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Humanitarian demining is an indispensable part of any post-conflict reconstruction and rehabilitation efforts, as landmines and other unexploded ordnance (UXO) continue to pose a significant threat to civilians and infrastructure. Traditional non- technical survey demining methods are typically time-consuming, labor-intensive, and require professional familiarity with a number of different complex tools and involved procedures. The emergence of the latest artificial intelligence (AI) technologies for deep learning (DL) and data analysis techniques presents an opportunity to transform humanitarian demining by significantly improving its efficiency, speed, accuracy, and safety. One such approach is the development of a data observatory that implements data ingestion pipelines into a data lake using data transformation to improve the usability of unstructured data about a mine scene. This paper addresses the key characteristics and benefits of such a system and its potential to improve humanitarian demining.

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.

In mountainous regions such as mountainous Croatia, natural and semi-natural landscapes are most common and are changing due to climate change and more intense economic activities. Due to these changes, there is a need of inventarisation and valorisation of mountainous landscapes in order to manage and preserve them. In Croatia, there is no inventory that would provide us with a zero status of mountain landscapes. On the other hand, the mountainous environment and the size of the area ​ present an obstacle for landscape mapping. The use of Sentinel-2a imagery and a digital relief model allow ​a fast, inexpensive, and accurate mapping of landscapes in large areas such as mountainous Croatia. Orthophoto images were used to increase the accuracy of sampling and thus the accuracy of the final classification. Remote sensing techniques, GIS analysis and field validation were used to create an inventory of the landscapes types. This type of landscape mapping, which includes geological, geomorphological and land cover data, provides a better understanding of the composition and configuration of mountain landscapes, leading to knowledge-based management and landscape-level protection.

Centar izvrsnosti za računalni vid na Fakultetu elektrotehnike i računarstva (FER) osnovan je 2012. godine sa ciljem okupljanja relevantnih istraživača s FER-a i s drugih sastavnica Sveučilišta. U osnivanju Centra sudjelovalo je sedam sastavnica Sveučilišta u Zagrebu. Ciljevi Centra su jačanje međunarodne vidljivosti Sveučilišta u Zagrebu u području računalnog vida, stvaranje kritične mase istraživača za zajednički nastup u većim znanstvenoistraživačkim i razvojnim projektima, poboljšanje kvalitete doktorskih istraživanja u području računalnog vida i poticanje zajedničkog nastupa prema gospodarstvu radi suradnje i transfera tehnologije. U radu je predstavljena motivacija za osnivanje Centra te znanstvene i stručne aktivnosti članova Centra.

The results of this analysis show that the trends towards open and unrestricted access to data, software, standards, and processing capabilities of the existing cloud computing platforms generally support basic scientific principles such as openness, accessibility and reusability. However, the challenges have been identified that need to be resolved in order to move from open Earth observation data to fully open analyses. For example, fair and impartial access for a heterogeneous group of users’ needs to be ensured, open access to the source code as well as the documentation needs to be provided, and non- restrictive licences that allow the creation of derivative works need to be applied. Looking ahead, we hope that this comparative analysis of the five operational platforms could inspire interested experts and non-expert users to explore the new paradigm of cloud-based platforms for Earth observation data access and processing.

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.

On 19 June 2020, Ivan Racetin defended his doctoral thesis Anomaly detection on hyperspectral and multispectral digital images of a mine suspected area at the Faculty of Geodesy, University of Zagreb., Ivan Racetin obranio je 19. lipnja 2020. na Geodetskom fakultetu Sveučilišta u Zagrebu doktorski rad Detekcija anomalija na hiper­spek­tra­lnim i višespektralnim digitalnim snimkama minski sumnjivog područja.

More than 50% of the world’s populations live in urban areas, and they emit almost 80% of global carbon dioxide. They are also responsible for 75% of energy consumption in the world. That increases pressure on food, water, and especially energy. These facts imply that there is an urgent need to provide low carbon dioxide level in cities with efficient and renewable energy to foster the growth of the green economy. Therefore, attention should be paid to renewable energy sources such as the Sun and be aware of the potential of solar power of some areas. In favor of this assertion, recently published studies have suggested that solar power will generate 20% of global electricity by 2027. The above-mentioned facts and predictions were the motivation for conducting preliminary research on the possibility for predicting solar potential. The predictions of solar power can be provided by means of remote sensing methods. Digitalized analog VNIR aerial images (UMK camera) with very high spatial resolution and Worldview2 channels are used as input data. For the purpose of discriminating the roofs, the classification was made by controlling both sets of images. The average number of sun hours in one day and the average consumption of an ordinary household were calculated. Initial results obtained in this preliminary research are demonstrated in this paper for an urban neighborhood in Zagreb (Croatia).

