Clab torino: A transdisciplinary environment to provide a challenge-based teaching model

Promoting an open dialogue, a constant interdisciplinary collaboration with companies, between universities, about partnership or open innovation perspective, today is a challenge that still faces some resistance. Learning to deal with complexity, with the coexistence of different points of view, in collaboration to combined and re-combined know-how in ever new, original and challenging formulations, brings with its specific needs. In this sense, design takes on a fundamental role to create projects with a view to sustainable innovation, projects that are increasingly responsive to contemporary complexity. So, how does design education need to change? How do working designers and design researchers can update their skills to meet the challenges of the present and future? This contribution, through the experimentation of the Contamination Lab Torino, investigates a new design-driven educational model intended as an extremely dynamic process from the creation of a multidisciplinary team to the transition from a product design logic to a Product Service System one, as the most effective way to face the issue of the system management, as a way to guarantee the appropriate flexibility to the contemporary needs of our society

TERRESTRIAL 3D MAPPING OF FORESTS: GEOREFERENCING CHALLENGES AND SENSORS COMPARISONS

Terrestrial 3D reconstruction is a research topic that has recently received significant attention in the forestry sector. This practice enables the acquisition of high-quality 3D data, which can be used not only to derive physical forest criteria such as tree positions and diameters, but also more detailed analyses related to ecological parameters such as habitat availability and biomass. However, several challenges must be addressed before fully integrating this technology into forestry practices. The primary challenge is accurately georeferencing surveyed 3D data acquired in the same location and placing them into a national projection reference system. Unfortunately, due to the forest canopy, the GNSS signal is often obstructed, and it cannot guarantee sub-meter accuracy. In this paper, we have implemented an indirect georeferencing methodology based on spheres with known coordinates placed at the forest’s edge where GNSS reception was more reliable and accurate than under the canopy. We evaluated its performance through three analyses that confirmed the validity of our approach. Indeed, the accuracy of the TLS point cloud, georeferenced using our method, is within a centimetre level (4.7 cm), whereas mobile scanning methods demonstrate accuracy within the decimetre range but still less than a metre. Additionally, we have initiated the analysis of a potential future application for mixed reality headsets, which could enable real-time acquisition and visualisation of 3D data

EVALUATING MONOCULAR DEPTH ESTIMATION METHODS

Depth estimation from monocular images has become a prominent focus in photogrammetry and computer vision research. Monocular Depth Estimation (MDE), which involves determining depth from a single RGB image, offers numerous advantages, including applications in simultaneous localization and mapping (SLAM), scene comprehension, 3D modeling, robotics, and autonomous driving. Depth information retrieval becomes especially crucial in situations where other sources like stereo images, optical flow, or point clouds are not available. In contrast to traditional stereo or multi-view methods, MDE techniques require fewer computational resources and smaller datasets. This research work presents a comprehensive analysis and evaluation of some state-of-the-art MDE methods, considering their ability to infer depth information in terrestrial images. The evaluation includes quantitative assessments using ground truth data, including 3D analyses and inference time

BENCHMARKING THE EXTRACTION OF 3D GEOMETRY FROM UAV IMAGES WITH DEEP LEARNING METHODS

3D reconstruction from single and multi-view stereo images is still an open research topic, despite the high number of solutions proposed in the last decades. The surge of deep learning methods has then stimulated the development of new methods using monocular (MDE, Monocular Depth Estimation), stereoscopic and Multi-View Stereo (MVS) 3D reconstruction, showing promising results, often comparable to or even better than traditional methods. The more recent development of NeRF (Neural Radial Fields) has further triggered the interest for this kind of solution. Most of the proposed approaches, however, focus on terrestrial applications (e.g., autonomous driving or small artefacts 3D reconstructions), while airborne and UAV acquisitions are often overlooked. The recent introduction of new datasets, such as UseGeo has, therefore, given the opportunity to assess how state-of-the-art MDE, MVS and NeRF 3D reconstruction algorithms perform using airborne UAV images, allowing their comparison with LiDAR ground truth. This paper aims to present the results achieved by two MDE, two MVS and two NeRF approaches levering deep learning approaches, trained and tested using the UseGeo dataset. This work allows the comparison with a ground truth showing the current state of the art of these solutions and providing useful indications for their future development and improvement

Deep domain adaptation by weighted entropy minimization for the classification of aerial images

Fully convolutional neural networks (FCN) are successfully used for the automated pixel-wise classification of aerial images and possibly additional data. However, they require many labelled training samples to perform well. One approach addressing this issue is semi-supervised domain adaptation (SSDA). Here, labelled training samples from a source domain and unlabelled samples from a target domain are used jointly to obtain a target domain classifier, without requiring any labelled samples from the target domain. In this paper, a two-step approach for SSDA is proposed. The first step corresponds to a supervised training on the source domain, making use of strong data augmentation to increase the initial performance on the target domain. Secondly, the model is adapted by entropy minimization using a novel weighting strategy. The approach is evaluated on the basis of five domains, corresponding to five cities. Several training variants and adaptation scenarios are tested, indicating that proper data augmentation can already improve the initial target domain performance significantly resulting in an average overall accuracy of 77.5%. The weighted entropy minimization improves the overall accuracy on the target domains in 19 out of 20 scenarios on average by 1.8%. In all experiments a novel FCN architecture is used that yields results comparable to those of the best-performing models on the ISPRS labelling challenge while having an order of magnitude fewer parameters than commonly used FCNs. © 2020 Copernicus GmbH. All rights reserved

Using semantically paired images to improve domain adaptation for the semantic segmentation of aerial images

Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new input instances. This is a particular significant problem for tasks involving aerial images: Often training data is only available for a limited geographical area and a narrow time window, thus leading to models which perform poorly in different regions, at different times of day, or during different seasons. Domain adaptation can mitigate this issue by using labelled source domain training examples and unlabeled target domain images to train a model which performs well on both domains. Modern adversarial domain adaptation approaches use unpaired data. We propose using pairs of semantically similar images, i.e., whose segmentations are accurate predictions of each other, for improved model performance. In this paper we show that, as an upper limit based on ground truth, using semantically paired aerial images during training almost always increases model performance with an average improvement of 4.2% accuracy and .036 mean intersection-over-union (mIoU). Using a practical estimate of semantic similarity, we still achieve improvements in more than half of all cases, with average improvements of 2.5% accuracy and .017 mIoU in those cases. © 2020 Copernicus GmbH. All rights reserved

Creating multi-temporal maps of urban environments of improved localization of autonomous vehicles

The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics. In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Qualification of the LHC Corrector Magnet Production with the CERN-built Measurement Benches

The LHC will incorporate about 7600 superconducting single aperture corrector magnets mounted in the main magnet cold masses. In order to follow up their production, we have designed and built 12 different benches for warm magnetic measurements based on rotating coils. Each bench was manufactured in two copies, one installed at the industry sites and the other kept at CERN for cross checks and monitoring of the measurement quality. These systems measure the main field, the field quality and the position and orientation of the field relative to the mechanical construction, all properties that are required for an effective use of the magnets. After calibration, the benches automatically refer the measured quantities to the mechanical interfaces used to align the correctors in the cold masses (pin holes or keys). In this paper we evaluate the global uncertainty achieved with the benches and compare the field measurements performed at room temperature in industry with measurements at 1.9 K performed at CERN on samples of each corrector type