Cross-Scale, Multi-Scale, and Multi-Source Data Visualization and Analysis Issues and Opportunities

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.

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BIM SYSTEM FOR THE CONSERVATION AND PRESERVATION OF THE MOSAICS OF SAN MARCO IN VENICE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w5-229-2017 ◽

2017 ◽

Vol XLII-2/W5 ◽

pp. 229-236 ◽

Cited By ~ 4

Author(s):

F. Fassi ◽

L. Fregonese ◽

A. Adami ◽

F. Rechichi

Keyword(s):

Geometric Model ◽

Three Dimensional ◽

Point Clouds ◽

Fundamental Principle ◽

Single Element ◽

Efficient System ◽

Multi Scale ◽

Source Data ◽

Entire Complex ◽

The Church

The Basilica of San Marco in Venice is a well-known masterpiece of World Heritage. It is a real multi-faceted architecture. The management of the church and its construction site is very complicated, and requires an efficient system to collect and manage different kinds of data. The BIM approach appeared to be the most suitable to collect multi-source data, to monitor activities and guarantee the well-timed operations inside the church. The purpose of this research was to build a BIM of the Basilica, considering all aspects that characterize it and that require particular care. Many problems affected the phase of the acquisition of data, and forced the team to establish a clear working pipeline that allowed the survey simultaneously, hand in hand, with all the usual activities of the church. The fundamental principle for the organization of the whole work was the subdivision of the entire complex in smaller parts, which could be managed independently, both in the acquisition and the modelling stage. This subdivision also reflects the method used for the photogrammetric acquisition. The complexity of some elements, as capitals and statues, was acquired with different Level of Detail (LoD) using various photogrammetric acquisitions: from the most general ones to describe the space, to the most detailed one 1:1 scale renderings. In this way, different LoD point clouds correspond to different areas or details. As evident, this pipeline allows to work in a more efficient way during the survey stage, but it involves more difficulties in the modelling stage. Because of the complexity of the church and the presence of sculptural elements represented by a mesh, from the beginning the problem of the amount of data was evident: it is nonsense to manage all models in a single file. The challenging aspect of the research job was the precise requirement of the Procuratoria di San Marco: to obtain the 1:1 representation of all the mosaics of the Basilica. This requirement significantly increased the effort in the acquisition stage, because it was necessary to reach a submillimetre resolution in the photographic images sufficient to distinguish perfectly each single tessera, also in the highest domes (28 meters). Furthermore, it introduced a new problem about the management of the gigapixel - orthophotos. The BIM approach presented in this paper tries to offer a solution to all these problems. The BIM application is based not on commercial software, but on a self-implemented system, which was previously tested on the Main Spire of Milano Cathedral. The multi-scale and multi-area approach have also been maintained in the BIM construction phase. In the case of Basilica di San Marco, the most important requirement was the management of the orthophotos of each single element. It was necessary to give the user the possibility to recover, for each item, not only the geometric model, but also the raster representation -orthophoto- of its surface: in order to do it, the BIM model acts as a three-dimensional catalogue.

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A Global Seamless DEM Based on Multi-Source Data Fusion (GSDEM-30): Product Generation and Evaluation

10.20944/preprints201906.0036.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Linwei Yue ◽

Huanfeng Shen ◽

Lu Liu ◽

Qiangqiang Yuan ◽

Liangpei Zhang

Keyword(s):

Data Fusion ◽

Comprehensive Evaluation ◽

Maximum Slope ◽

Multi Scale ◽

Aster Gdem ◽

Multiple Datasets ◽

National Elevation Dataset ◽

Digital Elevation ◽

Source Data

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

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Multi-scale data visualization for computational astrophysics and climate dynamics at Oak Ridge National Laboratory

Journal of Physics Conference Series ◽

10.1088/1742-6596/46/1/076 ◽

2006 ◽

Vol 46 ◽

pp. 550-555 ◽

Cited By ~ 1

Author(s):

Sean Ahern ◽

Jamison R Daniel ◽

Jinzhu Gao ◽

George Ostrouchov ◽

Ross J Toedte ◽

...

Keyword(s):

Data Visualization ◽

National Laboratory ◽

Climate Dynamics ◽

Oak Ridge ◽

Multi Scale ◽

Computational Astrophysics ◽

Oak Ridge National Laboratory ◽

Scale Data

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An interactive web-based tool for multi-scale physiological data visualization

Computers in Cardiology, 2004 ◽

10.1109/cic.2004.1443001 ◽

2005 ◽

Cited By ~ 1

Author(s):

M. Oefinger ◽

W. Zong ◽

M. Krieger ◽

R.G. Mark

Keyword(s):

Data Visualization ◽

Physiological Data ◽

Web Based ◽

Multi Scale

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Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization

10.31219/osf.io/6evmb ◽

2021 ◽

Author(s):

Sehi L'Yi ◽

Qianwen Wang ◽

Fritz Lekschas ◽

Nils Gehlenborg

Keyword(s):

Data Visualization ◽

Data Types ◽

Interactive Rendering ◽

Web Based ◽

Multi Scale ◽

Data Formats ◽

Wide Range ◽

Visualization Techniques ◽

Diverse Data ◽

Data Visualizations

The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling—a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js and its source code are available at https://gosling.js.org.

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