New Generation Platforms for Exploration of Crowdsourced Geo-Data

Brovelli, Maria Antonia; Minghini, Marco; Zamboni, Giorgio

doi:10.1007/978-3-319-65633-5_9

Cited by 7 publications

(6 citation statements)

References 45 publications

(42 reference statements)

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“…Similarly, there is a large and dynamic network of observation technologies, and institutions and agencies that also benefit from, and drive, advances and disruptions. Examples of disruptive technological advances in the field of ocean observation and more widely include NOAA's Big Data Project 2 ; EO Data Cubes and Analysis Ready Datasets (Giuliani et al, 2017;Nativi et al, 2017), crowd-sourcing and citizen science (Mazumdar et al, 2017;Brovelli et al, 2018;O'Sullivan et al, 2018) and deep learning and artificial intelligence (Lary et al, 2018). Related to this is the way that Big Data has changed the paradigm when it comes to the availability and exploitation of ocean observing data.…”

Section: The Need For Innovation and Usable Informationmentioning

confidence: 99%

The Role of Stakeholders in Creating Societal Value From Coastal and Ocean Observations

et al. 2019

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The importance of stakeholder engagement in ocean observation and in particular the realization of economic and societal benefits is discussed, introducing a number of overarching principles such as the convergence on common goals, effective communication, co-production of information and knowledge and the need for innovation. A series of case studies examine the role of coordinating frameworks such as the United States' Interagency Ocean Observing System (IOOS R), and the European Ocean Observing System (EOOS), public-private partnerships such as Project Azul and the Coastal Data Information Program (CDIP) and finally the role of the "third" or voluntary sector. The paper explores the value that stakeholder engagement can bring as well as making recommendations for the future.

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Section: The Need For Innovation and Usable Informationmentioning

confidence: 99%

The Role of Stakeholders in Creating Societal Value From Coastal and Ocean Observations

et al. 2019

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“…X3DOM [19], OSG.js [20], O3D [21], WebGL Earth [22], WebGL Globe [23], etc. to visualize 2D/3D features and different geospatial capabilities [24,25]. They gained interest by a wider audience of both the consumer and the scientific community.…”

Section: Related Research and Current Trendsmentioning

confidence: 99%

Design and Implementation of a 4D Web Application for Analytical Visualization of Smart City Applications

Murshed

Al-Hyari

Wendel

et al. 2018

IJGI

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Contemporary development of computer hardware and software, WebGIS and geo-web services as well as the availability of semantic 3D city models, facilitate flexible and dynamic implementation of web applications. The aim of this paper is to introduce 4D CANVAS, a web-based application for dynamic visualization of 3D geospatial data for improved decision making in smart city applications. It is based on the Cesium Virtual Globe, an open-source JavaScript library developed with HTML5 and WebGL. At first, different data formats such as JSON, GeoJSON, Cesium Markup Language (CZML) and 3D Tiles are evaluated for their suitability in 4D visualization applications. Then, an interactive Graphical User Interface (GUI) is built observing the principle of cartographic standards to view, manage, understand and explore different simulation outputs at multiple spatial (3D surface of buildings) and temporal (hourly, daily, monthly) resolutions. In this regard, multiple tools such as aggregation, data classification, etc. are developed utilizing JavaScript libraries. As a proof of concept, two energy simulations and their outputs of different spatial and temporal resolutions are demonstrated in five Asian and European cities. Finally, the 4D CANVAS is deployed both in desktop and multi-touch screens. The proposed application allows easy integration of any other geospatial simulation results, thereby helps the users from different sectors to explore them interactively in 4D.

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“…Accurate GNSS, along with a trend in the direction of lighter, less-expensive and metrically more accurate sensors, produces high-volumes of geospatial data. Crowd-sourcing solutions and sensors distributed in smart cities create and use large volumes of spatial data [1]. Datasets with unstructured points are a common direct or indirect output from such technologies.…”

Section: Introductionmentioning

confidence: 99%

Implementation and assessment of two density-based outlier detection methods over large spatial point clouds

Pirotti

Ravanelli

Fissore

et al. 2018

Open geospatial data, softw. stand.

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Several technologies provide datasets consisting of a large number of spatial points, commonly referred to as point-clouds. These point datasets provide spatial information regarding the phenomenon that is to be investigated, adding value through knowledge of forms and spatial relationships. Accurate methods for automatic outlier detection is a key step. In this note we use a completely open-source workflow to assess two outlier detection methods, statistical outlier removal (SOR) filter and local outlier factor (LOF) filter. The latter was implemented ex-novo for this work using the Point Cloud Library (PCL) environment. Source code is available in a GitHub repository for inclusion in PCL builds. Two very different spatial point datasets are used for accuracy assessment. One is obtained from dense image matching of a photogrammetric survey (SfM) and the other from floating car data (FCD) coming from a smartcity mobility framework providing a position every second of two public transportation bus tracks. Outliers were simulated in the SfM dataset, and manually detected and selected in the FCD dataset. Simulation in SfM was carried out in order to create a controlled set with two classes of outliers: clustered points (up to 30 points per cluster) and isolated points, in both cases at random distances from the other points. Optimal number of nearest neighbours (KNN) and optimal thresholds of SOR and LOF values were defined using area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Absolute differences from median values of LOF and SOR (defined as LOF2 and SOR2) were also tested as metrics for detecting outliers, and optimal thresholds defined through AUC of ROC curves. Results show a strong dependency on the point distribution in the dataset and in the local density fluctuations. In SfM dataset the LOF2 and SOR2 methods performed best, with an optimal KNN value of 60; LOF2 approach gave a slightly better result if considering clustered outliers (true positive rate: LOF2 = 59.7% SOR2 = 53%). For FCD, SOR with low KNN values performed better for one of the two bus tracks, and LOF with high KNN values for the other; these differences are due to very different local point density. We conclude that choice of outlier detection algorithm very much depends on characteristic of the dataset's point distribution, no one-solution-fits-all. Conclusions provide some information of what characteristics of the datasets can help to choose the optimal method and KNN values.

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New Generation Platforms for Exploration of Crowdsourced Geo-Data

Cited by 7 publications

References 45 publications

The Role of Stakeholders in Creating Societal Value From Coastal and Ocean Observations

The Role of Stakeholders in Creating Societal Value From Coastal and Ocean Observations

Design and Implementation of a 4D Web Application for Analytical Visualization of Smart City Applications

Implementation and assessment of two density-based outlier detection methods over large spatial point clouds

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