Visualizing subterranean infrastructure with Augmented Reality

 

The SIM application (Subterranean Infrastructure Map App and Service) is developed to ease construction and digging projects by visualizing underground infrastructure with augmented reality.

Augmented reality (AR) is a live direct or indirect view of a physical, real-world environment whose elements are augmented (or supplemented) by computer-generated sensory input such as sound, video, graphics or GPS data[1]. The applications EVRY develop uses augmented reality technology to present cadastral data which is distributed by the proDataMarket platform. With a connection to proDataMarket, SIM downloads subterranean infrastructure data that exists at the user location. This data is then used to visualize the underground grid of pipes and cables as well as give information about the pipe or cable. If there are a lot of pipes in a given area there could potentially be too much information to augment at a time. The user can then filter out pipe groups (such as water, sewage, electricity) to be able to get a more relevant view.

Picture1

Relevant information could be a pipes depth, the pipes owner as well as the age and material of the pipe. An issue with data like this is that it is often private. Data are also often owned by different actors, and a challenge is to give them incentive to share their data.

Picture2

One of the major technical challenges the development team have been facing, is the lack of accuracy on mobile devices. The GPS receivers and built-in compass on mobile devices are not accurate enough to give an exactly correct representation of the pipe grid. It is possible however to increase the GPS accuracy by using an external GPS receiver. But even though the GPS is correct, a small error with the heading will still create unwanted results. In addition to positioning, another challenge is the data quality in a given area. To create a good augmented reality experience, the framework needs to know the height above mean sea level. This is not always given information in the data set.

 

To accommodate these challenges, SIM has a calibration functionality that can “move” the pipe grid according to a given heading. It also has a call to “Google Elevation Service” to get the pipe grids height so that it does not rely on elevation data. If the augmented experience is still not sufficient, SIM also includes a 2d Map so the user may get an overview of the pipe grid

Picture3

If the user for some reason does not want to use the device camera (i.e. poor lightning conditions, broken lens etc.) or does not want to relocate to see the pipe grid, a Google Street View module is also implemented. This is a regular Google street view, with the pipe grid integrated so the user can stay at one location and see the pipe grid at another location.

 

[1] https://en.wikipedia.org/wiki/Augmented_reality

 

New paper: Towards a Reference Architecture for Trusted Data Marketplaces

Towards a Reference Architecture for Trusted Data Marketplaces by Dumitru Roman and Stefano Gatti. 2nd International Conference on Open and Big Data, 2016.

  • Abstract: Data sharing presents extensive opportunities and challenges in domains such as the public sector, health care and financial services. This paper introduces the concept of “trusted data marketplaces” as a mechanism for enabling trusted sharing of data. It takes credit scoring—an essential mechanism of the entire world-economic environment, determining access for companies and individuals to credit and the terms under which credit is provisioned—as an example for the realization of the trusted data marketplaces concept. This paper looks at credit scoring from a data perspective, analyzing current shortcomings in the use and sharing of data for credit scoring, and outlining a conceptual framework in terms of a trusted data marketplace to overcome the identified shortcomings. The contribution of this paper is two-fold: (1) identify and discuss the core data issues that hinder innovation in credit scoring; (2) propose a conceptual architecture for trusted data marketplaces for credit scoring in order to serve as a reference architecture for the implementation of future credit scoring systems. The architecture is generic and can be adopted in other domains where data sharing is of high relevance.
  • Download paper

 

Data Workflow in SoE

The datasets and challenges in integration

The State of Estate (SoE) business case focuses on generating an up-to-date, dynamic and high quality report on State-owned properties and buildings in Norway. It collects and integrates several datasets as listed below. The datasets are originated from heterogeneous sources and of different quality. Here are some scenarios that will cause challenges in the integration process.

Matrikkel data

Though Matrikkel data from the Norwegian mapping authority is one the most authoritative source of property data, not all the information is up to date. It could be sometimes caused by the delay of administrative procedure in municipalities, and sometimes the owners don’t report change to the municipalities because of the high cost to report the change, and sometimes it could be typos and some other manual updating errors. The buildings less than 15 square meters are not required to be registered in the Matrikkel.

Statsbygg’s property data

The Statsbygg’s property data is updated since the last report. However, the Matrikkel’s building number is not correctly registered on all the buildings. The address information is not necessarily updated either. It could be also be typos and some other manual updating errors in the dataset.

