Intelligent Systems for Crisis Management

This paper presents a methodological framework, which establishes links among the: i. 3D mapping, ii. 3D model creation and iii. damage classification grades of masonry buildings by European Macroseismic Scale-98 and the application of geoinformation methods towards 3D mapping and damage assessment after a catastrophic earthquake event. We explore the synergistic exploitation of a Real Time Kinematics system, terrestrial photogrammetry, Unmanned Aircraft Systems and terrestrial laser scanner for collecting accurate and high-resolution geospatial information. The proposed workflow was applied at the catastrophic earthquake of June 12th, 2017 on the traditional settlement of Vrisa on the island of Lesvos, Greece. The Structure from Motion method has been applied on the high-resolution terrestrial and aerial photographs, for producing accurate and very detailed 3D point clouds of the damaged buildings of the Vrisa settlement. Additionally, two Orthophoto maps and two Digital Surface Models have been created, with a spatial resolution of 5 cm and 3 cm, respectively. The first orthophoto map has been created just one day after the earthquake, while the second one, a month later. The significant advantages of the proposed methodology are: (a) the production of reliable and accurate 2D and 3D information at both village and building scales, (b) the ability to support scientists during building damage assessment phase and (c) the proposed damage documentation provides all the appropriate information which can augment all experts and stakeholders, national and local organizations focusing on the post-earthquake management and reconstruction processes of the Vrisa traditional village.

Response management is the first and very critical phase of the disaster management cycle on the post-earthquake reconnaissance efforts. After an earthquake, a rapid damage assessment is vital for emergency response actions. Various types of devices and methods were used in a post-earthquake situation to estimate damages such as deformation of structures. However, standardized procedures during emergency surveys often could not be followed due to restrictions of outdoor operations because of debris or decrepit buildings, the high human presence of civil protection agencies, expedited deployment of survey team and cost of operations. Terrestrial photogrammetry and laser scanning are two of the recently emerging technologies, which became even more preferable in hazard areas, due to there is no need for direct contact with the structure to be assessed. This research aims to discuss the challenges and benefits for the use of the technologies above, focusing on the comparison of the processed models derived from data acquired with these technologies. An evaluation is undertaken whether terrestrial photogrammetry and laser scanning provide high precision and spatial resolution data suitable for post-earthquake building damage assessment. Furthermore, the extracted models are valuable components that help engineering to understand the seismic behavior in a more comprehensible way.

The Disaster Management and Decision Support System (AYDES) is a software, data and analysis platform that provide accurate and current disaster and emergency data, reports, statistics, job inspections, queries, analyses etc. at every stages before and after the disaster. AYDES is a holistic platform integrated with many internal and external systems and services, including desktop, mobile and web-based applications that utilize GIS and RS technologies. It has been developed according to the content of the National Disaster Response Plan of Turkey and designed to be easily used by the National Disaster and Emergency Management Agency of Turkey (AFAD), collaborative Ministries, private institutions and provincial organizations. AYDES consists of three core components with their sub components, namely “Incident Command System”, “Spatial Information System” and “Recovery Information System”. Mobile software tools that can deliver real-time information to the web-based core components of AYDES that consists of applications used for mapping during both post-disaster damage detection and pre-disaster risk reduction. Additionally, in case of a need to disaster event inventories, potentially vulnerable assets, hazard—risk data, affected areas of probable or actual disasters, damage detection results and such data and analyses, two software tools have been developed, namely AYDES-RS, a desktop image processing and analysis software and AYDES-CS, a web-based crowdsourcing software tool whereas two of them enable to allow the use of imagery acquired by remote (space/aerial) technologies for various analyses before and after a disaster. In this chapter, we introduce AYDES and related literature survey about similar disaster management platforms and systems in the world.

This paper explores the application of real-time spatial information from urban transport systems to understand the outbreak, severity and spread of seasonal and pandemic influenza outbreaks from a spatiotemporal perspective. We believe that combining travel data with epidemiological data is the first step to develop a tool to predict future epidemics and better understand the effects that these outbreaks have on societal functions over time. Real-time data-streams provide a powerful, yet underutilised tool when it comes to monitoring and detecting changes to the daily behaviour of inhabitants. Historical datasets from public transport and road traffic serves as an initial indication of whether changes in daily transport patterns corresponds to seasonal influenza data. It is expected that changes in daily transportation habits corresponds to swings in daily and weekly influenza activity and that these differences can be measured through geostatistical analysis. Conceptually one could be able to monitor changes in human behaviour and activity in nearly true time by using indicators derived from outside the clinical health services. This type of more up-to-date and geographically precise information could contribute to earlier detection of influenza outbreaks and serve as background for implementing tailor-made emergency response measures over the course of the outbreaks.

