From April 2021 to June 2021, we will provide tutorials, trainings, and 1:1 sessions on identifying and fixing INSPIRE compliance gaps. Here’s the schedule:

Webinars and Sessions

INSPIRE Online Trainings

XPlanung Online Trainings

To sign up, e-mail us at info@wetransform.to with the list of events you want to attend and your name and organization. To learn more about the trainings, visit our workshops webpage.

From April 2021 to June 2021, we will provide tutorials, trainings, and 1:1 sessions on identifying and fixing INSPIRE compliance gaps. Here’s the schedule:

Webinars and Sessions

INSPIRE Online Trainings

XPlanung Online Trainings

To sign up, e-mail us at info@wetransform.to with the list of events you want to attend and your name and organization. To learn more about the trainings, visit our workshops webpage.

As for many of you, 2020 wasn’t exactly easy for the team at wetransform. It would have been a challenging year even without COVID-19, given the big INSPIRE deadline in October.

In the end, the hard work and resilience paid off though: We were able to help make more than 20.000 data sets compliant with INSPIRE metadata, service, and data specifications. We almost doubled the number of customers, increased the number of user organisations by more than 180%, increased the data volume on our platform by 600%, and also doubled our annualized recurring revenue.

With this background and with the goal to enable further growth and the execution of important strategic initiatives, we decided to discuss a potential funding round with investors. As a result, we are very happy to welcome CADFEM International AG as a new investor and shareholder at Wetransform. CADFEM International AG is the international branch of CADFEM, one of the leading providers of industrial simulation solutions worldwide. CADFEM has made more than 25 investments in start-ups in various stages.

CADFEM has invested in this round together with the HTGF, Germany’s leading early-stage venture fund.

Here are some of the key initiatives that we will now execute:

• Help customers to retain optimal digital sovereignty by making hale connect compatible with GAIA-X
• Found and build up an Environmental Data Spaces community under the IDSA
• Implement ecosystem Data Spaces, e.g. for forestry, as part of the FutureForest project

Reach out to us if you are interested in being part of any of these initiatives – as a community member, pilot user, or as an employee at wetransform - we are hiring!

For now, we are looking forward to work together with all of you and with our shareholders to make sure that spatial and environmental data become more accessible, useable, and useful, and really contribute to us becoming a more sustainable society!

As for many of you, 2020 wasn’t exactly easy for the team at wetransform. It would have been a challenging year even without COVID-19, given the big INSPIRE deadline in October.

In the end, the hard work and resilience paid off though: We were able to help make more than 20.000 data sets compliant with INSPIRE metadata, service, and data specifications. We almost doubled the number of customers, increased the number of user organisations by more than 180%, increased the data volume on our platform by 600%, and also doubled our annualized recurring revenue.

With this background and with the goal to enable further growth and the execution of important strategic initiatives, we decided to discuss a potential funding round with investors. As a result, we are very happy to welcome CADFEM International AG as a new investor and shareholder at Wetransform. CADFEM International AG is the international branch of CADFEM, one of the leading providers of industrial simulation solutions worldwide. CADFEM has made more than 25 investments in start-ups in various stages.

CADFEM has invested in this round together with the HTGF, Germany’s leading early-stage venture fund.

Here are some of the key initiatives that we will now execute:

• Help customers to retain optimal digital sovereignty by making hale connect compatible with GAIA-X
• Found and build up an Environmental Data Spaces community under the IDSA
• Implement ecosystem Data Spaces, e.g. for forestry, as part of the FutureForest project

Reach out to us if you are interested in being part of any of these initiatives – as a community member, pilot user, or as an employee at wetransform - we are hiring!

For now, we are looking forward to work together with all of you and with our shareholders to make sure that spatial and environmental data become more accessible, useable, and useful, and really contribute to us becoming a more sustainable society!

The default encodings for INSPIRE, as per INSPIRE Data Specifications, are usually GML for vector data and GeoTIFF for raster coverages. However, since a single encoding is not optimal for all use cases, alternative encodings can also be used. In our previous blog post about alternate encodings, we already explained how alternative encodings can help to improve the data usability.

