Fleet Management System for Search and Rescue Operations

Over one million people lost their lives in the last twenty years from natural disasters like wildfires, earthquakes and man-made disasters. In such scenarios the usage of a fleet of robots aims at the parallelization of the workload and thus increasing speed and capabilities to complete time sensitive missions. This work focuses on the development of a dynamic fleet management system, which consists in the management of multiple agents cooperating in order to accomplish tasks. We presented a Mixed Integer Programming problem for the management and planning of mission’s tasks. The problem was solved using both an exact and a heuristic approach. The latter is based on the idea of solving iteratively smaller instances of the complete problem. Alongside, a fast and efficient algorithm for estimation of travel times between tasks is proposed. Experimental results demonstrate that the proposed heuristic approach is able to generate quality solutions, within specific time limits, compared to the exact one.

Deep learning based style transfer for low altitude aerial imagery

Unmanned Aerial Vehicle (UAVs) equipped with cameras have been fast deployed to a wide range of applications, such as smart cities, agriculture or search and rescue applications. Even though UAV datasets exist, the amount of open and quality UAV datasets is limited. So far, we want to overcome this lack of high quality annotation data by developing a simulation framework for a parametric generation of synthetic data. The framework accepts input via a serializable format. The input specifies which environment preset is used, the objects to be placed in the environment along with their position and orientation as well as additional information such as object color and size. The result is an environment that is able to produce UAV typical data: RGB image from the UAVs camera, altitude, roll, pitch and yawn of the UAV. Beyond the image generation process, we improve the resulting image data photorealism by using Synthetic-To-Real transfer learning methods. Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different - although related - problem. This approach has been widely researched in other affine fields and results demonstrate it to be an interesing area to investigate. Since simulated images are easy to create and synthetic-to-real translation has shown good quality results, we are able to generate pseudo-realistic images. Furthermore, object labels are inherently given, so we are capable of extending the already existing UAV datasets with realistic quality images and high resolution meta-data. During the development of this thesis we have been able to produce a result of 68.4% on UAVid. This can be considered a new state-of-art result on this dataset.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

Groupme: city data and multi-preference activity allocation

Classic group recommender systems focus on providing suggestions for a fixed group of people. Our work tries to give an inside look at design- ing a new recommender system that is capable of making suggestions for a sequence of activities, dividing people in subgroups, in order to boost over- all group satisfaction. However, this idea increases problem complexity in more dimensions and creates great challenge to the algorithm’s performance. To understand the e↵ectiveness, due to the enhanced complexity and pre- cise problem solving, we implemented an experimental system from data collected from a variety of web services concerning the city of Paris. The sys- tem recommends activities to a group of users from two di↵erent approaches: Local Search and Constraint Programming. The general results show that the number of subgroups can significantly influence the Constraint Program- ming Approaches’s computational time and e�cacy. Generally, Local Search can find results much quicker than Constraint Programming. Over a lengthy period of time, Local Search performs better than Constraint Programming, with similar final results.

Dynamics of Bora wind over the Adriatic sea: atmospheric water balance and role of air-sea fluxes and orography

The Bora wind is a mesoscale phenomenon which typically affects the Adriatic Sea basin for several days each year, especially during winter. The Bora wind has been studied for its intense outbreak across the Dinaric Alps. The properties of the Bora wind are widely discussed in the literature and scientific papers usually focus on the eastern Adriatic coast where strong turbulence and severe gust intensity are more pronounced. However, the impact of the Bora wind can be significant also over Italy, not only in terms of wind speed instensity. Depending on the synoptic pressure pattern (cyclonic or anticyclonic Bora) and on the season, heavy snowfall, severe storms, storm surges and floods can occur along the Adriatic coast and on the windward flanks of the Apennines. In the present work five Bora cases that occurred in recent years have been selected and their evolution has been simulated with the BOLAM-MOLOCH model set, developed at ISAC-CNR in Bologna. Each case study has been addressed by a control run and by several sensitivity tests, performed with the purpose of better understanding the role played by air-sea latent and sensible heat fluxes. The tests show that the removal of the fluxes induces modifications in the wind approching the coast and a decrease of the total precipitation amount predicted over Italy. In order to assess the role of heat fluxes, further analysis has been carried out: column integrated water vapour fluxes have been computed along the Italian coastline and an atmospheric water balance has been evaluated inside a box volume over the Adriatic Sea. The balance computation shows that, although latent heat flux produces a significant impact on the precipitation field, its contribution to the balance is relatively minor. The most significant and lasting case study, that of February 2012, has been studied in more detail in order to explain the impressive drop in the total precipitation amount simulated in the sensitivity tests with removed heat fluxes with respect to the CNTRL run. In these experiments relative humidity and potential temperature distribution over different cross-sections have been examined. With respect to the CNTRL run a drier and more stable boundary layer, characterised by a more pronounced wind shear at the lower levels, has been observed to establish above the Adriatic Sea. Finally, in order to demonstrate that also the interaction of the Bora flow with the Apennines plays a crucial role, sensitivity tests varying the orography height have been considered. The results of such sensitivity tests indicate that the propagation of the Bora wind over the Adriatic Sea, and in turn its meteorological impact over Italy, is influenced by both the large air-sea heat fluxes and the interaction with the Apennines that decelerate the upstream flow.