University of Catania

Bridge management is a complex and challenging task at global scale. The main purpose of bridge management is to facilitate the identification of bridge deficiencies in order to carefully plan and execute maintenance operations, and ensure the continued safety of traffic. In 2020, the Italian Ministry of Infrastructure issued mandatory guidelines for risk classification and management, safety assessment and monitoring of bridges. These guidelines rely upon bridge inventory, which includes a number of heterogeneous inspection data organized in different files. This paper presents a hardware and software architecture for the automatic entry of all inspection data (e.g. spatial information, sheets and photos) in a relational spatial database and their visualization through a Desktop and Web GIS (Geographic Information System) application. The procedure is summarized in a new Bridge Management System (BMS), whose main novelty is the full control of the georeferenced infrastructures, with the opportunity of improving the status check and management of bridges and their components. The BMS has been developed using data collected during inspection surveys requested by the Consortium of Sicilian Highways on the conditions of the bridges and viaducts belonging to motorways in Sicily (Italy). The developed system overcomes most of the common limitations in existing BMSs, such as the limited capacity of visualizing geospatial data and the reduced supported data formats, providing a valid Decision Support System in evaluating and prioritizing repair and maintenance actions.

Forest fires are one of the most dangerous events, causing serious land and environmental degradation. Indeed, besides the loss of a huge quantity of plant species, the effects of fires can go far beyond: desertification, increased risk of landslides, soil erosion, death of animals, etc. For these reasons, mathematical models able to predict fire spreading are needed in order to organize and optimize the extinguishing interventions during fire emergencies. This work presents a new system to simulate and predict the movement of the fire front based on free and open source Geographic Information System (GIS) technologies and the Rothermel surface fire spread model, with the adjustments made by Albini. We describe the mathematical models used, provide an overview of the GIS design and implementation, and present the results of some simulations at Etna volcano (Sicily, Italy), characterized by high geomorphological heterogeneity, and where the native flora and fauna may be preserved and perpetuated. The results consist of raster maps representing the progress times of the fire front starting from an ignition point and as a function of the topography and wind directions. The reliability of results is strictly affected by the correct positioning of the fire ignition point, by the accuracy of the topography that describes the morphology of the territory, and by the setting of the meteorological conditions at the moment of the ignition and propagation of the fire.

Classification of roof covering in urban areas using aerial imagery is a challenging task. In this work we present a preliminary mapping of roofs using the high-resolution Skysat multispectral images. The classification is performed using a two-stage machine learning approach: the first stage includes a supervised classification for land use, while the second stage includes the classification of terraces and roofs with one or more pitches in those areas previously recognized as edifices. The methodology has been tested to classify the roofs in the north-east part of the Stromboli Island (Sicily, Italy). Our preliminary results are promising and encourage us to pursue further developments as ways to improve accuracy and reliability of the classification.