The resulting map seek to improve the identification of areas where there is no threat so that parts of suspected hazardous areas can be proposed for mine reduction, or suspected hazardous areas can be better defined.

The key object of interest in airborne and satellite Non-technical Survey are the indicators of mine presence and absence. That is why it is of great importance to establish an adequate list of indicators that will ensure the quality use and implementation of aerial and satellite Non- technical Survey. Close cooperation of all experts in the process of airborne and satellite Non- technical Survey is needed. Experts in the Mine Action Centers conduct an analytical assessment of existing Mine Information System data and establish a list of special and general requests for collecting additional information from the depth of Suspected Hazardous Area. Experts in remote sensing methods help them in forming the list of indicators for a specific geographic area. After multi-sensor airborne data collection and procurement of satellite images of a particular Suspected Hazardous Area, mine scene interpreters are locating and extracting the indicators from these images. The level of confidence is then assigned to the extracted indicators. In locating the indicators in the images, mine scene interpreters use expert and contextual knowledge of conducting war doctrines in particular geographic areas and (optional) simpler image processing methods. Geocoded indicators and their confidences are input for the production of Mine Danger Maps. This paper describes the role of indicators in Non-technical Survey processes in Croatia and Bosnia and Herzegovina within TIRAMISU – Advanced Intelligent Decision Support System.

Landmines affect the lives of people living in war-affected countries. The real challenge in humanitarian demining nowadays, within Non-technical Survey, is finding new approaches to SHA reduction. The establishment and use of mine information systems in humanitarian demining can improve the land release process by efficient mapping of suspected hazardous areas and visualizing of mine risk on it. Croatian Mine Action Center established such system and uses it for marking all suspected hazardous areas in Croatia. Nevertheless, data in this system comes from different sources and contains some inherent uncertainty. The level of ambiguity is being attempted to diminish by conducting analytical assessments of mine information system data and by collecting additional information about suspected hazardous areas. The landmine risk mapping requires defining the input data (indicators) from mine information systems and additional data that most suitably capture the association between the spatial objects of interest in the surrounding geographic area. The confidence level for each indicator is determined, which is, in fact, an estimate of the level of uncertainty. The fusion of all existing data is used after that for the purposes of creating of mine danger maps. These maps show the overall uncertainty of all input data and need further action for their use in reduction according to the Croatian Mine Action Center Standard Operational Procedures.

U članku je opisan koncept otkrivanja ilegalnih odlagališta otpada na dijelu područja grada Zagreba primjenom metoda daljinskih istraživanja na satelitskim snimkama visoke rezolucije WorldView-2. Obavljena je segmentacija vrijednosti piksela brojčanih satelitskih snimki, a nakon toga je provedena objektno orijentirana klasifikacija (u softverskom paketu Definiens Professional 5.0) na odabranim izvornim i poboljšanim snimkama navedenog satelitskog sustava prema parametrima: srednjak i standardno odstupanje. Nakon provedene klasifikacije dobiveni su rezultati filtrirani pomoću indikatora ilegalnih odlagališta otpada u GIS-softverskom paketu ArcGIS. Konačni su rezultati interpretirani (parametrima matrice konfuzije) i provjereni (validirani) preklapanjem s referentnim podacima o poznatim ilegalnim nesaniranim odlagalištima otpada i izlaskom na teren.

In the mine action community, Non-Technical Survey is implemented to provide information for better spatial definition of mine or explosive remnants of war contamination. Airborne and spaceborne surveys of wider Suspected Hazardous Area (SHA) regions are often executed to collect information about physical changes and circumstances that may be employed in the subsequent technical interventions. Simple electro-optical sensors which acquire data from the visible part of electromagnetic spectrum, like DSLR or SLR cameras, can be used for this kind of surveys. These aerial RGB images are then inspected by trained human photo (mine scene) interpreter who is familiar with the military doctrine and specific characteristics of the conflict. Mine scene interpreter seeks for the indicators of mine presence and absence. Novel image processing techniques could provide benefits to the standard photointerpretation workflow by making it more efficient. Geographic object-based image analysis (GEOBIA) is one of them. The idea of GEOBIA is to process georeferenced images at the object level, as the entities found in the environment are usually objects (or grouped pixels) instead of single pixels. This approach enables analysis of spatial relations and geometrical object parameters. The main step influencing the GEOBIA results is the quality of image segmentation. In this paper, results of more image segmentation algorithms are tested on the color aerial images of SHA and images of exploded ammunition depot in the Croatia. Acquired results are discussed, while potential and constraints of image segmentation for application in the Non-technical survey are underlined.