The GML default encoding works very well for system-to-system interoperability. But visualizing and analyzing large complex INSPIRE GML files in a GIS can be challenging. Thus, we have been working on supporting Geopackage, an alternative INSPIRE encoding that can be used directly on desktop.

How does this help me?

In our view GeoPackage is the optimal, open format for delivering medium to large sized data sets to GIS users. It is a single file that can store tables, vector geometries and rasters. It is extensible and fast to access. It can deal with simple and more detailed data models well. There is even the option to store views and styles. And GeoPackage does not have some of the shortcomings of GML such as 11-character attribute limits, unknown encodings, and missing or incomplete projection files.

Like INSPIRE GML datasets, GeoPackages are interoperable. In addition, GeoPackages can be used across all enterprise and personal computing environments. GeoPackages work much better even in environments with limited connectivity and bandwidth, such as mobile devices. Below you can find a comparison of GeoPackage and GML, and see how they complement each other:

How can I implement GeoPackage effectively, and what transformation rules should I consider?

There were always requests to add GeoPackage to the list of supported formats for hale»studio. To this end, we added a GeoPackage Reader and a Writer that was released with hale»studio 4.0. The Writer can create GeoPackages from scratch, including the schema and the metadata. This work was possible thanks to funding from Umweltbundesamt Austria and Rijkswaterstaat Netherlands, and support from the European Environmental Agency.

You can load data such as a shapefile, a FileGeodatabase, or simple GML. Then you map that data to a GeoPackage-specific schema or even an XML schema. Finally, you export your transformed data. If you already have GeoPackage source data, you can load it directly into hale»studio and use it in a transformation project.

The UML to Geopackage (U2G) rule was developed by UNIZAR, and has two parts:

• Compliance encoding rule: This rule is focused on INSPIRE compliance and helps to streamline data-validation procedures.
• Flattened dataset encoding rule: When encoded as GML, type aggregation in INSPIRE models can lead to a nesting depth of properties of up to 11 levels. Often, this leads to unnecessary structural overhead. The flattening of these structures significantly improves data usability in desktop software.

More information about the issues that GeoPackage addresses via encoding rules can be found here.

Schema Conversion Rules

The GeoPackage encoding takes a two-step approach. The first step occurs at the conceptual level, when INSPIRE constructs are transformed into GeoPackage constructs. These constructs are then turned into a Geopackage template. This template varies according to INSPIRE theme.

The mapping from the UML model to the GeoPackage, as per the original creators, can be found below:

The correspondence tables for other standards (for e.g. ISO 19115, ISO 19139, etc.) can be found here.

A Brief Case Study: Core Conformance Classes for the GeoPackage Encoding Rule for European Noise Directive data

Between 2020 and now, wetransform supported the EEA to provide a GeoPackage Encoding for European Noise directive data.

Conformance Classes

The END consists of multiple application schemas that inherit from different INSPIRE themes. This specific encoding rule defines several conformance classes:

• Noise Sources
• Noise Exposure including Noise Contours
• Quiet Areas
• Noise Action plans

A core conformance class describes common rules that are applied to all the aforementioned conformance classes. Additionally, there are also conformance-class specific rules.

The rules applied in this case to streamline the models were:

• Flattening hierarchical structures and data types: To make this data more useable in the GeoPackage encoding, the following strategies were applied:
  1. Substitution of complex types through simpler types (Simple Citation, Simple Codelist Reference, Simple Geographical Name, Simplified Localized Character String…)
  2. Usage of related tables for elements where the allowed cardinality is greater 1
  3. Flattening of properties
• Dealing with INSPIRE voidable attributes: To avoid clutter in the primary feature table whilst maintaining compatibility with INSPIRE conceptual model, a companion table to the actual primary feature table was created. Such properties could be stored in the companion table if required. Both the primary feature table and companion table are shown below:

• Setting default dataset properties: In INSPIRE data models, there are some properties that usually have the same value for every object in a data set, such as the voidReason attributes. Such attributes may be encoded into a DatasetDefaultProperties table and are removed as separate columns from the primary feature table. This results in streamlined GeoPackage primary feature table structure. The structure of the DatasetDefaultProperties is as follows:

• Handling code list values and titles: In INSPIRE GML, codelist values are encoded as xlinks that point to a fully qualified URL. Since these URLs contain special characters and are quite long, they are often harder to interpret, to use as labels and to use as filters for symbology. As a result, we use a specific model transformation rule:
  1. Keep the attribute name, but change the type to string
  2. In that string, write the local part of the value
  3. In an extra table called CodelistProperties, store a mapping of the table and property to the fully qualified URL of the codelist.

• Handling attributes with 1:n cardinality: In INSPIRE, many attributes of a feature type can have more than one value. This is used both to represent associations and composition relationships in the conceptual model, but often presents a challenge in other encodings than GML. As Geopackages can contain many tables with foreign key relationships, such compositions and associations are handled by introducing related tables. This is only done when a property type is complex and when the maximum multiplicity of the property is > 1.

• Handling of associations with 1:n and n:m multiplicity: In INSPIRE, features can have a many-to-many relationship. Such relationships can be represented in GeoPackage using a relationship table. In a relationship table, there is a primary key, as well as two foreign keys. As in the composition case, the foreign key columns are named _FID in the related table.

If you are interested in knowing more about the conformance class specific rules, just reach out to us at info@wetransform.to!

Want to start transforming data to GeoPackage? Try out our open-source tool, hale»studio today!

The required model transformations for complex GML cases are still under development, and you can expect to see them in the next hale»studio release later this year.

The default encodings for INSPIRE, as per INSPIRE Data Specifications, are usually GML for vector data and GeoTIFF for raster coverages. However, since a single encoding is not optimal for all use cases, alternative encodings can also be used. In our previous blog post about alternate encodings, we already explained how alternative encodings can help to improve the data usability.

The GML default encoding works very well for system-to-system interoperability. But visualizing and analyzing large complex INSPIRE GML files in a GIS can be challenging. Thus, we have been working on supporting Geopackage, an alternative INSPIRE encoding that can be used directly on desktop.

How does this help me?

In our view GeoPackage is the optimal, open format for delivering medium to large sized data sets to GIS users. It is a single file that can store tables, vector geometries and rasters. It is extensible and fast to access. It can deal with simple and more detailed data models well. There is even the option to store views and styles. And GeoPackage does not have some of the shortcomings of GML such as 11-character attribute limits, unknown encodings, and missing or incomplete projection files.

Like INSPIRE GML datasets, GeoPackages are interoperable. In addition, GeoPackages can be used across all enterprise and personal computing environments. GeoPackages work much better even in environments with limited connectivity and bandwidth, such as mobile devices. Below you can find a comparison of GeoPackage and GML, and see how they complement each other:

How can I implement GeoPackage effectively, and what transformation rules should I consider?

There were always requests to add GeoPackage to the list of supported formats for hale»studio. To this end, we added a GeoPackage Reader and a Writer that was released with hale»studio 4.0. The Writer can create GeoPackages from scratch, including the schema and the metadata. This work was possible thanks to funding from Umweltbundesamt Austria and Rijkswaterstaat Netherlands, and support from the European Environmental Agency.

You can load data such as a shapefile, a FileGeodatabase, or simple GML. Then you map that data to a GeoPackage-specific schema or even an XML schema. Finally, you export your transformed data. If you already have GeoPackage source data, you can load it directly into hale»studio and use it in a transformation project.

The UML to Geopackage (U2G) rule was developed by UNIZAR, and has two parts:

• Compliance encoding rule: This rule is focused on INSPIRE compliance and helps to streamline data-validation procedures.
• Flattened dataset encoding rule: When encoded as GML, type aggregation in INSPIRE models can lead to a nesting depth of properties of up to 11 levels. Often, this leads to unnecessary structural overhead. The flattening of these structures significantly improves data usability in desktop software.

More information about the issues that GeoPackage addresses via encoding rules can be found here.