Business Entity register

The Business Entity register dataset is from another national authoritative source with information of ministries and their subordinate organizations. However, not all the subordinate organizations of the ministries are registered as a sub-organization in the Business Entity register. The missing organizations need to be added manually as extra business entities to the dataset.

State-owned properties Report 2013-2014 (SoEReport2013)

The SoEReport2013 is a report from 2013 and it includes some properties or buildings that could be sold, rebuilt, demolished in the last few years. The old report also includes some non-reported ownership of properties and buildings in the government that we need to take care of in the new report. For example several properties were registered as owned by Statsbygg in the old report; however, they are registered as owned by the King in the Matrikkel database, which means that Statsbygg has taken care of the King’s property without reporting to the municipalities that ownership has changed.

ByggForAlle

The Matrikkel’s building number has not been registered on all the buildings in the ByggForAlle dataset and some of the key information could include typos, manual updating errors or be out-of-date too.

The data workflow

To meet the challenges in the data integration, we’ve developed a data workflow as shown in the diagram below. It illustrates the process of importing the datasets, quality control and integration of the datasets, and finally generating the result dataset. The involved roles and their activities are modelled as swimming lanes. The original and generated datasets are modelled as dataobjects in the diagram such as SoEReport2013, BusinessEntityRegister, NewOrgList_Comfirmed etc. The quality control process can be both machine automated and manual work based on human tasks and it will take care of the integration exceptions.

dataworkflowsoefigure1

There are 3 roles involved in this process.

  • The SystemAdmin is a technical role and its main tasks are dataset import and integration.
  • The SystemManager is a functional role that has the main task of quality control and generating the SoE report including organizing and communication tasks with other involved organizations.
  • The PropertyResponsible is a role for each involved organization and its main task is to prepare data, quality control and submit its own property-list and building-list.

The activity boxes are explained as below:

  • ImportOldReportWithOrgList: SystemAdmin starts with checking if the SoE report from 2013 is imported. If not, the SystemAdmin imports the report which also includes the old organization list.
  • ImportMinistrySub_Brreg: Then the SystemAdmin imports the organization list of the Ministries and subordinate organizations from the Business Entity Register.
  • MergeOrgListBrreg_SoEReport2013: The two organization lists are merged.
  • EditComfirmOrgList: The SystemManager will get signal to start editing and updating the list, the result will be the confirmed OrgList.
  • ImportOwnedPropertyBuildingFromMatrikkelBasedOnOrglist_Comfirmed: Based on the confirmed OrgList, the owned properties and buildings from the Cadastre database (Matrikkel) are imported by the SystemAdmin.
  • PrepareExportForOwned: The property responsible will prepare a property list in a format as agreed.
  • ImportOwnedFromOrg: If some of the organizations such as Statsbygg have their own database or list of owned properties and buildings the lists will be imported as necessary.
  • ImportByggForAlleData: Then the ByggForAlle data is imported.
  • MergeAllDatasets: Afterwards data from Matrikkel and Business Entity Register (OrgList_comfirmed), the SoE reports 2013, properties data from organizations such as Statsbygg, ByggForAlle are merged by the SystemAdmin.
  • QualityControlMergedList: The SystemManager will then start the quality control cycle of the merged list.
  • EditAndConfirmOwnedList: The property responsible in each organization will get the task to edit and confirm their property and building list.
  • ApproveAndFinalizeNewSoEReport: The SystemManager will do the final quality control before approving and finalizing the new SoE Report.

 

Expected results and an example

Here below is one of the expected result from data quality control and integration in the step of “MergeAllDatasets”. The maps below shows both the examples of properties on the SoEReport2013 but not on the list based on Matrikkel_Brreg integration, and the properties on the Matrikkel_brreg integration but not on the SoEReport2013. After identifying the mismatches in this way, the users can work further on to clean the datasets to correct the wrong registrations in the source systems.

Symbol BRREG_Matrikkel integrated dataset Old SoE Report Example
Simple hatch No Yes “, NORSK INST.FOR SKOG OG LANDSKAP, NORSK INSTITUTT FOR SKOG OG LANDSKAP”

“,BIOFORSK, TOLLEFSRUD MARI METTE”

Cross hatch Yes No “STATENS VEGVESEN, ,STATENS VEGVESEN”
land parcels filled with solid color Yes Yes “MATTILSYNET,MATTILSYNET,MATTILSYNET”

 

