellite images taken through EOSDA-Landviwer are further processed via gray scale conversion, segmentation, and edge detection, followed by anomaly detection using a logic XOR operation, which is enhanced for validation and justification of the result using computer vision.

Keywords:

EOMS, Edge impulse, Chooralmala-Wayanadu, Natural disaster, Landslide.

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