Close range mini Uavs photogrammetry for architecture survey

The survey of historical façades contains several bottlenecks, mainly related to the geometrical structure, the decorative framework, the presence of natural or artificial obstacles, the environment limitations. Urban context presents additional restrictions, binding by ground acquisition activity and leading to building data loss. The integration of TLS and close-range photogrammetry allows to go over such stuff, not overcoming the shadows effect due to the ground point of view. In the last year the massive use of UAVs in survey activity has permitted to enlarge survey capabilities, reaching a deeper knowledge in the architecture analysis. In the meanwhile, several behaviour rules have been introduced in different countries, regulating the UAVs use in different field, strongly restricting their application in urban areas. Recently very small and light platforms have been presented, which can partially overcome these rules restrictions, opening to very interesting future scenarios. This article presents the application of one of these very small RPAS (less than 300 g), equipped with a low-cost camera, in a close range photogrammetric survey of an historical building façade in Bologna (Italy). The suggested analysis tries to point out the system accuracy and details acquisition capacity. The final aim of the paper is to validate the application of this new platform in an architectonic survey pipeline, widening the future application of close-range photogrammetry in the architecture acquisition process

A deep representation for depth images from synthetic data

Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach

Dalla molteplicità alla meta: l'Errante, la Scomposizione, la Durata

1nonenoneRUSSO F.Russo, Fabi

From source to target and back: symmetric bi-directional adaptive GAN

The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled images can be modified to mimic target samples making it possible to train directly a classifier in the target domain, despite the original lack of annotated data. Inverse mappings from the target to the source domain have also been evaluated but only passing through adapted feature spaces, thus without new image generation. In this paper we propose to better exploit the potential of generative adversarial networks for adaptation by introducing a novel symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. Moreover we define a new class consistency loss that aligns the generators in the two directions imposing to conserve the class identity of an image passing through both domain mappings. A detailed qualitative and quantitative analysis of the reconstructed images confirm the power of our approach. By integrating the two domain specific classifiers obtained with our bi-directional network we exceed previous state-of-the-art unsupervised adaptation results on four different benchmark datasets

Inter-municipal Co-operation: the Managerial Perspective of Local Authorities

The paper aims at contributing to the body of knowledge referred to Public Administrations co-operation. In particular, the research is focused on Local Public Administration (LPA), intensely influenced by the global economic crisis. The study regards to how LPAs could reach more efficiency and effectiveness in providing services to its final users (citizen, companies and other PAs), as well as provide new services, especially on the cooperation among LPAs, called inter- LPA cooperation (ILPAC). Having analysed LPA paradigm, it has been possible to isolate some relevant trends characterizing LPA and some open scientific literature gaps about ILPAC: nowadays, in ILPAC phenomenon, some weak points can be highlighted, especially in start-up and in management phases. Consequently, in collaboration with the eGovernment Observatory of the Milan University of Technology, the research has inquired the reasons that lead to activate an ILPAC and develop a decision making framework for the governance of shared functions in the LPA. Particularly, it has focused on the identification of LPA environmental reasons and LPA proper characteristics pushing LPA to activate an ILPAC of its fundamental functions. Once identified these elements, it has tried to identify the organizational and managerial configurations adopted for ILPAC to manage shared functions. The study has implied the use of several instruments in order to investigate ILPAC phases, from their founding to ordinary management in the Italian context. Results have been analysed using statistical methods, in order to come to light some peculiarities already pointed out by the descriptive examination. In addition, linear regression has been set in order to inquire into ILPAC performances, compared to autonomous municipalities. Using this methodology, the analysis has pointed out some important suggestions pertaining to ILPAC management. Primary considerations has shown the effect of regional different governances that impact on the amount and the dimension of ILPACs in their territories. Secondly, associated municipalities obtain better performances than independent bodies, for instance, considering the One-Stop-Shop proceedings. In addition, linear regression has proven that ILPACs produce benefits in a wider context: the analysis has pointed out some important suggestions pertaining to ILPAC management, as organizational performances increase when the number of associated municipalities increase or both proceeding costs and time improve when large ILPACs formalize and clearly declare their objectives

The alien spreading of Chama pacifica Broderip, 1835 (Mollusca: Bivalvia: Chamidae) in the Mediterranean Sea

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Emotional Reactions to the Perception of Risk in the Pompeii Archaeological Park

The assessment of perceived risk by people is extremely important for safety and security management. Each person is based on the opinion of others to make a choice and the Internet represents the place where these opinions are mostly researched, found and reviewed. Social networks have a decisive impact: 92% of consumers say they have more trust in social media reviews than in any other form of advertising. For this reason, Opinion Mining and Sentiment Analysis have found interesting applications in the most diverse context, among which the most innovative is certainly represented by public safety and security. Security managers can use the perceptions expressed by people to discover the unexpected and potential weaknesses of a controlled environment or otherwise the risk and security perception of people that sometimes can be very different from real level of risk and security of a given site. Since the perceptions are the result of mostly unconscious elaborations, it is necessary to go deeper and to search for the emotions, triggered by the sensorial stimuli, that determine them. The objective of this paper is to study the perception of risk within the Pompeii Archaeological Park, giving emphasis to the emotional components, using the semantic analysis of the textual contents present in Twitter.Peer reviewe