The figure below shows that inside the Campus Ås. Some land parcels owned/leased by NMBU and Statens vegvesen according to Matrikkel are not included in the old SoE report, those land parcels are marked with crosshatch pattern. On the other side, some land parcels from the old SoE report are not included in the list based on BRREG and Matrikkel, such as the hatched land parcel with the label “, NORSK INST.FOR SKOG OG LANDSKAP, NORSK INSTITUTT FOR SKOG OG LANDSKAP” or “,BIOFORSK, TOLLEFSRUD MARI METTE”. Both of the simple hatch and cross hatch properties in the map need to be quality check and confirmed by the step of “QualityControlMergedList” and thereafter “EditAndConfirmOwnedList”.

dataworkflowsoefigure2

proDataMarket at the European Data Forum 2016

On 29 and 30 June proDataMarket participated in the European Data Forum (EDF) 2016, organized by Amsterdam Data Science and Technical University of Eindhoven under the auspices of the Dutch presidency of the European Union.

Evoluon

The conference, held in the Conference Center and former museum of Science and Technology Evoluon (Eindhoven, NE), was attended by Commissioner Günther Oettinger, the Rector of University of Tilburg and Philips, Siemens and TomTom CEOs. The event brought together more than 600 attendees from across Europe and multiple technology sectors.

General View

Likewise, proDataMarket presented a descriptive poster of the project, explaining its development and the conclusions reached so far in the different business cases and data-marketplace central infrastructure, and how proDataMarket aims to disrupt the PD market and demonstrate innovation across sectors where Property Data is relevant, by integrating technical framework for effective publishing, data consumption and showcasing data-driven business products.

poster

Besides the main event, the IQmulus project organized a workshop addressing Geospatial, Mathematical and Linked Big data. This event addressed aspects of big data where geolocation, geospatial or mathematical structures have a central role. In this side-event, the project coordinator, Dr. Dumitru Roman, also explained the whole project and its Business Cases.

Proof of Concept with Augmented Reality

 

The potential of the proDataMarket platform is huge, and by letting third party actors use and contribute to the “big data” platform, the potential could be even greater. To show how proDataMarket can be utilized, EVRY is developing two mobile applications that rely on proDataMarket service. The applications combine data from proDataMarket along with “augmented reality technology” to give the user a visual representation of the data. By doing this, EVRY will help contractors, construction or municipalities visualize future building projects. This is done with two iPad applications. The first application show underground infrastructure such as pipes and cables. The other application augments a 3D model in a real world scene.

Augmented reality (AR) is a live direct or indirect view of a physical, real-world environment whose elements are augmented (or supplemented) by computer-generated sensory input such as sound, video, graphics or GPS data [1]. The applications EVRY develops uses augmented reality technology to present cadastral data, distributed by proDataMarket. By doing this the applications can show underground structure on the screen (through the device camera), as well as 3D models of future building projects in a “real world scene” with information about the surroundings. This is done by having a 3D-model with correct measurement data (relative to its real world size), and by knowing the distance between a desired location and the user, the model can be scaled to the correct size according to the distance. Of course, if the user decides to manipulate the model (i.e. scaling it up), the size/distance relationship will be invalid. The 3D model augmentation can ease both private and commercial building projects by giving a visual presentation of how a building may look in a landscape.

The development process has been a process of trail and error and different augmented reality SDK have been examined. In the end the development team chose “Wikitude SDK [2]” to handle the augmentation processing. The task of augmenting a custom 3D model at a desired location is a suitable task for Wikitude SDK. By setting the model as a “Point of Interest” (POI) and using “GeoLocation”, the user can set the model at a desired location in a 2D map (Google map).

1

The model will be scaled to the correct size relative to the distance from the user. When a model is placed, Wikitude will augment the model and the user can see and manipulate with onscreen controls.

2

The manipulation controls are necessary because the iPad compass and location service are not accurate enough to get a satisfying result. If a user needs to place a model at a very exact location, there must be some way to tweak and calibrate the model. All in all, there are still some bugs left to fix in the applications, but the main functionality is in place and we are looking forward to show demos of what we have made.

[1] https://en.wikipedia.org/wiki/Augmented_reality

[2] http://www.wikitude.com/

Cerved and SpazioDati at Data Driven Innovation 2016

Cerved and SpazioDati participated in the first edition of Data Driven Innovation 2016 with a presentation and a stand about preliminary results of their collaborative work in the ProDataMarket project.