Schema Conversion Rules

The GeoPackage encoding takes a two-step approach. The first step occurs at the conceptual level, when INSPIRE constructs are transformed into GeoPackage constructs. These constructs are then turned into a Geopackage template. This template varies according to INSPIRE theme.

The mapping from the UML model to the GeoPackage, as per the original creators, can be found below:

The correspondence tables for other standards (for e.g. ISO 19115, ISO 19139, etc.) can be found here.

A Brief Case Study: Core Conformance Classes for the GeoPackage Encoding Rule for European Noise Directive data

Between 2020 and now, wetransform supported the EEA to provide a GeoPackage Encoding for European Noise directive data.

Conformance Classes

The END consists of multiple application schemas that inherit from different INSPIRE themes. This specific encoding rule defines several conformance classes:

• Noise Sources
• Noise Exposure including Noise Contours
• Quiet Areas
• Noise Action plans

A core conformance class describes common rules that are applied to all the aforementioned conformance classes. Additionally, there are also conformance-class specific rules.

The rules applied in this case to streamline the models were:

• Flattening hierarchical structures and data types: To make this data more useable in the GeoPackage encoding, the following strategies were applied:
  1. Substitution of complex types through simpler types (Simple Citation, Simple Codelist Reference, Simple Geographical Name, Simplified Localized Character String…)
  2. Usage of related tables for elements where the allowed cardinality is greater 1
  3. Flattening of properties
• Dealing with INSPIRE voidable attributes: To avoid clutter in the primary feature table whilst maintaining compatibility with INSPIRE conceptual model, a companion table to the actual primary feature table was created. Such properties could be stored in the companion table if required. Both the primary feature table and companion table are shown below:

• Setting default dataset properties: In INSPIRE data models, there are some properties that usually have the same value for every object in a data set, such as the voidReason attributes. Such attributes may be encoded into a DatasetDefaultProperties table and are removed as separate columns from the primary feature table. This results in streamlined GeoPackage primary feature table structure. The structure of the DatasetDefaultProperties is as follows:

• Handling code list values and titles: In INSPIRE GML, codelist values are encoded as xlinks that point to a fully qualified URL. Since these URLs contain special characters and are quite long, they are often harder to interpret, to use as labels and to use as filters for symbology. As a result, we use a specific model transformation rule:
  1. Keep the attribute name, but change the type to string
  2. In that string, write the local part of the value
  3. In an extra table called CodelistProperties, store a mapping of the table and property to the fully qualified URL of the codelist.

• Handling attributes with 1:n cardinality: In INSPIRE, many attributes of a feature type can have more than one value. This is used both to represent associations and composition relationships in the conceptual model, but often presents a challenge in other encodings than GML. As Geopackages can contain many tables with foreign key relationships, such compositions and associations are handled by introducing related tables. This is only done when a property type is complex and when the maximum multiplicity of the property is > 1.

• Handling of associations with 1:n and n:m multiplicity: In INSPIRE, features can have a many-to-many relationship. Such relationships can be represented in GeoPackage using a relationship table. In a relationship table, there is a primary key, as well as two foreign keys. As in the composition case, the foreign key columns are named _FID in the related table.

If you are interested in knowing more about the conformance class specific rules, just reach out to us at info@wetransform.to!

Want to start transforming data to GeoPackage? Try out our open-source tool, hale»studio today!

The required model transformations for complex GML cases are still under development, and you can expect to see them in the next hale»studio release later this year.

We are currently rolling out the latest feature release of hale»connect. This version has already been deployed to most public cloud and private cloud setups and is now also available for on premise setups.

We have increased the number of data sources that you can use, improved performance of INSPIRE view services (as shown in the chart below), and extended metadata support.

Quartile comparison for updated GetMap response times

For Users

New Features

• For transformed data sets, there is now a link to the source data set on the overview page.
• An uploaded SLD file can now also be downloaded again in the theme configuration.
• New functions have been added to improve the performance of web map services (see section for system administrators).
• In the metadata configuration, several “default” values can now be configured for fields with possible multiple occurrences (via a JSON array).
• It is now possible to use NetCDF files as data sources.

