News > Data Usefulness

2020 is ending soon. It was a year of challenges, but also full of innovations. With the INSPIRE deadline passing, it was an exciting time for wetransform, INSPIRE stakeholders and also for other SDI implementers. Here’s the year in retrospect.

The Highlights

The FutureForest Project: In February, we initiated a project that builds on harmonized INSPIRE data to solve a critical issue: The ecological and economic impact of climate change on our forests. Learn more here and see how INSPIRE can save our forests.

The GO-PEG Project: “What can I use INSPIRE data for?” is a question we’ve heard all too often. Through the Go-PEG project, organizations are making their data available for access through INSPIRE and the EDP. wetransform, along with other project partners, are harmonizing data to INSPIRE specification, and also working to make the data more useful, specifically to boost digital transformation and Business Intelligence/Artificial Intelligence projects. Learn more here.

Product Updates

Metadata V2.0: We are pleased to announce that auto-generated hale»connect dataset and network service is now fully compliant with the Technical Guidance for the Implementation of INSPIRE dataset and service metadata based on ISO/TS 19139:2007. Learn more here.

Dataset Series enabled per Organization: The Dataset series section allows you to enable the publishing of dataset series. A data set series is a collection of data sets sharing the same product specification [ISO 19115]. All files in a data set series have the same schema and the same spatial coordinate reference system. A data set series has its own INSPIRE, ISO or national metadata.

Services Graph on Dataset Overview Page: The dataset overview page now includes a service usage section that contains bar graphs which displays usage statistics for WMS and WFS services, with the option to display results for the last year or the last 30 days. The Overall accesses by service type graph displays total service usage. The WMS capabilities requests By User Agent graph displays the percentages of requests made by different user agents, including browsers such as Firefox and Chrome, QGIS, the hale»connect FeatureExplorer and more. The WFS capabilities requests By User Agent displays the same information for WFS services. The Export as CSV button enables users to download usage statistics based on a custom date range. This feature is only available to hale»connect advanced and hale»connect enterprise users.

Dataset Series Service Usage Graph
Overall Accesses by Service Type Graph

CSW Endpoints enabled per organization: A parameter was added to the CSW that enables users to retrieve all resources belonging to an organisation by providing their organisation number. Currently this additional functionality must be requested and enabled by wetransform. New suborganisations may take up to 24h to be included in the endpoint.

Coming Up Next

hale»studio Code Sprint: We are planning to do a virtual code sprint for hale studio 4.1 in from the 11th to the 17th of January, with a focus on UI/UX enhancements such as deleting single resources from a project. The release milestone can be found here.

Webinars and Trainings: The beginning of the year will be packed with informative content, including a webinar on the popular topic of Identifying and Publishing Priority Datasets. You can learn more about the upcoming webinars and trainings here.

And lastly, the wetransform team wishes you a joyous holiday season, and we look forward to more INSPIRE-ing work next year 😉

2020 is ending soon. It was a year of challenges, but also full of innovations. With the INSPIRE deadline passing, it was an exciting time for wetransform, INSPIRE stakeholders and also for other SDI implementers. Here’s the year in retrospect.

The Highlights

The FutureForest Project: In February, we initiated a project that builds on harmonized INSPIRE data to solve a critical issue: The ecological and economic impact of climate change on our forests. Learn more here and see how INSPIRE can save our forests.

The GO-PEG Project: “What can I use INSPIRE data for?” is a question we’ve heard all too often. Through the Go-PEG project, organizations are making their data available for access through INSPIRE and the EDP. wetransform, along with other project partners, are harmonizing data to INSPIRE specification, and also working to make the data more useful, specifically to boost digital transformation and Business Intelligence/Artificial Intelligence projects. Learn more here.

Product Updates

Metadata V2.0: We are pleased to announce that auto-generated hale»connect dataset and network service is now fully compliant with the Technical Guidance for the Implementation of INSPIRE dataset and service metadata based on ISO/TS 19139:2007. Learn more here.

Dataset Series enabled per Organization: The Dataset series section allows you to enable the publishing of dataset series. A data set series is a collection of data sets sharing the same product specification [ISO 19115]. All files in a data set series have the same schema and the same spatial coordinate reference system. A data set series has its own INSPIRE, ISO or national metadata.

