ROAd Digital Sustainable Twins in Emilia-Romagna

ROADSTER aims at investigating and developing new state-of-the-art AI solutions in Computer Vision and Deep Learning for assessing a new framework of “Digital and Sustainable Twin” of the complex ecosystem of roads and transportation facilities in industrial areas. The proposal is very innovative as it shifts the focus from the classical “Smart city” environment to that of industrial production areas, which are very critical for our territory in terms of traffic intensity, pollution, and workers' safety.

The goal is to provide new data, processed online and off-line with new Italian Services regarding a) road conditions and anomalies around industrial sites, b) personalized services about traffic conditions for optimizing transport scheduling and c) the improvement of workers safety during their journeys to/from the workplace.

ROASTER has two key aspects: it aims to provide valuable scientific results in AI and will be a proof-of-concept of a very ambitious and largely scalable project to create an Italian answer in the management of mobility data, devoted to the support of industry production and of the ecological transition.

Presentation of the results

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Publications

1	Bruno, Paolo; Amoroso, Roberto; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita "Investigating Bidimensional Downsampling in Vision Transformer Models" Proceedings of the 21st International Conference on Image Analysis and Processing, vol. 13232, Lecce, Italy, pp. 287 -299 , 23 - 27 May 2022, 2022 | DOI: 10.1007/978-3-031-06430-2_24 Conference
2	Amoroso, Roberto; Baraldi, Lorenzo; Cucchiara, Rita "Improving Indoor Semantic Segmentation with Boundary-level Objectives" Proceedings of the 16th International Work-conference on Artificial Neural Networks, vol. 12862, Online, pp. 318 -329 , June 16-18, 2021, 2021 | DOI: 10.1007/978-3-030-85099-9_26 Conference
3	Amoroso, Roberto; Baraldi, Lorenzo; Cucchiara, Rita "Assessing the Role of Boundary-level Objectives in Indoor Semantic Segmentation" COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2021, PT 1, vol. 13052 LNCS, Virtual, pp. 455 -465 , 27 September - 01 October 2021, 2021 | DOI: 10.1007/978-3-030-89128-2_44 Conference