Cerved & SpazioDati present the first prototype for proDataMarket @DataDrivenInnovation 2016
Cerved & SpazioDati present the first prototype for proDataMarket @DataDrivenInnovation 2016

 

Data Driven Innovation is an open summit about big data hosted by Roma Tre university and organized by Codemotion. During two days of the summit many people have had the possibility to see the first results of Cerved & SpazioDati proDataMarket project: the Cerved Scouting Terrain Service (CST), an interactive map showing Bologna territory scores and social demographic scores, as the social disease index, the economic disease index, the socio-demographic score and much more territory scores.

CST, 2d business case of Cerved: Employees of the working population in Bologna
CST, 2d business case of Cerved: Employees of the working population in Bologna

 

CST is the second business case Cerved is being developed within the proDataMarket project: the goal of this service is to provide target users with a tool to search and see property and territory information on a map. In order to achieve this, Cerved is developing value-added geo-marketing indicators, analyses and visualisations.

Authors: Claudio Castelli & Diego Sanvito

ProDataMarket place as a toll for connecting real-estate data publishers and prospect data consumers

The main objective of the ProDataMarket project is to create a data marketplace for open and proprietary real-estate and related contextual data.

Marketplace is a place where data producers meet prospect data consumers. In addition to basic features for making data accessible and discoverable, marketplace can provide more tools to help data producers “advertise” their data and better engage with potential data consumers. Among such tools are those that help data producers explain the type of their data, its attributes and demonstrate its value. In this post we discuss how these tools are being realised in the ProDataMarket place.

Driving example

Let’s consider a national statistical office, for example, the Italian National Institute of Statistics (ISTAT). ISTAT wants to disseminate one of its datasets, a dataset with census cells that cover the Italian region of Piemonte. This dataset subdivides the region of Piemonte in census sections according to ISTAT’s 2011 National Census. A census section is the smallest geographic unit for which the statistical variables of a population census are taken.

ISTAT is interested in explaining to the prospect data consumers that the data can be useful when it is needed to:

  • determine inter-municipal boundaries
  • describe different areas of a city in terms of some geographically-bound characteristics

Marketplace: initial steps

Figure 1 illustrates initial steps that ISTAT performs at the marketplace to present her data.

Figure 1: The data producer prepares, describes and publishes her data at the marketplace, to make accessible and discoverable.

 

ISTAT prepares its data for publication, describes and catalogues it. Now, a prospect data consumer can discover and explore the dataset of census cells of the Piemonte region. While ISTAT made the data accessible and discoverable, data consumers still have to figure our themselves what type of data it is, what is inside and what is it useful for.

Marketplace: explaining the data types

To explain the type of the data, ISTAT creates and attaches visualisations to its data, as shown in Fig. 2.

Figure 2: The data producer creates visualisations, to explain the type of the data

 

In addition to preparing, describing and publishing Piemonte census sections dataset, ISTAT can create a map of all the census cells of the Piemonte region. This gives an illustrative example of the data to the prospect data consumers: when exploring the dataset, the data consumer can immediately see that the data contains polygons, each of which represents a geographic area of a census section.

Now that the type of the data is clearer, ISTAT can go further and explain various attributes of the data.

Marketplace: explaining attributes of the data 

Figure 3 illustrates steps that ISTAT performs at the marketplace, to give the data consumers a glimpse of the data attributes.

Figure 3: The data producer queries the data, to explain data attributes.

 

As mentioned above, the dataset of the driving example contains census cells’ geometries. Every cell is attach to a certain municipality. This information becomes useful if one wants to represent single municipalities on a map. For example, to represent the city of Turin, ISTAT can prepare a subset of the census cells by filtering on the municipality attribute of each cell. Similarly, other attributes of the data can be highlighted.

Marketplace: putting data into context to explain its value

With the help of the marketplace, ISTAT can prepare, describe and visualise as many subsets of the data, as she wants to. Finally, to showcase the value of the data and explain to the data consumer its value, ISTAT can put census cells into context, as illustrated in Fig. 4.

Figure 4: The data producer augments its data from other data sources, to show the “value in context”.

 

This last approach is realised through the Augmentation Service that supports querying a co-located data source using several functions to produce a new dataset. Currently, the Augmentation Service uses data from OpenStreetMap, to provide context. For example, ISTAT can use the service to extract the number of bus stops found nearby each census cell, or the distance to the closest train station, or the length of pedestrian paths in each census cell. Once the new augmented dataset is prepared, ISTAT can proceed with visualisations. For example, she can create a coloured map to show density of nearby bus stops in Turin.