Although calibration during geocoding process shows drastic improvements in achieved geocoding accuracy, the internal orientation error remains. In order to properly determine these errors, we must first of all know the mathematical model of mapping of the line sensor. The mathematical model of mapping of the line sensor has been expanded to account for the distortion of the lens in order to eliminate all systematic errors induced by displacement of the real sensor mapping plane from the ideal mathematical model of the central projection. After a mathematical model was established, a calibration of the hyperspectral scanner was performed in the laboratory, observing test samples from 5 different recording sites. Calibration elements are determined by airborne beam equation (BBA method).

Unplanned explosion of the ammunition storage depot in Padjene, Croatia, triggered by the forest fire, happened in September 2011. Croatian research teams engaged in EU FP7 TIRAMISU project, CROMAC – CTDT Ltd. (Croatian Mine Action Center - Centre for Testing, Development and Training) and Faculty of Geodesy, University of Zagreb, promptly reacted by deployment of data acquisition module of Advanced Intelligence Decision Support System (Bajic 2010) for the aerial survey of impacted area. Troubling remnants of the explosion were scattered unexploded ordnances found in the various forms: undamaged, lightly or heavily deformed, burned or corroded. Remaining unexploded ordnances were not varying only in the form, they varied in the size also, ranging from rifle ammunition to cluster bombs. For dealing with these situations ground teams of demining experts are activated for clearance and recovery tasks. Primary objective of aerial survey in visible spectrum is to provide ground teams overview of affected area which serves as input data for planning of terrain clearance actions. This paper explores the capabilities for implementation of anomaly detection methods for extraction of unexploded ordnances on aerial RGB images acquired by consumer grade digital camera. There are two approaches in image analysis for the needs of object detection and extraction: classification and target detection. In the classification approach, the aim is to assign every pixel to the corresponding thematic class. Target detection approach, contrarily, scans the image with the aim to identify the presence of specific object or material (target). Although target detection can be regarded as binary classification with two classes: background and target, due to the difference in the essence of these approaches they are being distinguished. Number of classes is not the only difference between these approaches, term target also states that the number of the target pixels needs to be negligible compared to the number of the background class pixels. Result of this requirement is that different quality assessment methods need to be implemented depending on the approach used. Classification approach, mostly based on Bayes criterion, tends to minimize the probability of detection errors (false alarms and misses). This can easily be achieved by classifying every pixel as background, because of very sparse population of target pixels. Popular Neyman-Pearson criterion (Kay 1998), which maximizes the probability of detection while keeping the probability of false alarm at the constant rate, is more optimal for the target detection. Anomaly detection methods are a subset of target detection methods in which no a-priori information about the target spectra is available. Reed-Xiaoli (RX) (Reed & Yu 1990) algorithm is the benchmark anomaly detector: it calculates the Mahalanobis distance (Mahalanobis 1936) between the pixel under test and the background. Mahalanobis distance uses covariance matrix and mean value which can be calculated from local background (vicinity of pixel under test) or global scene covariance matrix and mean, so we distinguish local and global RX algorithm. In this paper, local and global RX algorithms were applied to the images of exploded ammunition depot acquired by Nikon D90 camera mounted on helicopter. As no ground truth data was feasible, images were visually interpreted. Object were vectorized using the object-based image analysis to reduce the human error in the manual vectorization. Experimental receiver operating curves (Kay 1998) were then constructed for both anomaly detection algorithms used. Results showed that application of anomaly detection methods can speed up the process of visual interpretation, but it cannot be used as completely automatic method for extraction of unexploded ordnances. REFERENCES Bajic, M. (2010): The Advanced Intelligence Decision Support System for the Assessment of Mine-suspected Areas, The Journal of ERW and Mine Action, 14. Kay, S. M. (1998): Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory. Signal Processing, Prentice-Hall, Upper Saddle River, NJ, USA Mahalanobis, P. C. (1936): On the generalised distance in statistics, Proceedings of the National Institute of Sciences of India, 2, 49–55. Reed, I. S., & Yu, X. (1990): Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE Transactions on Acoustics, Speech, and Signal Processing, 38, 1760–1770.