Services Graph on Dataset Overview Page: The dataset overview page now includes a service usage section that contains bar graphs which displays usage statistics for WMS and WFS services, with the option to display results for the last year or the last 30 days. The Overall accesses by service type graph displays total service usage. The WMS capabilities requests By User Agent graph displays the percentages of requests made by different user agents, including browsers such as Firefox and Chrome, QGIS, the hale»connect FeatureExplorer and more. The WFS capabilities requests By User Agent displays the same information for WFS services. The Export as CSV button enables users to download usage statistics based on a custom date range. This feature is only available to hale»connect advanced and hale»connect enterprise users.

Dataset Series Service Usage Graph
Overall Accesses by Service Type Graph

CSW Endpoints enabled per organization: A parameter was added to the CSW that enables users to retrieve all resources belonging to an organisation by providing their organisation number. Currently this additional functionality must be requested and enabled by wetransform. New suborganisations may take up to 24h to be included in the endpoint.

Coming Up Next

hale»studio Code Sprint: We are planning to do a virtual code sprint for hale studio 4.1 in from the 11th to the 17th of January, with a focus on UI/UX enhancements such as deleting single resources from a project. The release milestone can be found here.

Webinars and Trainings: The beginning of the year will be packed with informative content, including a webinar on the popular topic of Identifying and Publishing Priority Datasets. You can learn more about the upcoming webinars and trainings here.

And lastly, the wetransform team wishes you a joyous holiday season, and we look forward to more INSPIRE-ing work next year 😉

(more)

At wetransform, we fully support INSPIRE implementers since we think that key problems can be better solved through cross-border, accessible and useable, harmonised data. In February, we have started a new project that heavily builds on harmonised INSPIRE data to solve a critical issue: The massive ecological and economic impact of climate change on our forests.

Forests are subject to numerous stressors: extreme weather events such as heat, drought and heavy rainfall, pests such as bark beetles and air pollutants. Even tree species that were considered to be stable have suffered from these stressors in the last years. As a result, forest experts face new challenges. They have to identify stands that are at a high risk of being endangered. For each location, they need to find optimal forest development types, tree species and intraspecific varieties. Their measures must ensure that the biodiversity of forests and economic yields are maintained despite climate change.

The factors that influence the general vitality and productivity of forests (climate, weather, soil, geology, morphology, biodiversity, age mixing, etc.) are complex and interconnected. Forest experts consider these in the planning process. However, an in-depth analysis of these variables and their interconnectedness is still a big challenge.

A keystone of new approaches is, like in many other industries, a shift to data-driven decision support. Data on the aforementioned factors can form a basis for more effective and faster decision-making, but gathering useful data is not an easy task. The sheer size and complexity of environmental data related to forests ensures that. To compound matters, this data is held by different organizations, which usually have diverse use cases and use different schemas, formats and semantics. For example, one organization may represent the topographic data of a forest region in a shapefile, and another organization may represent the topographic data of a neighbouring region in the same forest as a GML file.

Forest experts thus depend on a wide range of data sets that are acquired from different sources. This interdisciplinary knowledge transfer is a challenge, but is also mission critical. High quality data acquisition and data integration are a precursor to the forest experts’ data driven decision-making and ultimately to the survival of our forests.

To help forest experts and owners to deal with these issues, wetransform has initiated the FutureForst project. FutureForst is a Phase I Artificial Intelligence Lighthouse project co-funded by the German Zukunft-Umwelt-Gesellschaft (ZUG) association. Through FutureForst, forest owners receive comprehensive decision support that has been adapted to their specific situation and goals. These recommendations are based on harmonized data such as the forest inventory, weather, pest development and air pollution.

In order to provide this decision support, we currently evaluate deep-learning methods and “Explainable AI” approaches such as Semantic Reasoning and Bayesian Belief Networks together with our partners, Minerva Intelligence GmbH and the Forstliche Versuchsanstalt Baden-Württemberg.

With the “Explainable AI” approach, the system can generate comprehensible results with accessible recommendations for action. Explainable AI methods enable users to see which variables of the input data lead to which outcome. These can be adjusted by experts and laypersons in any depth and checked for plausibility. On this basis, experts and laymen can then decide which forest development types and tree species can be established as “future forests” - forest ecosystems that can withstand climate change.

