AI for good: Machine Learning for Landmine Detection

Machine Learning for Landmine Detection

Machine Learning for Landmine Detection: A Practical End-to-End Project

Landmines continue to endanger communities in Ukraine, Sudan, the Democratic Republic of Congo, Myanmar, and many other regions where conflict has left the land unsafe long after hostilities end. Traditional clearance methods are slow, costly, and labour-intensive. This project explores how machine learning can support faster identification of high-risk areas.

The Motivation
Land contamination limits farming, blocks infrastructure projects, and threatens returning families. ML systems capable of analysing sensor readings, soil characteristics, and terrain patterns can act as force multipliers for humanitarian teams, improving detection coverage and accuracy.

Dataset and Approach
Using the Land Mines Dataset from the UCI Machine Learning Repository, I designed a full ML workflow:
– Data cleansing and exploratory analysis
– Feature engineering grounded in geophysics and signal behaviour
– Training and comparing traditional and gradient-boosted models
– Final model selection: CatBoost
– Packaging the model (.bin), constructing a Flask inference API, and deploying with Docker

Research Foundation
This work builds on insights from Yilmaz et al. (2018), who proposed a hybrid passive detection framework integrating multiple sensor features.

Outcomes
The deployed API returns real-time probability scores and predicted mine classes. Such tools, while experimental, demonstrate how ML can supplement demining efforts and accelerate safe community resettlement.

More technical details, code snippets, and diagrams will follow in my next posts.
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