Combined spatial point pattern analysis and remote sensing for assessing landmine affected areas

Chan, Jonathan Cheung-Wai; Alegria, Aura Cecilia; Veratelli, M. G.; Folegani, Marco; Sahli, Hichem

doi:10.1109/igarss.2012.6352394

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…The semi-structured nature of these records reflects the challenging conditions under which demining operations are conducted [63]. In many cases, demining teams use a combination of sketches, written descriptions, and photographs to create a comprehensive overview of the minefield.…”

Section: Use-case Examplementioning

confidence: 99%

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

Horvat,

Krtalić,

Akagić

et al. 2024

Electronics

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As landmines and other unexploded ordnances (UXOs) present a great risk to civilians and infrastructure, humanitarian demining is an essential component of any post-conflict reconstruction. This paper introduces the Minefield Observatory, a novel web-based datastore service that semantically integrates diverse data in humanitarian demining to comprehensively and formally describe suspected minefields. Because of the high heterogeneity and isolation of the available minefield datasets, extracting relevant information to determine the optimal course of demining efforts is time-consuming, labor-intensive and requires highly specialized knowledge. Data consolidation and artificial intelligence techniques are used to convert unstructured data sources and store them in an ontology-based knowledge database that can be efficiently accessed through a Semantic Web application serving as the Minefield Observatory user interface. The MINEONT+ ontology was developed to integrate diverse mine scene information obtained through non-technical surveys and remote sensing, such as aerial and hyperspectral satellite imagery, indicators of mine presence and absence, contextual data, terrain analysis information, and battlefield reports. The Minefield Observatory uses the Microdata API to embed this dataset into dynamic HTML5 content, allowing seamless usage in a user-centric web tool. A use-case example was provided demonstrating the viability of the proposed approach.

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Section: Use-case Examplementioning

confidence: 99%

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

Horvat,

Krtalić,

Akagić

et al. 2024

Electronics

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“…In previous studies, remnants of war indicators defined as "visual signs that mines or ERW may be near" were extracted from satellite and airborne images and their spatial relationships examined as a basis for risk assessment (Schultz et al, 2016). Testing GIS methods in combination with geostatistical methods for modelling mine risks is on the rise for the purpose of integrating data on demining activities (Alegria et al, 2011); Chan, Alegría, Veratelli, Folegani, and Sahli 2012;Lacroix et al 2013). GIS is integrated in humanitarian demining with different objectives.…”

Section: Background To Airborne and Satellite Remote Sensing In Humanmentioning

confidence: 99%

Development of the TIRAMISU Advanced Intelligence Decision Support System

Krtalić

Bajić

2018

European Journal of Remote Sensing

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The Advanced Intelligence Decision Support System (AIDSS) is the first mine action technology in humanitarian demining to combine remote sensing and data fusion methods with advanced surveillance and reconnaissance in a successful operational system. It aims to provide a reliable, efficient tool to support the process of making decisions about suspected hazardous areas, based on the methodology scientifically developed and validated in the FP5 SMART project. The system was developed through Technology Project TP-006/0007-01, supported by the Ministry of Science, Education and Sports of the Republic of Croatia, and deployed in operations in several suspected hazardous areas in Croatia and Bosnia and Herzegovina in 2008 and 2016. It was upgraded in the TIRAMISU project, and its name changed to TIRAMISU Advanced Intelligence Decision Support System. Gaps identified by end-users and system operators were filled in. Among the main results were innovations for generating mine danger maps. In this paper, only the structure of the system and its potential application in non-technical surveys as part of humanitarian demining are shown.

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“…In Ref. [11], it was further investigated the analysis as part of a GIS-based weighted linear combination approach, where points, lines and polygonshaped variables were modelled as the result of a KDE mapping. The concept has been further enriched by Ref.…”

Section: Methods For Landmines and Erw Risk Mappingmentioning

confidence: 99%

“…Such indicators are often represented as point data. Supported by the idea of using approaches based on landcover/land-use change detection that involve historical images, the following items could be considered as remnants of war indicators [11]: tracks that are no longer in use, abandoned agriculture/pasture areas, and edges of forests. Such indicators are often represented as polygons.…”

Section: Landmines and Erw Risk Indicators: Explanatory Variablesmentioning

confidence: 99%

“…In our case, it can be calculated as the amount of abandoned land per area unit, for zones with different hazard levels. As measure of abandoned land, we use the vegetation changes information obtained using remote sensing data [11]. The EnV is obtained using dual KDE, contrasting the mine action point data TP, against centroids of zones with vegetation changes (or risk indicators points).…”

Section: Distance To Health Facilities Per Population Indexmentioning

confidence: 99%

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Hazard Mapping of Landmines and ERW Using Geo-Spatial Techniques

Alegria

Zimányi

Cornelis³

et al. 2017

J Remote Sensing & GIS

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Landmines and Explosive Remnants of War (ERW) continue to represent a significant nuisance for society in affected countries. Coping with humanitarian and development activities, mine action aims at both, reducing the impacts of the presence of landmines/ ERW on the population, and ultimately returning cleared land to the communities. These are the main tasks of mine action decision makers. This study combines landmine/ERW contamination data with explanatory variables that contain information about underlying targets. They are integrated into a risk mapping framework using Geographic Information Systems with other information sources, such as remote sensing. The aim of this paper is to provide insights into the populations and/or locations at risk caused by landmine and ERW impacts on a broad and local scale. Thus, the concept of 'hotspots' is particularly useful because it provides a visual representation of exposure, aided by a geo-spatial representation of 'priority areas for mine action planners to focus on. We apply the Kernel Density Estimator (KDE) to derive such 'hotspots'. KDE is proposed as the basis to define landmine and ERW hazard, vulnerability, and element-at-risk maps, which enable producing a final output, the landmine/ERW risk map. This is accomplished by using an adaptive kernel bandwidth for datasets with highly heterogeneous spatial distributions, and a problem specific method for generating point samples from polygon data, before using them as inputs for KDE. The geo-statistical model presented here is a time-and-cost-efficient method to construct a landmine risk map, that is as representative as those produced by mine action actors. It can be used as a complement to the risk area maps made by these actors because they are slightly different but show a large degree of overlap. Moreover, the method helps revealing the variables which are the most linked to landmine/ERW-related events in the study area.

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Combined spatial point pattern analysis and remote sensing for assessing landmine affected areas

Cited by 4 publications

References 7 publications

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

Ontology-Based Data Observatory for Formal Knowledge Representation of UXO Using Advanced Semantic Web Technologies

Development of the TIRAMISU Advanced Intelligence Decision Support System

Hazard Mapping of Landmines and ERW Using Geo-Spatial Techniques

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