Search

Logical representation of knowledge enables reasoning based on formal logic, integration of heterogeneous data sources, augmentation of existing knowledge, and discovery of hidden knowledge that is hidden or unknown even to experienced professionals. Moreover, many high- performance software tools and services for logical reasoning are readily available in the academic community. Computational ontologies, or ontologies for short, are a comprehensive, formal, scalable, and adaptable methods for knowledge representation and automated reasoning. They have been successfully used in many diverse application domains, from medicine and natural sciences to engineering and applied sciences. However, the use of ontologies in the context of demining is novel and has not been exploited to the full advantage. In this context, a new core ontology called MINEONT (an acronym for "MINE-action ONTology") has been proposed. The MINEONT is written in the OWL-DL 2 formalism and allows an expressive and formal representation of concepts in mine action, high- level semantics, geospatial metadata, and information obtained through method of remote sensing, non-technical surveys, and other mine action survey types. The vocabulary of this ontology supports, among others, the following corpora: multisensory aerial and satellite imagery, derived indicators of mine presence and absence, contextual data, terrain analysis information, formalized knowledge of the humanitarian demining specialists in the form of declarations and procedures. The designed ontology has many advantages in describing data for non- technical surveys. However, it is also relevant for building a specialized recommender artificial intelligence (AI) expert system. Such an ontology- based decision support system (DSS) would integrate a large amount of collected data and facilitate interpretation and decision-making processes. In the paper, details about the Web Ontology Language (OWL) are given and its advantages for application in the aerial non- technical survey for humanitarian demining are explained. The MINEONT model is introduced and described in detail. The advantages of MINEONT compared to other existing methods in the aerial non-technical survey system are planned to be tested and demonstrated as part of scientific projects in which domestic and foreign partners, including CROMAC, will participate.

The industry of sensors and unmanned aerial systems (UAV) enables the advancement of detection of explosive objects (landmines, unexploded ordnance, improvised explosive devices) and the non-technical survey of areas contaminated by explosive threats. In the last ten years, dominates research and development of the direct detection of explosive devices versus detection of indirect indicators of the explosive threats on large areas in frame of civilian Non-Technical Survey (NTS). The impact of the mentioned phenomena is not used to its full potentials in the civilian domain of NTS. Although UAV technology can provide a large amount of data, interpretation of the data and the decision-making as an outcome of the interpretation are not developed yet. Between 1998 and 2020 many research and technology development projects on aerial NTS are realized. Some of them applied Decision Support System (DSS) paradigms: GIS-based DSS paradigm, Multi-Criteria-based DSS paradigm, Automatic Target Recognition (ATR) paradigm. The Scientific Council of HCR Center for testing, development, and training, initiated in December 2019 research and development of the Aerial Non- Technical Survey, based on UAV data acquisition, and a recommender system in its DSS functions. The proposed recommender system utilizes deep learning algorithms and collaborative filtering (CF) that rely on datasets containing selection preferences for different objects. DSS knowledge database will be based on ontologies to facilitate formal description of complex semantic structures, knowledge transfer, and reuse. The objects and preferences will be derived by fusion of features of secondary indicators, ranked by eigenvalues. The novel DSS will be implemented in the new Aerial Non-Technical Survey System which will be realized through projects by several partners. The operational validation will be done by CROMAC in mine suspected areas of Croatia.

The crucial document for the success of T-AI DSS application is the analytical assessment of the suspected hazardous area (SHA) based on data and information that are available in MIS of MACs. The outcomes of this analysis are the general and specific requirements for the airborne and space borne collecting and producing new, additional data, information and evidences about the former situation in SHA. The requirement for improvement of this module implies the determination of general guidelines for the implementation of this analysis in any MAC’s of mine-affected countries. According to this requirement, within this T-AI DSS’s module, general guidelines for conducting of the analytical assessment for better definition of the SHA and the definition of general and specific requirements for the collection of additional data (with T-AI DSS) for better re-definition of the SHA have been developed. T-AI DSS is a tool to support decision about the status of SHA. The input includes data from mine information system (MIS), expert knowledge, airborne and satellite data, contextual data, etc. The outcomes are detected and proved positions of indicators of mine presence (IMP) in space, reconstruction of battlefield (in time and space), better (re)definition of SHA, proposals for exclusion from or inclusion in the SHA (thematic maps). The requirement for improvement analytical assessment of (MIS) data module of T-AI DSS refers to the need to determine the general guidelines for the implementation of this analysis in any MAC’s of mine-affected countries. Due to this requirement, general guidelines for conducting of the analytical assessment for better definition of the SHA and the definition of general and specific requirements for the collection of additional data (with T-AI DSS) for better re-definition of the SHA should be developed within this T-Al DSS’s module. Another requirement for the research within the scope of this module is the need to develop simplified version of the T-AI DSS (without the airborne multisensor acquisition and satellite images) that can be used in MAC’s for the support of the SHA assessment, reduction, re-categorisation and inclusion, only with indicators of mine presence and mine absence derived from MIS data and MAC’s standard operational procedures.

The crucial document for the success of T-AI DSS application is the analytical assessment of the suspected hazardous area (SHA) based on data and information that are available in MIS of MACs. The outcomes of this analysis are the general and specific requirements for the airborne and space borne collecting and producing new, additional data, information and evidences about the former situation in SHA. The requirement for improvement of this module implies the determination of general guidelines for the implementation of this analysis in any MAC’s of mine-affected countries. According to this requirement, within this T-AI DSS’s module, general guidelines for conducting of the analytical assessment for better definition of the SHA and the definition of general and specific requirements for the collection of additional data (with T-AI DSS) for better re-definition of the SHA have been developed.

Some of the main problems in humanitarian demining are indeterminacy and lack of knowledge about current situation inside a mine suspected area (MSA). The borders of MSA are determined on the basis of information from mine information system (MIS: detected mines, mine incidents, interviews with population, minefield records, geodetic basis (maps, orthophoto maps), …). However, MSA in Croatia is a ‘’live matter’’ and changes in years after the conflict (people come back and use land in different ways). Because of that, the main idea of fusion of data, a priori information, contextual information and experts’ knowledge is analyze, interpret and combine all type of data about some MSA for getting information on current situation inside this particular MSA. These information are presented on danger maps [3] and support experts for humanitarian demining in the decision making about mine suspected area reduction or confirm suspicions in MSA [3]. In data fusion the images of airborne acquisition are used for detecting and enhancement of indicators of mine absence (IMA) [1] [4] and mine presence (IMP) [1] [4]. Furthermore, in data fusion a priori information, contextual information and experts’ knowledge are used for linking indicators with their impacts on environment and on each other within MSA. These impacts and levels of danger are shown on the danger maps [4]. The confidence map [4] is also an important product of data fusion, because it gives confidence (analytical assessment) of every statement on a danger map.

In this paper, we address the problem of spatial accuracy of the hyperspectral cubes of mountainous and hilly terrain. Achievable spatial accuracy and precision of the hyperspectral images at the mountainous and hilly terrain are influenced by different parameters. In the paper is reported the main achievement for the used survey system with hyperspectral line scanner, the increased spatial accuracy, and precision based on the GCP’s derived from the digital orthophoto map.