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Predictive Analysis of Landmine Risk

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number8765724
Pages (from-to)107259-107269
Number of pages11
JournalIEEE Access
Early online date19 Jul 2019
Accepted/In press11 Jul 2019
E-pub ahead of print19 Jul 2019
Published19 Aug 2019


  • 08765724

    08765724.pdf, 2.61 MB, application/pdf

    Uploaded date:18 Sep 2019

    Version:Final published version

    Licence:CC BY

King's Authors


Demining is a highly impactful but complex problem which requires considerable resources and time. Land mine detection is the most hazardous and time consuming of the tasks in the demining pipeline. Currently, the risk of landmines being present in an area is estimated on the basis of non-technical surveys which are expensive and slow. This paper presents a novel spatial landmine risk prediction model to help improve allocation of resources in demining operations and even predict future areas of interest. Our approach is based on training predictive models on geographical and social development data for areas recorded to have been demined in the past. We then use this model to predict areas with high chance of mine presence in the vicinity of the demined area so as to progressively expand the area of operations. We explore weighted classification and biased scoring methods to improve the performance of our base logistic regression and support vector machine models. Refinement of conventional models allows us to tackle the problem of unbalanced datasets in our application. The resulting pipeline is then characterised in terms of various performance metrics. The results show that the pipeline has a potential to provide reliable predictive information based on historic demining data, which can help organizations plan their resource allocations in future demining operations.

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