A GEO-DATABASE FOR 3D-AIDED MULTI-EPOCH DOCUMENTATION OF BRIDGE INSPECTIONS

The recent collapse of bridges in Italy has prompted numerous studies on monitoring and maintenance. Many structures in Italy have been in service for over 50 years, necessitating new approaches to ensure their safety. To address this issue, Italy's Consiglio Superiore dei Lavori Pubblici (Superior Council of Public Works) has developed the Guidelines for Risk Classification and Management, proposing a multi-level approach to bridge management within a complex geomorphological environment. The guidelines outline a multi-level process that includes surveying the structures, conducting detailed inspections, and assigning risk classes based on hazard, exposure, and vulnerability. Current inspection processes are time-consuming and costly. Therefore, alternative monitoring technologies are crucial. Unmanned aerial vehicles equipped with cameras, laser technologies, and GPS systems offer flexible and cost-effective solutions for visual inspection. These technologies enable the collection of both quantitative and qualitative data, such as size, material properties, and overall condition. In this context, efficient data management and exploration systems are necessary to handle the vast amount of geo-referenced information. Multi-epoch databases play a crucial role in documenting the conditions of bridges and supporting a maintenance and structural health monitoring workflow. These databases can be utilized within a Bridge Management System to aid road managers in decision-making processes. Additionally, 3D exploration platforms provide visual analysis and highlight areas of interest within the structure. This work presents a multi-epoch geo-database that adheres to the Italian guidelines, offering optimized data management and queryability for 2D and 3D information. The entire process is designed using open-source and reproducible solutions

Detección de indicadores de seguridad de pacientes (PSI-Patient Safety Indicators) en un estudio multi-céntrico de carga de enfermedad y resultados de la atención

La calidad/ seguridad de la atención médica se puede estimar analizando los registros de egresos de hospitales generales de agudos (HGA). Se obtuvieron indicadores de seguridad de los pacientes (PSI), que detectan eventos adversos en la atención médica (EAs). En un estudio multi-céntrico se adecuó un método para obtener los PSI en la Argentina basado en codificaciones diagnosticas primarias (Dx1) y secundarias (Dx2) y codificaciones de procedimientos (Px1 y Px 2). La estandarización de los diagnósticos y de los procedimientos permitió realizar la plataforma del estudio Utilización de Servicios, Costos y Resultados en Argentina (USCR-A). Se obtienen los EAs definidos por los PSI #3, #7 y #13. El objetivo de este trabajo es presentar los resultados cuali y cuantitativos de una aplicación de los PSI en el contexto del trabajo y registro del estudio multi-céntrico en Utilización de Servicios, Costos y Resultados en Argentina (USCR-A).Sociedad Argentina de Informática e Investigación Operativ

The Legacy of Sycamore Gap: The Potential of Photogrammetric AI for Reverse Engineering Lost Heritage with Crowdsourced Data

\ua9 Author(s) 2024.The orientation of crowdsourced and multi-temporal image datasets presents a challenging task for traditional photogrammetry. Indeed, traditional image matching approaches often struggle to find accurate and reliable tie points in images that appear significantly different from one another. In this paper, in order to preserve the memory of the Sycamore Gap tree, a symbol of Hadrian\u27s Wall that was felled in an act of vandalism in September 2023, deep-learning-based features trained specifically on challenging image datasets were employed to overcome limitations of traditional matching approaches. We demonstrate how unordered crowdsourced images and UAV videos can be oriented and used for 3D reconstruction purposes, together with a recently acquired terrestrial laser scanner point cloud for scaling and referencing. This allows the memory of the Sycamore Gap tree to live on and exhibits the potential of photogrammetric AI (Artificial Intelligence) for reverse engineering lost heritage

A REPLICABLE OPEN-SOURCE MULTI-CAMERA SYSTEM FOR LOW-COST 4D GLACIER MONITORING

Image-based monitoring has emerged as a prevalent technique for sensing mountain environments. Monoscopic time-lapse cameras, which rely on digital image correlation to quantify glacier motion, have limitations due to the need for a Digital Elevation Model for deriving 3D flow velocity fields. Multi-camera systems overcome this limitation, as they allow for a 3D reconstruction of the scene. This paper presents a replicable low-cost stereoscopic system designed for 4D glacier monitoring. The system consists of independent and autonomous units, built from off-the-shelves components, such as a DSLR camera, an Arduino microcontroller, and a Raspberry Pi Zero, reducing costs compared to pre-built time-lapse cameras. The units are energetically self-sufficient and resistant to harsh alpine conditions. The system was successfully tested for more than a year to monitor the northwest terminus of the Belvedere Glacier (Italian Alps). Daily stereo-pairs acquired were processed with Structure-from-Motion to derive 3D point clouds of the glacier terminus and estimate glacier retreat and ice volume loss. By combining the information about ice volume loss with ablation estimates and ice flow velocity information, e.g., derived from monoscopic-camera time series, a multi-camera system enables a comprehensive understanding of sub-seasonal glacier dynamics