Monitoring infrastructures is becoming an important and challenging issue. In Italy, the heritage consists of more than 60,000 bridges, which need to be inspected and detected in order to guarantee their strength and durability function during nominal lifespan. In this paper, a non-destructive survey methodology for study concrete bridges surface deterioration and viaducts is presented. Terrestrial and unmanned aerial vehicle (UAV) photogrammetry has been used for visual inspection of a standard concrete overpass in L’Aquila (Italy). The obtained orthomosaic has been processed by means of Object-Based Image Analysis (OBIA) to identify and classify deteriorated areas and decay forms. The results show a satisfactory identification and survey of deteriorated areas. It has also been possible to quantify metric information, such as width and length of cracks and extension of weathered areas. This allows to perform easy and fast periodic inspections over time in order to evaluate the evolution of deterioration and plan urgency of preservation or maintenance measures.

The main advantage of using the Unmanned Aerial Vehicle (UAV) photogrammetry in a post-earthquake scenario is the ability to completely document the state of the structures and infrastructures, damaged by the earthquake, ensuring the safety of all operators during the data acquisition activities. The safety and accessibility aspect in the area is of crucial concern after an earthquake and sometimes many areas may be inaccessible, but, at the same time, it is necessary to collect data in order to monitor and evaluate the damage. The development of new algorithms in the field of Computer Vision drastically improved the degree of automation of the 3D point clouds generation using the photogrammetry techniques. In addition, data acquisition techniques using the UAV allow a complete 3D model with the highest possible resolution especially with respect to the conventional satellite or aerial photogrammetry to be produced. These advantages make the UAV photogrammetry highly suitable for surveys in a geo-hazard context as in a post-earthquake scenario. Some results from surveys carried out with the UAV photogrammetry after L'Aquila Earthquake occurred in 2009 will be presented and discussed.

On April 6, 2009, an earthquake of 6.3 magnitude struck central Italy with its epicentre near L'Aquila, at 42.3502° N, 13.3762° E. The earthquake damaged 3000 to 11 000 buildings in the medieval city of L'Aquila. Several buildings totally collapsed, 308 people were killed. The post emergency phase till now is just at its beginning step. Conventional surveying techniques using high precision total stations, GNSS receivers and laser scanners for investigations on damaged buildings are always becoming more automated, accurate and operative and even much more fast. Even if these techniques represent instruments of extreme operability there are still many evident limits on their use, especially regarding the survey of both the roofs and the facades of tall buildings or dangerous places, typical on post earthquake situations. So using micro UAVs for surveying in such particular cases, many of these problems can be easily bypassed. In fact, the present work aims on experimenting using multi-rotor micro UAVs, that will allow high quality image capturing on roofs and facades of structures in the old city center of L'Aquila. To obtain actual stereoscopic acquisitions of buildings some conditions on the geometry of acquisition have to be observed, for this reason, taking as a guideline classic flight photogrammetric, a flight planning software was developed. Accurate planning for UAVs acquisitions is very important also considering the reduced autonomy of such vehicles. This can be a strategic point if we want to use UAVs for early damage assessment and also for post event reconstruction planning.

Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due to anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial to preserve these areas for safeguarding not only coastal ecosystems and cultural heritage, but also the population living there. In this context, monitoring coastal areas is essential and geomatics techniques, especially satellite remote sensing imagery, might prove very advantageous. In this paper, a semi-automatic methodology to extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 and optical Sentinel-2 satellite images was developed. An experimental algorithm, called J-Net Dynamic, was tested in two pilot sites. The semi-automatic methodology was validated with GNSS (Global Navigation Satellite System) reference shorelines and demonstrated to be a powerful tool for a robust extraction of the shoreline both from optical and SAR images. The experimental algorithm was able to extract the shoreline closer to the reference with SAR images on the natural beach of Castelldefels and it was demonstrated to be less sensitive to speckle effects than the commonly used Canny Edge Detector. Using the SAR images of the urban beach of Somorrostro, the Canny detector was not able to extract the shoreline, while the new algorithm could do it but with low accuracy because of the noise induced by man-made structures. For further investigation, the Sentinel-2-extracted shorelines were also compared to the ones extracted by a state-of-the-art tool, CoastSat, in the two beaches using both automatic and manual thresholds. The mean errors obtained with J-Net Dynamic were generally higher than the ones from CoastSat using the manual threshold but lower if using the automatic one. The proposed methodology including the J-Net Dynamic algorithm proves to extract the shorelines closer to the reference in most of the cases and offers the great advantage of being able to work with both optical and SAR images. This feature could allow to reduce the time lag between satellite derived shorelines paving the way to an enhanced monitoring and management of coastal areas.

Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including the WorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images.