Primary competition visual

Classification for Landslide Detection

1 000 CHF
Challenge completed 3 months ago
Classification
Earth Observation
Python
GIS
Computer Vision
Machine Learning
Deep Learning
961 joined
303 active
Starti
Apr 17, 25
Closei
Aug 04, 25
Reveali
Aug 04, 25
About

The optical imagery is sourced from Sentinel-2, specifically from the MultiSpectral Instrument (MSI) at Level-2A, which provides Red, Green, Blue, and Near Infrared (NIR) bands with a resolution of 10 meters.

Complementing this is radar imagery from Sentinel-1, which includes the VV and VH polarization bands (Vertical-Vertical and Vertical-Horizontal) at a similar resolution of 10 meters. The dataset includes post-event radar imagery, which captures backscatter intensity changes on the ground, and change detection bands calculated by subtracting pre-event from post-event data. The radar data is particularly useful as it can penetrate cloud cover, providing a consistent view of ground surface changes regardless of weather conditions.

Together, these optical and radar datasets offer a multi-faceted perspective, combining visual and SAR data to detect ground disturbances that often accompany landslides. Additionally, the dataset is based on landslide inventories from various earthquake-triggered landslide events.

Thank you to Lorenzo Nava, Postdoctoral Research Associate in Multihazard Remote Sensing, Departments of Earth Sciences & Geography, University of Cambridge, Cambridge Complex and Multihazard Research Group (CoMHaz) for the data preparation and starter notebook.

Files
Description
Files
Each of the top 10 participants will need to provide a document covering the aspects in this document.
This is a starter notebook to help you make your first submission.
Is an example of what your submission file should look like. The order of the rows does not matter, but the names of the "ID" must be correct.
Train contains the target. This is the dataset that you will use to train your model.
Test resembles Train.csv but without the target-related columns. This is the dataset on which you will apply your model to.
The .npy files related to the train file.
The .npy files related to the test file.