In a further step, different climate forecasts can then be taken into account and forest conversion scenarios can be simulated. In addition, a solution forum will be offered where users and partners can exchange information on their approach.

The data that is core to the approach is harmonised and published with wetransform’s tools, hale connect and hale studio. These tools have already been used by hundreds of organizations to consolidate heterogenous stacks into harmonized data that can be easily analysed. Afterwards, components of Minerva’s AI suite are used for the analysis and development of explainable recommendations based on the harmonized data.

When this project is finished, we aim to provide a FutureForst solution that offers:

  • Always up-to-date, homogeneous data basis
  • Complete picture of the environment including real-time stressors such as pest infestation
  • Highly local, explainable recommendations for action based on international data
  • Solution forum for users and partners We are currently executing open remote workshops on this project every two weeks.

These workshops provide opportunities to learn about what the concrete outcomes of the project are, and to contribute experiences and requirements. The workshops are themed as follows:

  • 15.04.2020: End user scenario development
  • 29.04.2020: Existing and missing data
  • 13.05.2020: AI Approaches and Algorithms

Reach out to us to stay updated on the project’s progress and let us know how your country is dealing with this challenge:


At wetransform, we fully support INSPIRE implementers since we think that key problems can be better solved through cross-border, accessible and useable, harmonised data. In February, we have started a new project that heavily builds on harmonised INSPIRE data to solve a critical issue: The massive ecological and economic impact of climate change on our forests.

Forests are subject to numerous stressors: extreme weather events such as heat, drought and heavy rainfall, pests such as bark beetles and air pollutants. Even tree species that were considered to be stable have suffered from these stressors in the last years. As a result, forest experts face new challenges. They have to identify stands that are at a high risk of being endangered. For each location, they need to find optimal forest development types, tree species and intraspecific varieties. Their measures must ensure that the biodiversity of forests and economic yields are maintained despite climate change.

The factors that influence the general vitality and productivity of forests (climate, weather, soil, geology, morphology, biodiversity, age mixing, etc.) are complex and interconnected. Forest experts consider these in the planning process. However, an in-depth analysis of these variables and their interconnectedness is still a big challenge.

A keystone of new approaches is, like in many other industries, a shift to data-driven decision support. Data on the aforementioned factors can form a basis for more effective and faster decision-making, but gathering useful data is not an easy task. The sheer size and complexity of environmental data related to forests ensures that. To compound matters, this data is held by different organizations, which usually have diverse use cases and use different schemas, formats and semantics. For example, one organization may represent the topographic data of a forest region in a shapefile, and another organization may represent the topographic data of a neighbouring region in the same forest as a GML file.

Forest experts thus depend on a wide range of data sets that are acquired from different sources. This interdisciplinary knowledge transfer is a challenge, but is also mission critical. High quality data acquisition and data integration are a precursor to the forest experts’ data driven decision-making and ultimately to the survival of our forests.

To help forest experts and owners to deal with these issues, wetransform has initiated the FutureForst project. FutureForst is a Phase I Artificial Intelligence Lighthouse project co-funded by the German Zukunft-Umwelt-Gesellschaft (ZUG) association. Through FutureForst, forest owners receive comprehensive decision support that has been adapted to their specific situation and goals. These recommendations are based on harmonized data such as the forest inventory, weather, pest development and air pollution.

In order to provide this decision support, we currently evaluate deep-learning methods and “Explainable AI” approaches such as Semantic Reasoning and Bayesian Belief Networks together with our partners, Minerva Intelligence GmbH and the Forstliche Versuchsanstalt Baden-Württemberg.

With the “Explainable AI” approach, the system can generate comprehensible results with accessible recommendations for action. Explainable AI methods enable users to see which variables of the input data lead to which outcome. These can be adjusted by experts and laypersons in any depth and checked for plausibility. On this basis, experts and laymen can then decide which forest development types and tree species can be established as “future forests” - forest ecosystems that can withstand climate change.

In a further step, different climate forecasts can then be taken into account and forest conversion scenarios can be simulated. In addition, a solution forum will be offered where users and partners can exchange information on their approach.

The data that is core to the approach is harmonised and published with wetransform’s tools, hale connect and hale studio. These tools have already been used by hundreds of organizations to consolidate heterogenous stacks into harmonized data that can be easily analysed. Afterwards, components of Minerva’s AI suite are used for the analysis and development of explainable recommendations based on the harmonized data.

