Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation

Semantic segmentation models have reached remarkable performance across various tasks. However, this performance is achieved with extremely large models, using powerful computational resources and without considering training and inference time. Real-world applications, on the other hand, necessitate models with minimal memory demands, efficient inference speed, and executable with low-resources embedded devices, such as self-driving vehicles. In this paper, we look at the challenge of real-time semantic segmentation across domains, and we train a model to act appropriately on real-world data even though it was trained on a synthetic realm. We employ a new lightweight and shallow discriminator that was specifically created for this purpose. To the best of our knowledge, we are the first to present a real-time adversarial approach for assessing the domain adaption problem in semantic segmentation. We tested our framework in the two standard protocol: GTA5 to Cityscapes and SYNTHIA to Cityscapes. Code is available at: https://github.com/taveraantonio/RTDA.Comment: Accepted at I-RIM 3D 202

Integrated Underground Analyses as a Key for Seasonal Heat Storage and Smart Urban Areas

The design and performance of a shallow geothermal system is influenced by the geological and hydrogeological context, environmental conditions and thermal demand loads. In order to preserve the natural thermal resource, it is crucial to have a balance between the supply and the demand for the renewable energy. In this context, this article presents a case study where an innovative system is created for the storage of seasonal solar thermal energy underground, exploiting geotechnical micropiles technology. The new geoprobes system (energy micropile; EmP) consists of the installation of coaxial geothermal probes within existing micropiles realized for the seismic requalification of buildings. The underground geothermal system has been realized, starting from the basement of an existing holiday home Condominium, and was installed in dry subsoil, 20 m-deep below the parking floor. The building consists of 140 apartments, with a total area of 5553 m2, and is located at an altitude of about 1490 m above sea level. Within the framework of a circular economy, energy saving and the use of renewable sources, the design of the geothermal system was based on geological, hydrogeological and thermophysical analytical studies, in situ measurements (e.g., Lefranc and Lugeon test during drilling; Rock Quality Designation index; thermal response tests; acquisition of temperature data along the borehole), numerical modelling and long-term simulations. Due to the strong energy imbalance of the demand from the building (heating only), and in order to optimize the underground annual balance, both solar thermal storage and geothermal heat extraction/injection to/from a field of 380 EmPs, with a relative distance varying from 1 to 2 m, were adopted. The integrated solution, resulting from this investigation, allowed us to overcome the standard barriers of similar geological settings, such as the lack of groundwater for shallow geothermal energy exploitation, the lack of space for borehole heat exchanger drilling, the waste of solar heat during the warm season, etc., and it can pave the way for similar renewable and low carbon emission hybrid applications as well as contribute to the creation of smart buildings/urban areas

Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images

In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e.g., a field of crops and a small vehicle). We propose a solution to these problems based on two ideas: (i) we use together a set of suitable augmentation and a consistency loss to guide the model to learn semantic representations that are invariant to the photometric and geometric shifts typical of the top-down perspective (Augmentation Invariance); (ii) we use a sampling method (Adaptive Sampling) that selects the training images based on a measure of pixel-wise distribution of classes and actual network confidence. With an extensive set of experiments conducted on the Agriculture-Vision dataset, we demonstrate that our proposed strategies improve the performance of the current state-of-the-art method.Comment: CVPR 2022 Workshop - Agriculture Visio

IDDA: a large-scale multi-domain dataset for autonomous driving

Semantic segmentation is key in autonomous driving. Using deep visual learning architectures is not trivial in this context, because of the challenges in creating suitable large scale annotated datasets. This issue has been traditionally circumvented through the use of synthetic datasets, that have become a popular resource in this field. They have been released with the need to develop semantic segmentation algorithms able to close the visual domain shift between the training and test data. Although exacerbated by the use of artificial data, the problem is extremely relevant in this field even when training on real data. Indeed, weather conditions, viewpoint changes and variations in the city appearances can vary considerably from car to car, and even at test time for a single, specific vehicle. How to deal with domain adaptation in semantic segmentation, and how to leverage effectively several different data distributions (source domains) are important research questions in this field. To support work in this direction, this paper contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains. The dataset has been created to explicitly address the challenges of domain shift between training and test data in various weather and view point conditions, in seven different city types. Extensive benchmark experiments assess the dataset, showcasing open challenges for the current state of the art. The dataset will be available at: https://idda-dataset.github.io/home/ .Comment: Accepted at IROS 2020 and RA-L. Download at: https://idda-dataset.github.io/home

