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Although plastic pollution is one of the most noteworthy environmental issues nowadays, there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which is needed to prevent its negative effects and to plan mitigation actions. Unmanned Aerial Vehicles (UAVs) can provide suitable data for mapping floating plastic, but most of the methods require visual interpretation and manual labeling. The main goals of this paper are to determine the suitability of deep learning algorithms for automatic floating plastic extraction from UAV orthophotos, testing the possibility of differentiating plastic types, and exploring the relationship between spatial resolution and detectable plastic size, in order to define a methodology for UAV surveys to map floating plastic. Two study areas and three datasets were used to train and validate the models. An end-to-end semantic segmentation algorithm based on U-Net architecture using the ResUNet50 provided the highest accuracy to map different plastic materials (F1-score: Oriented Polystyrene (OPS): 0.86; Nylon: 0.88; Polyethylene terephthalate (PET): 0.92; plastic (in general): 0.78), showing its ability to identify plastic types. The classification accuracy decreased with the decrease in spatial resolution, performing best on 4 mm resolution images for all kinds of plastic. The model provided reliable estimates of the area and volume of the plastics, which is crucial information for a cleaning campaign.

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The Smart Cities data and applications need to replicate, as faithfully as possible, the state of the city and to simulate possible alternative futures. In order to do this, the modelling of the city should cover all aspects of the city that are relevant to the problems that require smart solutions. In this context, 2D and 3D spatial data play a key role, in particular 3D city models. One of the methods for collecting data that can be used for developing such 3D city models is Light Detection and Ranging (LiDAR), a technology that has provided opportunities to generate large-scale 3D city models at relatively low cost. The collected data is further processed to obtain fully developed photorealistic virtual 3D city models. The goal of this research is to develop virtual 3D city model based on airborne LiDAR surveying and to analyze its applicability toward Smart Cities applications. It this paper, we present workflow that goes from data collection by LiDAR, through extract, transform, load (ETL) transformations and data processing to developing 3D virtual city model and finally discuss its future potential usage scenarios in various fields of application such as modern ICT-based urban planning and 3D cadaster. The results are presented on the case study of campus area of the University of Novi Sad.

This paper proposes a Serbian cadastral domain model as the country profile for the real estate cadastre, based on the Land Administration Domain Model (LADM), defined within ISO 19152. National laws and other legal acts were analyzed and the incorrect applications of the law are outlined. The national "Strategy of measures and activities for increasing the quality of services in the field of geospatial data and registration of real property rights in the official state records", which was adopted in 2017, cites the shortcomings of the existing cadastral information system. The proposed profile can solve several problems with the system, such as the lack of interoperability, mismatch of graphic and alphanumeric data, and lack of an integrated cadastral information system. Based on the existing data, the basic concepts of the Serbian cadastre were extracted and the applicability of LADM was tested on an obtained conceptual model. Upon obtaining positive results, a complete country profile was developed according to valid national laws and rulebooks. A table of mappings of LADM classes and country profile classes is presented in this paper, together with an analysis of the conformance level. The proposed Serbian country profile is completely conformant at the medium level and on several high-level classes. LADM also provides support for three-dimensional (3D) representations and 3D registration of rights, so the creation of a country profile for Serbia is a starting point toward a 3D cadastre. Given the existence of buildings with overlapping rights and restrictions in 3D, considering expanding the spatial profile with 3D geometries is necessary. Possible solutions to these situations were analyzed. Since the two-dimensional (2D) cadastre in Serbia is not fully formed, the proposed solution is to use the 2D model for simple right situations, and the 3D model for more complex situations.