When this project is finished, we aim to provide a FutureForst solution that offers:

  • Always up-to-date, homogeneous data basis
  • Complete picture of the environment including real-time stressors such as pest infestation
  • Highly local, explainable recommendations for action based on international data
  • Solution forum for users and partners We are currently executing open remote workshops on this project every two weeks.

These workshops provide opportunities to learn about what the concrete outcomes of the project are, and to contribute experiences and requirements. The workshops are themed as follows:

  • 15.04.2020: End user scenario development
  • 29.04.2020: Existing and missing data
  • 13.05.2020: AI Approaches and Algorithms

Reach out to us to stay updated on the project’s progress and let us know how your country is dealing with this challenge:


(more)

Many INSPIRE implementers we talk to question the usefulness of the directive. They see a lot of effort to create and update metadata, to harmonise data and to publish view and download services, but also see that these are hard to use for their own core processes, due to a mismatch between the formats and data models and what typical GIS software can easily consume.

INSPIRE Spatial Data Infrastructure (SDI) implementation focuses on two aspects:

  • Making the data accessible: The SDI on an international, national and regional level is specified. Metadata, view and download services, and data access APIs are implemented.
  • Making the data usable: The SDI on an international, national and regional level is specified, and the associated datasets are documented, interoperable and validated according the relevant specifications.

Achieving these would mean that you are fully INSPIRE compliant. New users will find data much more easily, and they will be able to use it since it is documented and harmonised. The question that remains, however, is how can this interoperability provide a return on the investment to the actual implementers?

Interoperable datasets provide the value add of reducing future costs for data exchange data between authorities, and they reduce costs for the deployment of new processes since standard tools can be used.

Both internal and external Return of Investment will increase exponentially when a third aspect is added to the SDI, which is making the data useful. For this, data in the SDI has to be optimized to match used cases applicable for relevant stakeholders, content and delivery methods must be curated and optimized, and datasets must be prioritised. Such use-case optimized specifications can take many forms:

  • INSPIRE extensions targeted at enriching data in the SDI in order to better support specific business requirements;
  • Analytic APIs targeted at boosting data-driven analysis;
  • BI/AI data optimization to reduce AI model training costs and improve the quality of the data that forms the basis of certain business processes.

The current status of the maturity of SDIs as data platforms is shown below:

Maturity of SDIs as Data Platforms
Maturity of SDIs as Data Platforms

To make INSPIRE data more usable and useful, wetransform along with partner organizations has initiated the GO-PEG project. The project aims to bring high-value, harmonised spatial data sets to the European Data Portal. Such data sets are currently often inaccessible, fragmented, and highly heterogeneous, as they are managed by dozens, or even hundreds of different organisations.

GO-PEG intends to set up a highly automated data harmonisation workbench, from which web services providing access (view and download) to harmonised thematic open dataset(s) from multiple and heterogeneous candidate data sources (including geospatial data provided under INSPIRE, Copernicus data and data provided by crowd-sourced initiatives such as OpenStreetMap) are generated.

The project will benefit stakeholders by providing the following assets:

  • Data providers:
    • …get access to tools that simplify data harmonisation towards INSPIRE and other open standards;
    • …exchange information on data quality, harmonisation, licencing, etc. between data providers and IT experts that provide data harmonisation tools;
    • …increase the number of their clients/data users based on novel use-cases scenarios which have arisen out of efficient data harmonisations.
  • Experts:
    • …can easily access and use trans-European INSPIRE conformant datasets within the relevant thematic fields.
    • …increase the number of clients/data users based on novel use-cases scenarios which have arisen out of efficient data harmonisations.
  • Policy makers:
    • … have a larger and interoperable database to support decision-making on the national and European level.
  • INSPIRE Community
    • …fosters optimal standardization and data harmonisation processes.

In the project, organizations will make their data available for access through INSPIRE and the EDP. To provide a unified dataset at the EDP, wetransform and the associated project partners will harmonise the data to INSPIRE data specifications, as well as make it more useful, specifically to boost digital transformation and BI/AI projects.

In effect, we would make data interoperable for free in the project, though it would need to meet at least one of the data licenses used in the EDP (which is Open Data, but one can pick the least permissive if needed).