Copernicus is a European Union Programme that was designed to provide service in delivering free and openly accessible data about Earth surface through satellite and in-situ observations. Copernicus provides images of a set of the Sentinel families and contributing missions. Copernicus also collects and provides information from the ground (in-situ data). Copernicus services are available worldwide today. Although the images of the satellite system are characterized by rough resolution (which limits the application of more precise analysis), they can be a good source of information and data that may provide a better observation of certain objects or phenomena of interest on the scene to experts and help them in decision making. This paper presents the analysis of the possibility to use satellite images of Sentinel system. The results of Sentinel system image analysis have been compared with the analysis results and processing of Landsat satellite images (that are also public and free). The information and data obtained as the results of interpretation and processing of Sentinel and Landsat satellite systems images can be used by experts in various fields of human activity as a support in making decision. The fields of human activity covered by the research presented in this paper are the following: forestry and agronomy ; humanitarian demining ; monitoring of natural and man-made disasters (floods, fires, environmental pollution). The results obtained by means of interpretation and processing of Sentinel and Landsat satellite images have been analysed and interpreted. The interpretation of the results has been made visually, statistically and then presented by means of confusion matrix. Some channels (images) of the Sentinel 2 satellite system have better surface resolution than all multispectral channels of the Landsat satellite system. However, the channels of Landsat satellite system have better surface resolution than some channels of the Sentinel 2 satellite system. In both cases, it is the surface resolution of 10 m and higher. Hence, it will not be possible to identify certain objects and phenomena of interest on the scene, but only to detect them with a certain level of confidence. Due to the mentioned fact, the processing has also been made that included the channels of both systems. The bands have been also mutually sharpened. Further research is planned to be carried out within the scope of future concrete project tasks in order to define the procedures more thoroughly that are intended for solving specific problems based on the conclusion obtained from performed research.

Salinization greatly affects the ecosystems, crop production and the economy. Source of salinity can be generally categorized as primary or naturally occurring and secondary or human induced salinity. Primary salinity stems from atmospheric effects on the rocks containing primary minerals for thousands of years, discharged ions are then combined in various salts which are later naturally distributed to remote geographical areas. Improper management of natural resources which results in soil and water salt build-up applies to secondary salinity. The area being analyzed in this paper is the alluvial river Neretva delta. Today, this area is used for intensive agricultural production of fruits and vegetables, from which citrus orchards are being most present. Agronomic production in the river Neretva delta poses substantial economic importance at a local and a national level as well. Lately, the sustainability of this production has become threatened by the increase in the salinity of groundwater and surface waters. Climate, hydrological and hydrogeological processes, including the engineering structures dictate the salinity fluctuation in the delta. There are two main directions of salt water penetration in the river Neretva delta: sea water intrusion through the riverbed and from deep underground layers where groundwater is already salinized because of direct connection with the sea through porous aquifer. Intensive agriculture demands high water levels, particularly in semi-arid climate, which then culminates with the rising of salty groundwater table. Furthermore, drop in quality of water used for irrigation due to reduced precipitation and extensive use of fertilizers and herbicides add to the total salinity in the river Neretva delta. Soil or water salinity is often depicted by measurements of electrical conductivity which is expressed in deciSiemens per meter (dS/m). This paper describes the interrelationship between in situ measurements of electrical conductivity in open watercourses with Landsat TM5 data in the period between May 2009 and October 2011. Water electrical conductivity was measured once per month on the 13 locations mostly placed in the melioration canals and near the pumping stations. Satellite data was firstly corrected for radiometric and atmospheric effects, afterwards the intensity, salinity, and vegetation indices were calculated. A total of 37 satellite images were analyzed in this study, where 23 of them are placed in the path 30 and row 188, while remaining 14 are placed in the same path but in the row 187 of the Worldwide Reference System. These images needed to be analyzed separately since pixel footprint of Landsat TM5 equals to 900 square meters. Pixel misalignment between these two groups of images and spectral mixing of classes at the pixel level, resulted in considerable differences in spectra at a given point for the two substantial images from different rows. In near vicinity of in situ sampling points, pixels classified as cultivated and uncultivated areas were selected. Correlation analysis of the original Landsat bands, their ratios and spectral indices with the electrical conductivity values was performed and accomplished correlation values were discussed.

