XTRACTIS FOR defense & security

Passive Magnetic Identification of Land Mines

Benchmark vs. Logistic Regression, Random Forests, Boosted Trees & Neural Networks

How to detect land mines and identify the type of detected mine from a few variables, automatically, efficiently, and in an explainable way?

Goals & benefits

Find the cause-and-effect relationships between the relevant predictors among the 3 parameters of this study, and the actual presence of a mine and its type.

Enhance demining technicians and military experts’ knowledge by understanding the strategies to identify the type of mine.

Help engineers to design enhanced mine detectors, manual or autonomous, embedding a classifier making explainable and accurate automated decisions.

Assist the military profession in making a more reliable decision, thanks to rapid, systematic, explainable, and safer detection process with a passive magnetic sensor.


We get a Predictive Model that is:


A Decision System composed of 29 unchained gradual rules using the 3 variables that XTRACTIS confirmed as significant.


Good Real Performance on External Test.

Efficient & Operational.

Running in real-time up to 70,000 predictions per second (i7 @2.5GHz with 8 physical cores), offline or online (API).

Use Case 03/2023 (v1.1)

Results by
XTRACTIS® GENERATE 13.0.44978 (02/2023)


  1. Problem Definition
  2. Xtractis Solution
  3. Top-Model Induction
  4. Explained Predictions for 3 cases
  5. Top-Models Benchmark