Priority will be given to high value datasets defined by the Open Data Directive. The services will be harmonised in terms of data content, level of detail, data structure, vocabularies and licence conditions and will have a geographical coverage including at least 5 Member States.

If you have the relevant thematic data (environment data, emergency management data and disaster management data), and you would like to take part in this project (and effectively make your data INSPIRE compliant and interoperable for free), write to us by clicking the button below!


Many INSPIRE implementers we talk to question the usefulness of the directive. They see a lot of effort to create and update metadata, to harmonise data and to publish view and download services, but also see that these are hard to use for their own core processes, due to a mismatch between the formats and data models and what typical GIS software can easily consume.

INSPIRE Spatial Data Infrastructure (SDI) implementation focuses on two aspects:

  • Making the data accessible: The SDI on an international, national and regional level is specified. Metadata, view and download services, and data access APIs are implemented.
  • Making the data usable: The SDI on an international, national and regional level is specified, and the associated datasets are documented, interoperable and validated according the relevant specifications.

Achieving these would mean that you are fully INSPIRE compliant. New users will find data much more easily, and they will be able to use it since it is documented and harmonised. The question that remains, however, is how can this interoperability provide a return on the investment to the actual implementers?

Interoperable datasets provide the value add of reducing future costs for data exchange data between authorities, and they reduce costs for the deployment of new processes since standard tools can be used.

Both internal and external Return of Investment will increase exponentially when a third aspect is added to the SDI, which is making the data useful. For this, data in the SDI has to be optimized to match used cases applicable for relevant stakeholders, content and delivery methods must be curated and optimized, and datasets must be prioritised. Such use-case optimized specifications can take many forms:

  • INSPIRE extensions targeted at enriching data in the SDI in order to better support specific business requirements;
  • Analytic APIs targeted at boosting data-driven analysis;
  • BI/AI data optimization to reduce AI model training costs and improve the quality of the data that forms the basis of certain business processes.

The current status of the maturity of SDIs as data platforms is shown below:

Maturity of SDIs as Data Platforms
Maturity of SDIs as Data Platforms

To make INSPIRE data more usable and useful, wetransform along with partner organizations has initiated the GO-PEG project. The project aims to bring high-value, harmonised spatial data sets to the European Data Portal. Such data sets are currently often inaccessible, fragmented, and highly heterogeneous, as they are managed by dozens, or even hundreds of different organisations.

GO-PEG intends to set up a highly automated data harmonisation workbench, from which web services providing access (view and download) to harmonised thematic open dataset(s) from multiple and heterogeneous candidate data sources (including geospatial data provided under INSPIRE, Copernicus data and data provided by crowd-sourced initiatives such as OpenStreetMap) are generated.

The project will benefit stakeholders by providing the following assets:

  • Data providers:
    • …get access to tools that simplify data harmonisation towards INSPIRE and other open standards;
    • …exchange information on data quality, harmonisation, licencing, etc. between data providers and IT experts that provide data harmonisation tools;
    • …increase the number of their clients/data users based on novel use-cases scenarios which have arisen out of efficient data harmonisations.
  • Experts:
    • …can easily access and use trans-European INSPIRE conformant datasets within the relevant thematic fields.
    • …increase the number of clients/data users based on novel use-cases scenarios which have arisen out of efficient data harmonisations.
  • Policy makers:
    • … have a larger and interoperable database to support decision-making on the national and European level.
  • INSPIRE Community
    • …fosters optimal standardization and data harmonisation processes.

In the project, organizations will make their data available for access through INSPIRE and the EDP. To provide a unified dataset at the EDP, wetransform and the associated project partners will harmonise the data to INSPIRE data specifications, as well as make it more useful, specifically to boost digital transformation and BI/AI projects.

In effect, we would make data interoperable for free in the project, though it would need to meet at least one of the data licenses used in the EDP (which is Open Data, but one can pick the least permissive if needed).

Priority will be given to high value datasets defined by the Open Data Directive. The services will be harmonised in terms of data content, level of detail, data structure, vocabularies and licence conditions and will have a geographical coverage including at least 5 Member States.

If you have the relevant thematic data (environment data, emergency management data and disaster management data), and you would like to take part in this project (and effectively make your data INSPIRE compliant and interoperable for free), write to us by clicking the button below!


(more)