In the scope of the EU FP7 TIRAMISU project a novel methodology for semi-automatic interpretation of digital multisensor images for the purpose of detection and extraction of unexploded ordnances was developed. Image interpretation is usually being executed by a professional human interpreter, which is highly reliable but simultaneously labor intensive and time consuming procedure. Experience showed that a human interpreter can’t be replaced by automatic methods of digital image processing, while some methods of digital image processing can reduce the workload of the interpreter. Methodology is based on combination of pixel and object based image analysis where lessons and rules learned on test dataset are then applied on other images of the same scene but different locations. Pixel based image analysis like transformations to various color spaces (IHS, CIELAB), Principal Component Analysis (PCA), Independent Component Analysis (ICA) and basic raster math were used. By executing these processing new datasets with different statistical properties are created. Now we have series of artificial layers which serve as input for statistical analysis and enables us better delineation of targeted objects. Object based image analysis offered additional geometrical parameters and reduced the human error in the process of manual vectorization of identified objects. Achieved results of methodology implementation on aerial images of exploded ammunition depot in Padjene (Croatia) are presented. Validation procedure was executed on randomly selected spatial subsets of 25 images and the outputs are shown in the form of confussion matrix.

Advanced Intelligence Decision Support System (AI DSS) is an operational Non-Technical Survey (NTS) system for collecting and processing (additional) data about suspected hazardous areas (SHA) ; data fusion and producing thematic (danger) maps about SHA. The existing Advanced Intelligence Decision Support System (AI DSS) was upgraded within FP7 project TIRAMISU (Toolbox Implementation for Removal of Anti-Personnel Mines, Submunitions and UXO) into TIRAMISU Advanced Intelligence Decision Support System (T–AI DSS) by implementing various new and improved sub-modules. T-AI DSS has benefited from the development of its components in TIRAMISU. The gaps identified by end-users and system operators have been filled in. The objectives include increasing its robustness, decreasing the workload of the operator, improving the semi-automatic mapping of features of interest for the detection of unexploded ordnance UXO, developing simpler imagery and data collecting units, implementing new sensors, optimizing application on different aerial platforms, verification in applications. T-AI DSS is an operational Non-Technical Survey (NTS) system which can integrate several NTS TIRAMISU tools and also use results obtained by several TIRAMISU tools which are not integrated into a system. The system consists of several sub-systems: for aerial multisensor image and data acquisition, for pre-processing and processing, for management of knowledge and contextual information, for implementing the outcomes of subjective photointerpretation, for fusion at pixel level, at features level and at decisions level. T-AI DSS is the only NTS tool within the frame of TIRAMISU that performs fusion. T-AI DSS is the operational TIRAMISU solution for NTS that is proposed to the mine action centres (MAC) worldwide because it is adaptable to specific terrain and situations. A simplified version (without remote sensing data acquisition) has also been developed that can be used in MACs for the support in the SHA assessment, reduction, re-categorisation and inclusion, indicating only mine presence and mine absence derived from MIS data. Services will be provided to realise NTS mission, to ensure transfer of know-how and capacity building.

Indicators of mine presence and absence are the main objects on the scene that need to be detected and spatialy positioned for the purpose of conducting quality aerial non- technical survey of suspected hazardous areas (SHA). Detection of these indicators on the images of the observed scene and positioning of the existing indicators from the mine information system (MIS) enables creation of danger map. Danger map is the result of the fusion of all existing data about mine indicators. Experts of warfare and mining in a particular area can bring conclusions on potential areas that are contaminated by mines on the basis of danger maps. However, the possibility of recognition and extraction of mine presence indicators on aerial and satellite images greatly depends on the image quality and corresponding ground sampling distance, as well as contextual knowledge and experience of mine scene interpreter. Therefore, due to the fact that the experts draw conclusions about SHA based on position of indicators in space and their zones of influence on the environment, it is necessary to add a level of confidence (the level of reliability with which the interpreter evaluates its decision) for each extracted indicator. This information is essential for a more realistic appearance of danger map depending on the available data on the observed scene and from MIS. The confidence of the decision that has been detected and extracted indicator of mine presence affects the level of danger of an area on the danger map. Tools used to automatically extract indicators on the mine scene can be validated in this way. Validation is done based on the true data about mine scene. Internal validation can be carried out by an experienced interpreter of mine scene, while operational validation will be performed after demining of the observed scene.