Satellite images applied to property data

The Sentinels are a fleet of satellites designed specifically to deliver the wealth of data and imagery that are central to the European Commission’s Copernicus programme . This unique environmental monitoring programme is making a step change in the way we manage our environment, understand and tackle the effects of climate change and safeguard everyday lives. Sentinel-2 carries an innovative wide swath high-resolution multispectral imager with 13 spectral bands for a new perspective of our land and vegetation. The combination of high resolution, novel spectral capabilities, a swath width of 290 km and frequent revisit times is generating unprecedented views of Earth. Sentinel-2 is providing information for agricultural and forestry practices and for helping manage food security. Satellite images will be used to determine various crop and plant indexes. Some examples of these parameters could be:

  • Normalised Difference Vegetation Index (NDVI)
  • Normalised Difference Snow and Ice Index (NDSI)
  • Enhanced vegetation index (EVI)

This is particularly important for effective crops production prediction and applications related to Earth’s vegetation.

SentinelExampleSentinel use example

Sentinel-2 is the first optical Earth observation mission of its kind to include three bands in the ‘red edge’, which provide key information on the state of vegetation. In the previous image from 6 July 2015 acquired near Toulouse, France, the satellite’s multispectral instrument was able to discriminate between two types of crops: sunflower (in orange) and maize (in yellow).
These new and advanced datasets will be used inside CAPAS Business case to improve and enrich the information already obtained using LIDAR datasets (What is LIDAR?). Indeed, using LIDAR is possible to obtain accurate surface maps. However, data updates frequency is not very high. On the other hand, Sentinel constellation has a very high revisit frequency (five days) and offers information about kind of crops and their evolution. In conclusion, the use and merging of those different datasets answer several question regarding CAP parameters:

  • Is a specific parcel cultivated?
  • What kind of crop is growing in a plot?
  • Has the number of trees of a copse changed? When?
  • What is the ratio between Ecological Surfaces Areas (EFAs) and Productive areas in a given place?

Processing this kind of information could be very complex and laborious. It depends on selected indexes, chosen bands and geographical area. Furthermore, the processing is complicated by the high volumes of data. However, final results will offer a very detailed and accurate overview about land cover changes, environmental monitoring, crop monitoring, food security and detailed vegetation & forest monitoring parameters as leaf area index, chlorophyll concentration or carbon mass estimations. All this information and results have direct relation with Common Agricultural Policy principles and new European “Greening” policies.

Note: Some details about the characteristics and features of these instruments are available here.

Recent proDataMarket presentations

 

 

 

 

proDataMarket business cases at RuleML2015 Industry Track

The proDataMarket SoE and CAPAS business cases have been published/presented at the RuleML2015 Industry Track:

Norwegian State of Estate: A Reporting Service for the State-Owned Properties in Norway by Ling Shi, Bjørg E. Pettersen, Ivar Østhassel, Nikolay Nikolov, Arash Khorramhonarnama, Arne J. Berre, and Dumitru Roman

  • Abstract: Statsbygg is the public sector administration company responsible for reporting the state-owned property data in Norway. Traditionally the reporting process has been resource-demanding and error-prone. The State of Estate (SoE) business case presented in this paper is creating a new reporting service by sharing, integrating and utilizing cross-sectorial property data, aiming to increase the transparency and accessibility of property data from public sectors enabling downstream innovation. This paper explains the ambitions of the SoE business case, highlights the technical challenges related to data integration and data quality, data sharing and analysis, discusses the current solution and potential use of rules technologies.
  • Paper

 

CAPAS: A Service for Improving the Assignments of Common Agriculture Policy Funds to Farmers and Land Owners by Mariano Navarro, Ramón Baiget, Jesús Estrada and Dumitru Roman

  • Abstract: The Tragsa Group is part of the group of companies administered by the Spanish state-owned holding company Sociedad Estatal de Participaciones Industriales (SEPI). Its 37 years of experience have placed this business group at the forefront of different sectors ranging from agricultural, forestry, livestock, and rural development services, to conservation and protection of the environment in Spain. Tragsa is currently developing a business case around the implementation of a Common Agriculture Policy Assignment Service (CAPAS) – an extension of a currently active and widely used service (more than 20 million visits per year). The extension of the service in this business case is based on leveraging new cross-sectorial data sources, and targets a substantial reduction of incorrect agricultural funds assignments to farmers and land owners. This paper provides an overview of the business case, technical challenges related to the implementation of CAPAS (in areas such as data integration), discusses the current solution and potential use of rule technologies.
  • Paper