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Open AccessArticle
Bridging Human Expertise with Machine Learning and GIS for Mine Type Prediction and Classification
by
Adib Saliba
Adib Saliba 1,2,
Kifah Tout
Kifah Tout 1
,
Chamseddine Zaki
Chamseddine Zaki 3 and
Christophe Claramunt
Christophe Claramunt 2,*
1
Doctoral School of Sciences and Technology, Lebanese University, Hadath 1533, Lebanon
2
Naval Academy Research Institute, 29160 Lanvéoc, France
3
College of Engineering and Technology, American University of the Middle East, Kuwait City 15453, Kuwait,
[email protected]
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2024, 13(7), 259; https://doi.org/10.3390/ijgi13070259 (registering DOI)
Submission received: 27 May 2024
/
Revised: 5 July 2024
/
Accepted: 19 July 2024
/
Published: 20 July 2024
Abstract
This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of clearance. The model is based on direct input and validation from field decision-makers for their practical applicability and effectiveness, and accurate historical demining data extracted from military databases. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes military-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algorithmic precision for maximal safety and effectiveness in demining.
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MDPI and ACS Style
Saliba, A.; Tout, K.; Zaki, C.; Claramunt, C.
Bridging Human Expertise with Machine Learning and GIS for Mine Type Prediction and Classification. ISPRS Int. J. Geo-Inf. 2024, 13, 259.
https://doi.org/10.3390/ijgi13070259
AMA Style
Saliba A, Tout K, Zaki C, Claramunt C.
Bridging Human Expertise with Machine Learning and GIS for Mine Type Prediction and Classification. ISPRS International Journal of Geo-Information. 2024; 13(7):259.
https://doi.org/10.3390/ijgi13070259
Chicago/Turabian Style
Saliba, Adib, Kifah Tout, Chamseddine Zaki, and Christophe Claramunt.
2024. "Bridging Human Expertise with Machine Learning and GIS for Mine Type Prediction and Classification" ISPRS International Journal of Geo-Information 13, no. 7: 259.
https://doi.org/10.3390/ijgi13070259
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