This paper describes application of methodology for semi-automatic interpretation of digital multisensor images for the purpose of detection and extraction of unexploded ordnances developed within the EU FP7 TIRAMISU project on the site of exploded ammunition depot in Padjene, Croatia. Process relies on combining the advantages of the both radiometric and object based image analysis using the statistical tools where lessons and rules learned on the test image are then applied on other images of the same scene but different location. In this case methodology has been applied on aerial images acquired by consumer DSLR camera mounted on helicopter flying at average height of 300 meters above ground level. Prior to this application perspective preliminary result was achieved for aluminum objects. During the further application, image processing for improved detection and extraction of corroded objects was defined and evaluated. Achieved results of methodology application on different scenes at same location (exploded ammunition depot). At the end, perspective of further research and analysis of this methodology is stated.

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.

Computer-assisted image processing for extracting of IMP on the basis of object oriented analysis (using images segmentation) was carried out (by means of factor and cluster analysis) within the interactive interpretation of images. The final evaluation will be conducted by experienced interpreter and it will be also carried out on the basis of NIIRS and IQM parameters. The one aim of this research is to establish a methodology and guidelines for interactive semi-automatic interpretation of digital images in humanitarian demining for the purpose of detection and extraction of (strong) indicators of mine presence and absence which can be seen on the images. The experience gathered in working with AI DSS in undertaken projects led to the conclusion that a man (the human eye and contextual view on the scene) as interpreter (of mine scene) can hardly be replaced by automatic methods of digital image processing. However, some methods of digital image processing (implemented in certain ways) may help the interpreter in the identification of certain anomalies in the scene and easier detection of indicators. The basic idea of interactive methods of semi-automatic interpretation of digital images of SHA is to provide assistance to the interpreter in the interpretation of digital images rather than to replace him. The objective of this method is to be sensor independent and independent of object of research. The concept of this method was defined and initial research carried out in 2013.

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.

The module of T-AI DSS for data acquisition should provide the stability and reliability of (aerial) data acquisition on each platform. Subsystems (system configurations are different for different platforms) for aerial data acquisition will be examined on multiple platforms (Mi-8, blimp, Bell-206) and should have stable operation, without cancellation, on every platform. A new system for aerial hyperspectral survey: TIRAMISU light hyperspectral imaging system (T-LHSIS), was conceptualized by CTDT, designed, constructed and manufactured by FGUNIZ. T-LHSIS was tested on two platforms: blimp and Bell-206 helicopter by CTDT and FGUNIZ. The features of the system shown in the following text. T-LHSIS is a new tool, which can be used as stand alone equipment or in T-AIDSS. The helicopter Bell- 206 is a new platform which can be used within T-AI DSS. The continuous and stable electricity supply for the aerial acquisition multisensor subsystem is re- designed, developed and built up in T-AI DSS. It is necessary to provide the independence of the power supply for the aerial acquisition system of AI DSS. This module is used for the collection of additional data from the depths of SHA. The requirements (general and specific) for the collection of additional data on SHA-in are the result of work in the module for the analytical assessment of the data in MIS.

The consequences of explosion of ammunition storage depots are spatially scattered unexploded ordnances (UXOs). The unplanned explosion of ammunition depot, caused by extensive forest fire, happened in Pađene, Croatia. Airborne multisensor image acquisition (with Advanced Intelligence Decision Support System ‐ AI DSS) over destroyed ammunition depot in Pađene, Croatia, has been executed. In this paper we introduce a novel methodology for semi‐automatic interpretation of digital multisensor images for the purpose of detection and extraction of UXOs on images provided with AI DSS. It includes methods and procedures based on the statistical and object image analysis for improved identification and extraction of UXOs. The method is designed to assist the interpreter in the analysis of images, rather than as an automatic method for image analysis.

The general survey of the unexploded ordnance (UXO) scattered around the ammunition depot after its unplanned explosion and the ground based measurements of the hyper spectral samples (end members) of UXO have been done in order to create initial spectral library of UXO. The methods, technique and collected data are presented and evaluated. The Johnson’s criterion was used for detection, classification, recognition and identification of UXO in the airborne images (visible and near infrared wavelengths) of the surrounding of the ammunition depot. The test site near Benkovac contains 1000 landmines and 524 metal debris buried in the year 2001 in squares 1x1 m, at depths from 5 to 20 cm below the surface. The locations of 885 landmines and debris are not public and the blind tests are possible. The sparse vegetation covers some squares since the soil is meager. The hyper spectral data have been collected of the 1x1 m squares by the ground based mechanic scanner. The spectral samples of the vegetation inside and outside of the test site have been collected and total Nitrogen contents were measured.

