Stefano Zorzi

Spatio-temporal InSAR for bridge monitoring: Integrating physics-based models with persistent scatterer interferometry

Alessandro Lotti, Stefano Zorzi, Enrico Tubaldi, Alfredo Rocca et autres

This work presents a novel spatio-temporal InSAR (ST-InSAR) framework that integrates physics-based structural models into persistent scatterer interferometry (PSI) for bridge monitoring. The proposed approach mitigates the limitations of conventional PSI in civil infrastructure applications, namely large displacements that exceed the ambiguity …

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A Simplified Approach for Multi-Hazard Seismic Risk Assessment of Bridges

Nadia Salvatore, Enrico Tubaldi, Alessandro Pagliaroli, Stefano Zorzi et autres

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Test accuracy requirements for optimal decision-making in management of post-tensioned concrete bridges

Stefano Zorzi, Francesco Rossi, Enrico Tubaldi, John Quigley et autres

The safety and reliability assessment of post-tensioned (PT) concrete bridges is critical to the management of the infrastructure. Assessing the structural health condition of PT concrete bridges is challenging due to the inaccessibility of prestressing systems. In combination with visual inspections, engineers …

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Bayesian Approach for Main Frequency Identification on Extremely Under-Sampled Signals

Chiara Nardin, Stefano Zorzi, Federica Zonzini, Daniele Zonta et autres

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Reliability-Based Quality Assessment of Monitoring Systems Using Bayesian Networks

Stefano Zorzi, Marco Broccardo, Daniele Zonta

it (code pays fourni par la source)

Reliability-based metrics for structural health monitoring information quality assessment

Stefano Zorzi, Marco Broccardo, Daniel Tonelli, Daniele Zonta

One of the primary challenges in civil engineering today is the safety assessment and management of existing infrastructures. In this context, structural health monitoring (SHM) plays a fundamental role in data acquisition and assessment of the safety of a given infrastructure. SHM …

it (code pays fourni par la source)

Re:PolyWorld - A Graph Neural Network for Polygonal Scene Parsing

Stefano Zorzi, Friedrich Fraundorfer

While most state-of-the-art instance segmentation methods produce pixel-wise segmentation masks, numerous applications demand precise vector polygons of detected objects instead of rasterized output. This paper proposes Re:PolyWorld as a remastered and improved version of PolyWorld, a neural network that extracts object vertices …

at (code pays fourni par la source)

Deep Learning for the Automatic Division of Building Constructions Into Sections on Remote Sensing Images

Philipp Schuegraf, Stefano Zorzi, Friedrich Fraundorfer, Ksenia Bittner

Urban areas predominantly consist of complex building structures, that are assembled of multiple building sections. From very high resolution (VHR) remote sensing imagery, not only roof-tops but also the separation lines between them are visible. Since fully convolutional neural network (FCN)-based methods …

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PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images

Stefano Zorzi, Shabab Bazrafkan, Stefan Habenschuss, Friedrich Fraundorfer

While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output. This paper introduces PolyWorld, a neural network that directly extracts building vertices from an image and …

at (code pays fourni par la source)

RegGAN: An End-to-End Network for Building Footprint Generation with Boundary Regularization

Qingyu Li, Stefano Zorzi, Yilei Shi, Friedrich Fraundorfer et autres

Accurate and reliable building footprint maps are of great interest in many applications, e.g., urban monitoring, 3D building modeling, and geographical database updating. When compared to traditional methods, the deep-learning-based semantic segmentation networks have largely boosted the performance of building footprint generation. …

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End-to-End Semantic Segmentation and Boundary Regularization of Buildings from Satellite Imagery

Qingyu Li, Stefano Zorzi, Yilei Shi, Friedrich Fraundorfer et autres

Building footprint generation is a vital task of satellite imagery interpretation. However, the segmentation masks of buildings obtained by existing semantic segmentation networks often have blurred boundaries and irregular shapes. In this research, we propose a new boundary regularization network for building …

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Machine-learned 3D Building Vectorization from Satellite Imagery

Yi Tong Wang, Stefano Zorzi, Ksenia Bittner

We propose a machine learning based approach for automatic 3D building reconstruction and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) and a panchromatic (PAN) image as input, we first filter out non-building objects and refine the building shapes of the …

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