Landslides are a natural phenomenon with devastating consequences, frequent in many parts of the world. Thousands of small and mediumsized ground movements follow earthquakes or heavy rainfalls. Landslides have become more damaging in recent years due to climate change, population growth, and unplanned urbanization in unstable mountain areas. Early landslide detection is critical for quick response and management of the consequences. Accurate detection provides information on the landslide's exact location and extent, which is necessary for landslide susceptibility modelling and risk assessment.

Recent advances in machine learning and computer vision combined with the growing availability of satellite imagery and computational resources have facilitated rapid progress in landslide detection. Landslide4Sense aims to promote research in this direction and challenges participants to detect landslides around the globe using multisensor satellite images. The images are collected from diverse geographical regions offering an important resource for remote sensing, computer vision, and machine learning communities.

Timestamp,
0:00 Background
1:22 Introduction
4:12 Installation of Libraries and basic setup
5:37 Testing the dataset
8:42 Creating training dataset
13:43 Custom loss function (Dice Loss)
14:33 Visualization of train dataset
15:00 Train/test split data
16:50 UNet model for segmentation
21:12 Model testing
23:25 Prediction for validation data
26:20 Writing the prediction into h5 format
27:55 Summary and final note


#landslide #deeplearning #neuralnetwork #landslide4sense

Tutorial notebook: https://github.com/iamtekson/landslid...
Landslide4Sense challenge and data: https://www.kaggle.com/datasets/tekba...


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