Aerial surveys in coastal areas using Unmanned Aerial Vehicles (UAVs) present many limitations. However, the need for detailed and accurate information in a marine environment has made UAVs very popular. The aim of this paper is to present a protocol which summarizes the parameters that affect the reliability of the data acquisition process over the marine environment using Unmanned Aerial Systems (UAS). The proposed UAS Data Acquisition Protocol consists of three main categories: (i) Morphology of the study area, (ii) Environmental conditions, (iii) Flight parameters. These categories include the parameters prevailing in the study area during a UAV mission and affect the quality of marine data. Furthermore, a UAS toolbox, which combines forecast weather data values with predefined thresholds and calculates the optimal flight window times in a day, was developed. The UAS toolbox was tested in two case studies with data acquisition over a coastal study area. The first UAS survey was operated under optimal conditions while the second was realized under non-optimal conditions. The acquired images and the produced orthophoto maps from both surveys present significant differences in quality. Moreover, a comparison between the classified maps of the case studies showed the underestimation of some habitats in the area at the non-optimal survey day. The UAS toolbox is expected to contribute to proper flight planning in marine applications. The UAS protocol can provide valuable information for mapping, monitoring, and management of the coastal and marine environment, which can be used globally in research and a variety of marine applications.

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

Unmanned aerial systems (UAS) are widely used in the acquisition of high-resolution information in the marine environment. Although the potential applications of UAS in marine habitat mapping are constantly increasing, many limitations need to be overcome—most of which are related to the prevalent environmental conditions—to reach efficient UAS surveys. The knowledge of the UAS limitations in marine data acquisition and the examination of the optimal flight conditions led to the development of the UASea toolbox. This study presents the UASea, a data acquisition toolbox that is developed for efficient UAS surveys in the marine environment. The UASea uses weather forecast data (i.e., wind speed, cloud cover, precipitation probability, etc.) and adaptive thresholds in a ruleset that calculates the optimal flight times in a day for the acquisition of reliable marine imagery using UAS in a given day. The toolbox provides hourly positive and negative suggestions, based on optimal or non-optimal survey conditions in a day, calculated according to the ruleset calculations. We acquired UAS images in optimal and non-optimal conditions and estimated their quality using an image quality equation. The image quality estimates are based on the criteria of sunglint presence, sea surface texture, water turbidity, and image naturalness. The overall image quality estimates were highly correlated with the suggestions of the toolbox, with a correlation coefficient of −0.84. The validation showed that 40% of the toolbox suggestions were a positive match to the images with higher quality. Therefore, we propose the optimal flight times to acquire reliable and accurate UAS imagery in the coastal environment through the UASea. The UASea contributes to proper flight planning and efficient UAS surveys by providing valuable information for mapping, monitoring, and management of the marine environment, which can be used globally in research and marine applications.

The transition of a city to a smart city depends on the preservation of open spaces because they can ensure both a safe and a quality living. In a smart city context, it is important for planners to pre-allocate resources during planning phase in order to satisfy the demand during catastrophic events. Geo-computation approaches can contribute towards the spatial-optimization of urban open-spaces for evacuation purposes in cases of catastrophic events. This work will use a location allocation spatial model to facilitate the planing of urban evacuation actions in Mytilini, Lesvos, Greece. Spatial analysis techniques have evolved during the last decades, mainly due to increased computation resources and other complementary technological advances. In this article, the authors attempt to show the contribution of spatial analysis in emergency management planning towards the smart city vision.

Finding an optimal path in a road network is a method of planning and decision-making that is mainly related to transportations and emergency response. The paper presents an algorithm for finding optimal paths in spatial networks, through the utilization of open source GIS and mathematical analysis of Networks using Graph Theory as well as using geographical proximity attributes of network nodes. The geometric and spatial information of the network as well as its relations with points of interest (POI) of the study areas located at the nodes and edges of the network, are transformed into spatial information, which by applying spatial queries in a geographical database (Postgis/Pgrouting) give query-enabled paths. The case study for the application of the algorithm and finding a route based on spatial queries is the island of Lesvos. This island combines intense topography and a complex road network with multiple geometric relationships. The area also has points of interest such as cultural, tourist and social. The final route will be a synthesis of morphological, tourist and cultural elements similar to the spatial search queries. Finally, the methodology as well as the search algorithm can be applied to any Spatial Network (transportations, environment, energy) described by its geographical features, considering all kinds of geographical issues, thus solving spatial problems and contributing to local development.

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In Greece, a lack of a planning strategy was identified in the context of allocating students to schools. Particularly, the Secondary Educational Management of Lesvos Prefecture along with school Principals decide upon student allocation based on empirical knowledge and approximation techniques. As a consequence, during the school season of 2018-2019 capacity and proximity limitations were violated. This study introduces a Spatial Decision Support System (SDSS) to assist school location-allocation decisions in future seasons. The objective of the proposed SDSS is to minimize commute-to-school distance concerning capacity and proximity limitations. For this purpose, a capacitated P-median approach is adopted and formulated as a mixed-integer linear problem. The SDSS is further evaluated using actual data for students' transition from primary to secondary education in the city of Mytilene, Greece. Evaluation of current allocation practices carried out and further compared to those obtained by the SDSS. The results indicate a decrease of 8% in total distance whereas proximity and capacity constraints were respected accordingly. The results may be potentially useful for school planners to assist the allocation decisions in the city of Mytilene.

The mathematical analysis of a spatial network using graph theory and Geographical Information Systems (GIS) for path finding, has created the need to compare possible solutions to better solve spatial problems in road networks. The paper aims to provide a comprehensive and documented selection of the identification of similar routes on a spatial network through the development of a spatial Similarity Index. The index compares the geographical characteristics of routes (altitude, length, distance from points of interest) drawn in a spatial network and calculates the percentage of similarity between the routes and the criteria that contributed to their drawing. The purpose of this multicriteria indicator is to select the optimal solution for spatial problems that occur in a network, such as transport, energy, environment, sport, and tourism. This leads to the Similarity Index serving as a reliable tool in decision-making for local and regional development. The case study is the Greek island of Lesbos, with a complex road network that develops over a relief with strong differences in altitude. In addition, there are many points of tourist, cultural and economic interest on the island, which helps to find the path that largely fulfils all geographical parameters.