Changes

• When uploading a shape file as a schema that contains unsupported special characters in the file name, an error message is now displayed instead of just failing.
• A task has been added that regularly checks whether there are any unnecessary attachments and deletes them.
• The limit for uploading SLDs has been increased and now also allows very large SLD files (up to 2 MB).

Fixes

• For dataset series, the button for deactivating / activating services is no longer incorrectly displayed.
• An error in the sorting of the organization filter for the data records related to the case has been corrected.
• If records are deleted in a series, no more raster-related resources remain.
• When INSPIRE metadata is generated, no invalid “spatial scope” is generated if no value has been specified.
• The support for multi-step transformation chains has been improved.
• Errors in the calculation of capacity points for data record series have been corrected.

Information for system administrators

Introduction of a Hybrid Rendering Mode for View Services

So far, hale»connect uses what is known as blob mode to store complex GML objects. This means that instead of generating a fully-fledged normalized database schema, the GML is stored as a single big blob. While this works well for WFS and has additional advantages, it limits WMS performance in several critical places.

We have now introduced a new hybrid mode that stores the data as before as a blob in the database, but also uses the SLD to create a relational database structure that is used for rendering in the WMS.

This relational structure only contains the information that is relevant for rendering, i.e. the geometry and any properties referenced in the SLD. In many cases, this enables us to achieve much better rendering performance, as the data for rendering can be fetched more efficiently from the database.

Limitation 1: Hybrid mode is currently not supported for data set series.

Limitation 2: Whether the hybrid mode can be used for a publication depends on the structure of the data and the SLD. Certain constructs may not (yet) be supported. This includes, for example, if information from associated features is required in the SLD (example: visualization of MappedFeature depending on GeologicUnit).

Introduction of automated generalisation in the hybrid mode

In the new hybrid mode, generalized geometries can be generated in order to optimize the rendering in the WMS for different scales. This means that the rendering performance is significantly less affected by detailed geometries, but the complete details are still available via the WFS and on a large scale in the WMS.

Users can only choose to activate or deactivate generalization at this time. If activated, a standard configuration provided by us is applied, which creates four generalization steps for scales around 1: 5,000, 1: 15,000, 1: 150,000 and 1: 1,000,000.

Note that the generalization option is only available if the hybrid mode can be used for a data set.

Limiting the number of features to render per tile

The performance when rendering WMS requests suffers greatly when large sets of objects are to be rendered. In extreme cases, a single request can encompass several million objects to render, e.g. if an entire data record is to be displayed on a small scale at once. Often this results in response times that are beyond any timeouts, and which are also beyond the INSPIRE Quality of Service requirements.

In hale»connect there is now the possibility to limit the maximum number of features rendered for a given tile. This limitation already applies to the database in hybrid mode and for SLDs without filters, even in blob mode. Larger objects are preferred over smaller objects to be more likely to select objects that are also visible or that span several tiles.

For this functionality there are no user-visible fine-tuning options – it can only be turned on or off. When activated for a dataset, no more than 10,000 features are rendered for a single 256x256 tile.

Erroneous Feature Type associations

An error has led to inconsistent feature type assignment in the database. This could lead to problems when inserting data.

This problem has been resolved by reading and using the assignment stored in the database. To do this, however, the data in the database must be corrected so that it uses the correct mapping.

This happens automatically with this version when the WMS/WFS service is started, but the data is no longer compatible with previous versions after the process has been completed.

The logs should contain messages such as “Starting attempt to fix blob mappings in feature store” and “Completed fixing blob mappings in feature store” with additional details at the first start for each feature store / publication.

In order to avoid the possible caching of incorrect tiles by Mapproxy, it is recommended to switch off the bsp-mapproxy service for the duration of the process (e.g. by setting the configuration key override_replicas.bsp-mapproxy to 0 before the deployment).

Interested in learning more about how hale»connect can make INSPIRE easy for you? Click here.