Hrvatski centar za razminiranje (HCR) je još 2004. godine pregledao, definirao i označio cjelokupnu minski sumnjivu površinu (MSP) u Republici Hrvatskoj (RH). Međutim, zbog smanjenja rizika i nedostatka informacija iz dubine MSP-a, njezine granice definirane su u većim površinama nego što ih zauzima u stvarnosti. Razminiranje je dugotrajan i skup proces, a RH se obvezala da će do 201 9. godine riješiti problem mina na svome teritoriju. Takav razvoj situacije dovodi do traženja i pronalaženja rješenja za ubrzavanje rješavanja minskog problema u RH (a i u svijetu) redukcijom definiranog MSP-a. Za redukciju već definiranih MSP-a potrebni su dodatni podaci i informacije iz dubine MSP-a na osnovi kojih će se revizijom postojećeg MSP-a izvršiti redukcija. Svi naknadno prikupljeni podaci i informacije, zajedno s poznatim podacima iz minskog informacijskog sustava (MIS) HCR-a, obrađuju se u sustavu za potporu odlučivanja u uvjetima neodređenosti. Tako dobiveni rezultati prikazuju se na različitim tematskim kartama koje prikazuju položaje objekta i njihove utjecaje na okolinu u prostoru. Dakle, tematske karte na jednom prikazu daju uvid u teme na osnovi kojih eksperti u humanitarnom razminiranju mogu donositi odluke u tom procesu. Takve tematske karte rezultat su metodologije stvarane unutar projekta Space and Airborne Mined Area Reduction Tools (SMART), a dalje su razvijane: u tehnologijskom projektu Sustav za multisenzorsko zrakoplovno izviđanje i nadzor u izvanrednim situacijama i zaštiti okoliša, TP-06/0007-01, Ministarstva znanosti, obrazovanja i sporta RH ; u projektu Deployment of the Advanced Intelligence Advanced Intelligence Decision Support System for Mine Suspected Area Reduction in Bosnia and Herzegovina i autorovoj doktorskoj disertaciji. Ovim radom se uspostavljaju novi nazivi unaprijeđenih i novih tematskih karata koji nedvosmisleno definiraju ono što prikazuju i olakšavaju čitljivost i komunikaciju između proizvođača i korisnika.

This publication presents methodology for application of the Advanced Intelligence Decision Support System (AIDSS) for the assessment of the mine suspected area in the frame of the humanitarian mine action. Besides the methodology, the publication contains instructions, explanation and examples. Its aim is to enable humanitarian mine action experts in the Mine Action Centers (MAC) or Governmental Administration to prepare conditions and to apply AI DSS technology in their country.

Glavni cilj i doprinos ovog rada je uvođenje novih rješenja u sustav za potporu odlučivanju u uvjetima neodređenosti koji je proizašao iz projekta Space and airborne mined area reduction tools (SMART) financiranog od strane Europske komisije. Težište rada je postavljeno na jake indikatore minske prisutnosti na sceni koji do sada nisu analizirani i definirani na ovaj način. Motivacija i glavni doprinos rada je predstavljeni način računanja težina jakih indikatora minske prisutnosti na sceni za izradu tematskog prikaza (karta opasnosti) koji prikazuje razinu opasnosti oko indikatora minske prisutnosti na sceni. Ovaj rad uvodi kartu opasnosti koja pokazuje i uvjerljivost tvrdnji o razini opasnosti. Također je predstavljena i karta uvjerljivosti tvrdnji o razini opasnosti na površini zona opasnosti oko jakih indikatora minske prisutnosti prikazanih na karti opasnosti. Za ovu svrhu korišteni su analitičko hijerarhijski procesi i metode neizrazitih skupova (fuzzy logic). Rad je u cjelosti posvećen smanjenju neizrazitosti u skupu svih dostupnih informacija o jakim indikatorima minske prisutnosti kako bi se donositelju odluke omogućio uvid u sve poznate aspekte problema. Jaki indikatori minske prisutnosti na sceni (rov, prometnica, ukopani zakloni, bunker, suhozid, kameni nasip, most, gaz) interaktivno su istaknuti pomoću segmentacije slike i značajki pojedinog segmenta čime se zamjenjuje subjektivno isticanje indikatora. Jaki indikatori minske prisutnosti na sceni su analizirani metodama faktorske i klasterske analize, i definirani preko značajki općih oblika (duljina/širina, asimetrija, gustoća, indeks oblika, indeks granice, kompaktnost, oblost, prilagođena elipsa, prilagođeni pravokutnik, poluosi najmanje obuhvaćajuće elipse, poluosi najveće okružujuće elipse). I u statističku analizu informacija iz minskih zapisnika (jednog od najvažnijih jakih indikatora minske prisutnosti na sceni) uvode se faktorska i klasterska analiza koje daju objektivne mjere skupa informacija i usporedbu s procjenama eksperta za način miniranja.

