Land Mines
Donated on 12/19/2022
Detection of mines buried in the ground is very important in terms of safety of life and property. Many different methods have been used in this regard; however, it has not yet been possible to achieve 100% success. Mine detection process consists of sensor design, data analysis and decision algorithm phases. The magnetic anomaly method works according to the principle of measuring the anomalies resulting from the object in the magnetic field that disturbs the structure of it, the magnetic field, and the data obtained at this point are used to determine the conditions such as motion and position. The determination of parameters such as position, depth or direction of motion using magnetic anomaly has been carried out since 1970.
Dataset Characteristics
Tabular, Multivariate, Other
Subject Area
Engineering
Associated Tasks
Classification, Clustering
Feature Type
Real, Integer
# Instances
338
# Features
3
Dataset Information
For what purpose was the dataset created?
PhD thesis
Who funded the creation of the dataset?
Cemal YILMAZ Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey, cemal@gazi.edu.tr Hamdi Tolga Kahraman Department of Software Engineering, Karadeniz Technical University, Trabzon,Turkey htolgakahraman@ktu.edu.tr Salih Soyler Institute of Science, Gazi University, Ankara, Turkey salih_sogler@yahoo.com
What do the instances in this dataset represent?
Land Mines
Are there recommended data splits?
no
Does the dataset contain data that might be considered sensitive in any way?
No
Was there any data preprocessing performed?
processing of missing
Additional Information
Yilmaz, C., Kahraman, H. T., & Söyler, S. (2018). Passive mine detection and classification method based on hybrid model. IEEE Access, 6, 47870-47888.
Has Missing Values?
No
Introductory Paper
By C. Yilmaz, H. Kahraman, Salih Söyler. 2018
Published in IEEE Access
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| V | Feature | Continuous | voltage: output voltage value of FLC sensor due to magnetic distortion | V | no |
| H | Feature | Continuous | high: the height of the sensor from the ground | cm | no |
| S | Feature | Continuous | soil type: 6 different soil types depending on the moisture condition [dry and sandy, dry and humus, dry and limy, humid and sandy, humid and humus, humid and limy] | no | |
| M | Target | Integer | mine type: mine types commonly encountered on land (5 different mine classes) | no |
0 to 4 of 4
Additional Variable Information
Class Labels
Null, Anti-Tank, Anti-personnel, Booby Trapped Anti-personnel, M14 Anti-personnel
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset land_mines = fetch_ucirepo(id=763) # data (as pandas dataframes) X = land_mines.data.features y = land_mines.data.targets # metadata print(land_mines.metadata) # variable information print(land_mines.variables)
KAHRAMAN,Hamdi Tolga. (2022). Land Mines. UCI Machine Learning Repository. https://doi.org/10.24432/C54C8Z.
@misc{misc_land_mines_763,
author = {KAHRAMAN,Hamdi Tolga},
title = {{Land Mines}},
year = {2022},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C54C8Z}
}Keywords
Creators
Hamdi Tolga KAHRAMAN
htolgakahraman@yahoo.com
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.