Natural hazards are existence of natural components and processes, which create a situation that could negatively affect people, the economy and the environment. Rising public awareness about natural hazards could improve the quality of life, save financial resources and even save lives. The complicated essence of natural hazards and the interrelations between natural components require a complex analysis of natural hazard factors. In the current research, the factors are analysed by application of analytic hierarchy process. Spatial overlay analysis is done in GIS environment and as a result, landslide susceptibility and flood susceptibility maps are created. Single hazard maps are overlaid in order to receive a complex hazard level. A fuzzy logic with four inputs and one output is designed. This model is used to determine the complex level of hazard considering the factors interaction. The results of the current research and suggested approach could support decision makers in civil protection, territorial planning and management.

One important issue aftermath of disasters is the optimum allocation of the medical assistance to the demanded locations. In this paper, the optimum allocation of the medical assistance to the injured according to a multi-criteria decision making is performed by Multiplicatively Weighted Network Voronoi Diagram (MWNVD). Particle Swarm Optimization (PSO) is applied to optimize the MWNVDs. In this paper, two types of multi-agent rescue models for incorporating the allocation of the medical supplies to the injured locations according to the generated PSO-MWNVDs, wayfinding of emergency vehicles as well as using smart city facilities were proposed. In one of the proposed model, the priority of the injured for receiving the medical assistance, information transfer about the condition of the injured to the hospitals prior to ambulance arrival and updating of ambulance route were considered. Another proposed model has facilities of coordination of emergency vehicles with traffic lights in the intersection and updating of fire engine route compared to the facilities of the first one. The partial difference between the estimated and expected population for receiving the medical assistance in MWNVDs is computed as 37%, while the PSO-MWNVD decreased the mentioned difference to 6%. Also, the time evaluation of the mentioned proposed models and another multi-agent rescue operation model, which uses MWNVD and does not have the studied smart facilities was performed. The results show that the response time of ambulances to the injured and the ambulance mission duration in the proposed model, that has more smart facilities, is improved to other models.

Landslide is a commonly and frequently observed disaster on the Earth both spatially and temporally. The landslide researches mainly aim at characterizing and understanding this process and the earth dynamics, and predicting their occurrence based on triggering factors, their spatial and temporal dimension, thus assess the hazard potential; and estimating the risks which they cause on the economy, environment, and lives. Due to the great variety and amount of data included in this field, it is crucial to form complete landslide databases both at regional level and worldwide. The main aim of this assessment is to bring new insights to the landslide data acquisition aspect by different users. The need of accurate and reliable geodata collection by ordinary people is inevitable for ensuring sufficient spatiotemporal density and distribution, thus forming extensive landslide databases and simulating and planning the future. With the developments in geoinformation technologies, as well as the transforming power of information and communication technologies (ICT) on the society, it became possible to use the citizen science (CitSci) methods in many scientific fields. It has as well enormous potential in landslide data collection, validation and interpretation, and thus contribute to landslide researches. In this review, the uncertainties lead by missing data and affecting quality of regional landslide assessments are discussed, and the potential of citizen science in landslide researches is described. The role of volunteer data is portrayed with specific examples from the literature. The levels of citizen contribution are depicted accordingly.

Unmanned Aerial Vehicle (UAV) is one of the geospatial-based data acquisition technologies which acquire data within a short period for slope mapping studies. Geospatial-based UAV mapping are widely used in many applications, specifically for scientific and mapping research. The capabilities of rapid data acquisition and accessibility to slope risk area are several advantages of using UAV technology. However, the accuracy that influences the output of slope mapping studies using UAV technology need to be considered such as flying altitude and selection of the optimum numbers of Ground Control Points (GCPs). This study focuses on the reviews of geospatial-based UAV mapping, others geospatial-based technologies as well as accuracy assessment of its output. Several considerations were discussed in the production of slope map using UAV technology namely determining the optimum number of GCPs and flying altitudes, as well as evaluating of UAV images. This study presents the production of high resolution slope map area that has been conducted at Kulim, Kedah, Malaysia as the slope location prone to landslide occurrences. Multi-rotor UAV known as DJI Phantom 4 was used for collecting the high resolution images with various flying altitudes. The result of X, Y and Z coordinates show that the accuracy is influenced by the flying altitude of UAV. As for flying altitude is increased, the accuracy of slope mapping is improved. Moreover, the analysis indicated that the slope area coverage and the number of tie point increases as the UAV altitude level also increases.