Survey and definition of mine suspected area (MSA) of all territory of Republic of Croatia was conducted the by employees of Croatian mine action centre (CROMAC). However, due to the lack of information and in order to reduce the risk for the local population MSA was defined in larger dimension. In the following years, when after MSA was defined, CROMAC was updating it's mined information system (MIS) with new information and did permanently reduction/extension of MSA in accordance with the mentioned information. The multi-sensor system for reconnaissance and surveillance, and decision support system in conditions of uncertainty provide help in collecting and process new information for purposes of humanitarian demining community in Croatia. Requirements for new information on objects that are present on the scene are defined in collaboration with experts from CROMAC. Collecting and processing information are carried out according to those requirements. Collecting information with multi-sensor system is performed in order to find and position an object which was assumed by the experts that can be defended/protected with mines (indicators of mine presence - IMP) or an object for which experts assumed that can be free of mines (indicators of mine absence - IMA). In the conditions of uncertainty, the data fusion of all available data (MIS, GIS, ortho-photo, (war/military) maps, aero-photo images, multi-spectral images, satellite images, contextual data, common and expert knowledge) is used within decision support system. Result of the fusion is a thematic map which displays impacts of all indicators onto their environment and their mutual impacts onto each other. Such map is called a danger map, and it displays degree of danger around indicators within some specific MSA. This thematic image (danger map) is used as help to experts in humanitarian demining for better definition and reduction of MSA. Above mentioned systems were used in three locations in Croatia, and this article will present some results of this processing.

Minefield records on placing mines are the best source of information about presence, position, shape and contents of minefields. Experience showed that those information are not perfect, so, there is need for critical assessment of their value and applicability. In other words, type of imperfection has to be defined, and if possible, expressed quantitatively. This article will show some results of the statistical analysis of information quality in one set of minefield records within specific mined suspected area (MSA). Quality of information obtained from minefield record is important for production of danger map, since minefield records are the stronger indicator of mine presence on the scene with the highest level of danger. Because of the mentioned, it is very important to position a mine-field, and this can be done only based on good quality information from the minefield records.

A frequent and quite normal situation in the crisis management is an instance of uncertainty and a lack of amount, structure, time adequacy and quality of information and data, thus the decisions makers and reacting managers have to deal with this problem. In the technologic project TP-06/0007-01 was implemented Decision Support System (DSS) for the area reduction for mine action management on a strategic level through the use of data fusion methodological approach and the upgrade of the results from SMART project . Although the DSS was applied on the problems of the humanitarian mine, it has wider potential and could be applied in other crisis situations. Data fusion methodological approach is based on (1) the interpreted and fused multi-sensor imagery collected by aerial reconnaissance and surveillance, (2) enhanced and extracted indicators of mine presence and indicators of mine absence, (3) the formalized expert knowledge, (4) on the combined contextual data, (5) the spatial data provided by geographic information system (GIS) and (6) on the data contained in other information systems (e.g. Mine Information System (MIS) of the mine action centre (MAC)). The generic solutions from SMART have been integrated with data fusion methodological approach and implemented in the form of the DSS. SMART is intended to provide some of this additional information that would help in two ways: it can reinforce the suspicion of some places and reduce the suspected area on others. Output of the DSS at the final stage consist of three maps: (1) the map of mine danger measure, (2) confidence map of indicators of mine presence and indicators of mine absence, (3) the map of spatial stability of results. The methodological combination of three of them enables the validation or dismissal of areas proposed for reduction, thus they are suitable for practical use in mine action management of the humanitarian demining.

The key factors of the application of the airborne remote sensing technology for humanitarian demining are the ninefield indicators or the indicators of mine presence and indicator of mine absence and the analytical assessment of the contamination by the land mines. The basic simple concept of the minefield indicators was given in (1), were it was related to the ordinary physical features of the artificial and natural objects in the mine suspicious area. Similar concept was used in other works, while in (2) were introduced signatures of the minefields indicators and additional contextual features. The signatures depend on the sensors used for the remote sensing and this aspect was thoroughly considered in (2). The contextual features can be derived by the analytical assessment of the terrain contamination by the land mines, addition of the formalized knowledge enables efficient use of the indicators for the support of the decision making and for suspicious area reduction.