THE SDGs IN THE REPORTS OF THE ITALIAN COMPANIES. RESEARCH DOCUMENT N. 16

The document represents the first result of the study conducted by the research group "SDGs and business reporting" and aims to be the starting point of a process for corporate awareness towards sustainable development objectives. The document reveals our country commitment on Agenda 2030; a commitment that involves the entire "Italian system" in pursuit of the 17 sustainable development goals, through the active role of Italian companies as operators. Hence, it not only creates economic value on sustainable development but also it sensitize other companies, end users and the community in general. Although the results depict a sustainable development goals reporting in becoming and not entirely conscious, they provide inputs for entrepreneurs, directors, managers, regulators, consultants, etc. who, for various reasons, are the actors in a process of profound business change that is affecting the corporate reporting and disclosures. The document provides, in this context, useful hints to a better understanding the new corporate reporting direction; indeed, reporting is increasingly affected by an accountability process and responsibility towards both internal and external stakeholders

The real-time multiparametric network of Campi Flegrei and Vesuvius

Volcanic processes operate over a wide range of time scale that requires different instruments and techniques to be monitored. The best approach to survey a volcanic unrest is to jointly monitor all the geophysical quantities that could vary before an eruption. The monitoring techniques are sometimes peculiar for each volcano, which has its own behavior. The simultaneous investigation of all the geophysical and geochemical parameters improves the sensibility and the understanding of any variation in the volcanic system. The Osservatorio Vesuviano is the INGV division charged of the Campi Flegrei and Vesuvius monitoring, two of the highest risk volcanic complexes in the world due to the large number of people living on or close to them. Each of them have peculiarities that increase the monitoring challenge: Campi Flegrei has high anthropic noise due to people living within its numerous craters; Vesuvius has a sharp topography that complicates the data transmission and analysis. The real time monitoring of the two areas involves several geophysical fields and the data are transmitted by a wide data-communication wired or radio infrastructure to the Monitoring Centre of Osservatorio Vesuviano: - The seismic network counts of 20 station sites in Campi Flegrei and 23 in Vesuvius equipped with velocimetric, accelerometric and infrasonic sensors. Some of them are borehole stations. - The GPS network counts of 25 stations operating at Campi Flegrei caldera and 9 stations at Vesuvius volcano. All the procedures for remote stations managing (raw data downloading, data quality control and data processing) take place automatically and the computed data are shown in the Monitoring Centre. - The mareographic network counts of 4 stations in the Campi Flegrei caldera coast and 3 close to the Vesuvius that transmit to the Monitoring Centre where the data are elaborated. - The tiltmetric network consist of 10 stations distributed around Pozzuoli harbor, the area of maximum ground uplift of Campi Flegrei, evidenced since 2005, and 7 stations distributed around the Vesuvius crater. Each tiltmetric station is also equipped with a temperature and magnetic sensor. The signals recorded are sent to the Monitoring Centre. - The 4 marine multiparametric stations installed in the Pozzuoli gulf send accelerometric, broad band, hydrophonic and GPS data to the Monitoring Centre. - The geochemical network counts of 4 multiparametric stations in the fumarolic areas of Campi Flegrei and 2 stations in the Vesuvius crater (rim and bottom) with data transmission to the Monitoring Centre. They collect soil CO2 flux, temperature gradient and environmental and meteorological parameters and transmit them directly to the Monitoring Centre. - The permanent thermal infrared surveillance network (TIRNet) is composed of 6 stations distributed among Campi Flegrei and Vesuvius. The stations acquire IR scenes at night-time of highly diffuse degassing areas. IR data are processed by an automated system of IR analysis and the temperatures values are sent to the Monitoring CentrePublishedVienna, Austria1IT. Reti di monitoraggio e sorveglianz