Natural or man-made disasters are phenomena that can affect large areas and have many environmental, societal and economic impacts. Landslides are among the major disasters of large scale that may affect the natural environment as well as urban areas, often causing massive destruction, loss of property, or even fatalities worldwide. Developing tools that are effective for disaster management is imperative to monitor and mitigate their effect. Satellite data and remote sensing techniques, combined with geological data and studies can provide valuable information regarding monitoring of natural hazards in general and especially of landslides. This chapter concerns the ex ante and ex post study of a complex set of landslides that occurred in the lignite mine of Amynteon in north-western Greece (June 2017). Weakened material cohesion due to fragmentation, further degraded by mining activities and hydrogeological factors led to the catastrophic event. The landslide occurred in along the south faces of the mine, resulting to extended collapses, destruction of mining machinery, evacuation of the adjacent Anargyri village and a big financial impact. Landsat 8 and Sentinel-2 satellite data acquired before and after the event are being used. Digital image processing techniques are applied for change detection. In addition, geological data are being used to provide information about the geological background of the area and landslides vulnerability. Visual interpretation of the area affected by the landslides is also being done, contributing to the overall study.

The application of Structure-from-Motion (SfM) Photogrammetry with ground-based and UAV camera stations may be exploited for modelling the topographic surface of Alpine glaciers. Multi-temporal repeated surveys lead to geometric models that may be applied to analyze the glacier retreat under global warming conditions. Thanks to the integration of point clouds obtained from ground-based and UAV imaging platforms, a complete 3D reconstruction also including vertical and sub-vertical surfaces may be achieved. These 3D models may be also exploited to understand the precursory signals of local collapse that might represent a risk for tourists and hikers visiting glaciers. In this paper a review on the application of SfM Photogrammetry in the field of glaciological studies is reported. The case of Forni Glacier in the Italian Alps is presented as emblematic study. Photogrammetric data sets obtained from measurement campaigns carried out in 2014, 2016, 2017 and 2018 have been processed using a common workflow. Attention is paid to a few crucial aspects, such as image orientation and calibration, dense surface matching, georeferencing and data fusion. In the end, the use of output point clouds to evaluate the risk of collapse in the Forni Glacier is addressed.

A satellite processing platform for high resolution forest assessment (FORSAT) was developed. It generates the digital surface models (DSMs) of the forest canopy by advanced processing of the very-high resolution (VHR) optical satellite imagery and automatically matches the pre- and post-fire DSMs for 3D change detection. The FORSAT software system can perform the following tasks: pre-processing, point measurement, orientation, quasi-epipolar image generation, image matching, DSM extraction, orthoimage generation, photogrammetric restitution either in mono-plotting mode or in stereo models, 3D surface matching, co-registration, comparison and change detection. It can thoroughly calculate the planimetric and volumetric changes between the epochs. It supports most of the VHR optical imagery commonly used for civil applications. Capabilities of FORSAT have been tested in two real forest fire cases, where the burned areas are located in Cyprus and Austria. The geometric characteristics of burned forest areas have been identified both in 2D plane and 3D volume dimensions, using pre- and post-fire optical image data from different sensors. The test studies showed that FORSAT is an operational software capable of providing spatial (3D) and temporal (4D) information for monitoring of forest fire areas and sustainable forest management. Beyond the wildfires, it can be used for many other forest information needs.

Flood is an annual disaster in Malaysia, especially in the east coast region. Recently, other regions in Malaysia have experienced devastating flood as well. To monitor the flood extent, aerial monitoring approach is considered as one of the best measures. Compared to space borne remote sensing, aerial platforms are more reliable in obtaining real time data with higher spatial resolution. Among the obstacles in using space borne remote sensing approach are cloud coverage and revisit limitations, making it less desirable option for flood monitoring. In this chapter, a review of four types of aerial platforms that perform remote sensing task is presented, namely; rotary wings, fixed wings, blimps and helikites. The main criteria discussed in the review are payload capacity, endurance (flight duration), altitudes, tolerable wind speed, vertical take-off and landing ability, and the ability to perform under adverse weather, such as heavy precipitation and winds. From the findings, there are lack of studies that mentioned about the capability of aerial platform in rough weather conditions. Out of all four types of aerial platforms discussed, helikite is seen to be the most suitable device to fly in adverse weather. Nevertheless, the only drawback in using helikite is that it has mobility issue since it is tethered to the ground. Helikite application is suitable for small area coverage. As for future recommendations, the study to evaluate the helikite’s reliability in performing such task has a great opportunity to be pursued further.