No abstract

During the last few years, there has been a growing exploitation of consumer-grade cameras allowing one to capture 360&amp;deg; images. Each device has different features and the choice should be entrusted on the use and the expected final output. The interest on such technology within the research community is related to its use versatility, enabling the user to capture the world with an omnidirectional view with just one shot. The potential is huge and the literature presents many use cases in several research domains, spanning from retail to construction, from tourism to immersive virtual reality solutions. However, the domain that could the most benefit is Cultural Heritage (CH), since these sensors are particularly suitable for documenting a real scene with architectural detail. Following the previous researches conducted by Fangi, which introduced its own methodology called Spherical Photogrammetry (SP), the aim of this paper is to present some tests conducted with the omni-camera Panono 360&amp;deg; which reach a final resolution comparable with a traditional camera and to validate, after almost ten years from the first experiment, its reliability for architectural surveying purposes. Tests have been conducted choosing as study cases <i>Santa Maria della Piazza</i> and <i>San Francesco alle scale Churches</i> in Ancona, Italy, since they were previously surveyed and documented with SP methodology. In this way, it has been possible to validate the accuracy of the new survey, performed by means an omni-camera, compared with the previous one for both outdoor and indoor scenario. The core idea behind this work is to validate if this new sensor can replace the standard image collection phase, speeding up the process, assuring at the same time the final accuracy of the survey. The experiment conducted demonstrate that, w.r.t. the SP methodology developed so far, the main advantage in using 360&amp;deg;&amp;thinsp;omni-directional cameras lies on increasing the rapidity of acquisition and panorama creation phases. Moreover, in order to foresee the implications that a wide adoption of fast and agile tools of acquisition could bring within the CH domain, points cloud have been generated with the same panoramas and visualized in a WEB application, to allow a result dissemination between the users.

Abstract:The main goal of the SIT-REM project is the design and the development of an interoperable web-GIS environment for the information retrieval and data editing/updating of the geobotanical and wildlife map of Marche Region. The vegetation, plant landscape and faunistic analysis allow the realization of a regional information system forwildlife-geobotanical data. A main characteristic of the SIT-REM is its flexibility and interoperability, in particular, its ability to be easily updated with the insertion of new types of environmental, faunal or socio-economic data and to generate analyses at any geographical (from regional to local) or quantitative level of detail. Different query levels obtain the latter: spatial queries, hybrid query builder and WMSs usable by means of a GIS. SIT-REM has been available online for more than a year and its use over this period has produced extensive data about users' experiences.

Abstract:The prevention and correct management of natural disaster event sequences play a key role in saving human lives. The availability of embedded and mobile smart computing systems opens new roads for the management of land and infrastructures by civil protection operators. To date, research has explored the use of social networks for the management of disasters connected to meteorological/hydrogeological events or earthquakes, but without emphasis on the importance of an integrated system. The main feature of the Whistland system proposed in this paper is to make synergistic use of augmented reality (AR), crowd-mapping (CM), social networks, the Internet of Things (IoT) and wireless sensor networks (WSN) by exploiting technologies and frameworks of Web 2.0 and GIS 2.0 to make informed decisions about the chain of events. The Whistland system is composed of a geo-server, a mobile application with AR and an analytics dashboard. The geo-server acts as the hub of the sensor and social networks. The abstracted concept in this sense is the transformation of the user domain into "intelligent sensors" for the whole scope of crisis management. The social network integration is made through an efficient pointer-like mechanism that keeps the storage requirement low through a mobile application based on an augmented reality engine and provides qualitative information that sensors are unable to capture. Real-time analyses, geo-searches and the capability to examine event histories with an augmented reality engine all help the stakeholders to understand better the state of the resources under observation/monitoring. The system has been extensively tested in the programmed maintenance of river basins, where it is necessary to log maintenance activities in order to keep the riverbank clean: a significant use-case in many countries affected by hydro-geological instability.

The combination of elevation data together with multispectral high-resolution images is a new methodology for obtaining land use/land cover classification. It represents a step forward for both the accuracy and automation of LULC applications and allows users to setup thematic assignments through rules based on feature attributes and human expert interpretation of land usage. The synergy between different types of information means that LiDAR can give new hints at both the segmentation and hybrid classification steps, leading to a joint use of multispectral, spatial and elevation data. The output is a thematic map characterized by a custom-designed legend that is able to discriminate between land cover classes with similar spectral characteristics (level 3 of the CLC legend). Experimental results from a hilly farmland area with some urban structures (Musone river basin, Ancona, Italy) are used to highlight how the proposed methodology enhances land cover classification in heterogeneous environments.