Satellite Derived Bathymetry (SDB) method uses satellite or other remote multispectral imagery for depth determination in very shallow coastal areas with clear waters. Commonly, SDB survey method can be used when planning hydrographic surveying of marine areas not surveyed or areas with old bathymetric data. This method has become widely used in the past few years. SDB is a survey method founded on analytical modelling of light penetration through the water column in visible and infrared bands. In this article, SDB method was applied by using freeof-charge Landsat 8 and Sentinel 2 satellite images to get the bathymetric data in the area of Hramina Bay in the Central Adriatic. SDB processing procedures and algorithms were described. Processed satellite data was uploaded on geodetic software and ENC S-57 format. The bathymetric map of Hramina Bay obtained by the SDB method was compared with the approach usage band Electronic Nautical Chart (ENC) HR400512 with satisfying positional and vertical accuracy.

<p><strong>Abstract.</strong> Due to climate changes, wildfire breakouts get more frequent and difficult to control. In the mid-July 2017, the wildfire spread from wildland to the city of Split, the second-largest city in Croatia. This unpredictable spread almost caused the collapse of emergency response systems. Fortunately, a greater tragedy was avoided with the composure of the responsible services and the help of the citizens. The citizens helped in extinguishing the fire and timely provided the significant amount of disaster-related information on different platforms and through social media. In this paper, we address the problem of identifying useful Volunteered Geographic Information (VGI) and georeferenced social media, for improving situation awareness while the wildfire was reaching the Croatian city of Split. Additionally, we combine social media with other external data sources (e. g. Sentinel-2 satellite images) and authoritative data (e.g. Croatian National Protection and Rescue Directorate official data and Public Fire Department of Split data) to establish the geographical relations between the wildfire phenomena and social media messages. In this manner, we seek to leverage the existing knowledge and data about the spatiotemporal characteristics of the Split wildfire in order to improve the identification of useful information from georeferenced social media with other integrated data sources that can be valuable for improving situation awareness in wildfire events.</p>

As climate change continues, wildfire outbreaks are becoming more frequent and more difficult to control. In mid-July 2017, a forest fire spread from the forests to the city of Split in Croatia. This unpredictable spread nearly caused emergency systems to collapse. Fortunately, a major tragedy was avoided due to the composure of the responsible services and the help of citizens. Citizens helped to extinguish the fire and provided a large amount of disaster-related information on various social media platforms in a timely manner. In this paper, we addressed the problem of identifying useful Volunteered Geographic Information (VGI) and georeferenced social media crowdsourcing data to improve situational awareness during the forest fire in the city of Split. In addition, social media data were combined with other external data sources (e.g., Sentinel-2 satellite imagery) and authoritative data to establish geographic relationships between wildfire phenomena and social media messages. This article highlights the importance of using georeferenced social media data and provides a different perspective for disaster management by filling gaps in authoritative data. Analyses from the presented reconstruction of events from multiple sources impact a better understanding of these types of events, knowledge sharing, and insights into crowdsourcing processes that can be incorporated into disaster management.

A novel method for structural health monitoring (SHM) by using RGB+D data has been recently proposed. RGB+D data are created by fusing image and laser scan data, where the D channel represents the distance, interpolated from laser scanner data. RGB channel represents image data obtained by an image sensor integrated in robotic total station (RTS) telescope, or on top of the telescope i.e., image assisted total station (IATS). Images can also be obtained by conventional cameras, or cameras integrated with RTS (different kind of prototypes). RGB+D image combines the advantages of the two measuring methods. Laser scans are used for distance changes in the line of sight and image data are used for displacements determination in two axes perpendicular to the viewing direction of the camera. Image feature detection and matching algorithms detect and match discrete points within RGB+D images obtained from different epochs. These way 3D coordinates of the points can be easily calculated from RGB+D images. In this study, the implementation of this method was proposed for measuring displacements and monitoring the behavior of structural elements under constant load in field conditions. For the precision analysis of the proposed method, displacements obtained from a numerical model in combination with measurements from a high precision linear variable differential transformer (LVDT) sensor was used as a reference for the analysis of determined displacements from RGB+D images. Based on the achieved results, we calculated that in this study, the precision of the image matching and fusion part of the RGB+D is ±1 mm while using the ORB algorithm. The ORB algorithm was determined as the optimal algorithm for this study, with good computing performance, lowest processing times and the highest number of usable features detected. The calculated achievable precision for determining height displacement while monitoring the behavior of structural element wooden beam under different loads is ±2.7 mm.

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