We are currently rolling out the latest feature release of hale»connect. This version has already been deployed to most public cloud and private cloud setups and is now also available for on premise setups.

We have increased the number of data sources that you can use, improved performance of INSPIRE view services (as shown in the chart below), and extended metadata support.

Quartile comparison for updated GetMap response times

For Users

New Features

• For transformed data sets, there is now a link to the source data set on the overview page.
• An uploaded SLD file can now also be downloaded again in the theme configuration.
• New functions have been added to improve the performance of web map services (see section for system administrators).
• In the metadata configuration, several “default” values can now be configured for fields with possible multiple occurrences (via a JSON array).
• It is now possible to use NetCDF files as data sources.

Changes

• When uploading a shape file as a schema that contains unsupported special characters in the file name, an error message is now displayed instead of just failing.
• A task has been added that regularly checks whether there are any unnecessary attachments and deletes them.
• The limit for uploading SLDs has been increased and now also allows very large SLD files (up to 2 MB).

Fixes

• For dataset series, the button for deactivating / activating services is no longer incorrectly displayed.
• An error in the sorting of the organization filter for the data records related to the case has been corrected.
• If records are deleted in a series, no more raster-related resources remain.
• When INSPIRE metadata is generated, no invalid “spatial scope” is generated if no value has been specified.
• The support for multi-step transformation chains has been improved.
• Errors in the calculation of capacity points for data record series have been corrected.

Information for system administrators

Introduction of a Hybrid Rendering Mode for View Services

So far, hale»connect uses what is known as blob mode to store complex GML objects. This means that instead of generating a fully-fledged normalized database schema, the GML is stored as a single big blob. While this works well for WFS and has additional advantages, it limits WMS performance in several critical places.

We have now introduced a new hybrid mode that stores the data as before as a blob in the database, but also uses the SLD to create a relational database structure that is used for rendering in the WMS.

This relational structure only contains the information that is relevant for rendering, i.e. the geometry and any properties referenced in the SLD. In many cases, this enables us to achieve much better rendering performance, as the data for rendering can be fetched more efficiently from the database.

Limitation 1: Hybrid mode is currently not supported for data set series.

Limitation 2: Whether the hybrid mode can be used for a publication depends on the structure of the data and the SLD. Certain constructs may not (yet) be supported. This includes, for example, if information from associated features is required in the SLD (example: visualization of MappedFeature depending on GeologicUnit).

Introduction of automated generalisation in the hybrid mode

In the new hybrid mode, generalized geometries can be generated in order to optimize the rendering in the WMS for different scales. This means that the rendering performance is significantly less affected by detailed geometries, but the complete details are still available via the WFS and on a large scale in the WMS.

Users can only choose to activate or deactivate generalization at this time. If activated, a standard configuration provided by us is applied, which creates four generalization steps for scales around 1: 5,000, 1: 15,000, 1: 150,000 and 1: 1,000,000.

Note that the generalization option is only available if the hybrid mode can be used for a data set.

Limiting the number of features to render per tile

The performance when rendering WMS requests suffers greatly when large sets of objects are to be rendered. In extreme cases, a single request can encompass several million objects to render, e.g. if an entire data record is to be displayed on a small scale at once. Often this results in response times that are beyond any timeouts, and which are also beyond the INSPIRE Quality of Service requirements.

In hale»connect there is now the possibility to limit the maximum number of features rendered for a given tile. This limitation already applies to the database in hybrid mode and for SLDs without filters, even in blob mode. Larger objects are preferred over smaller objects to be more likely to select objects that are also visible or that span several tiles.

For this functionality there are no user-visible fine-tuning options – it can only be turned on or off. When activated for a dataset, no more than 10,000 features are rendered for a single 256x256 tile.

Erroneous Feature Type associations

An error has led to inconsistent feature type assignment in the database. This could lead to problems when inserting data.

This problem has been resolved by reading and using the assignment stored in the database. To do this, however, the data in the database must be corrected so that it uses the correct mapping.

This happens automatically with this version when the WMS/WFS service is started, but the data is no longer compatible with previous versions after the process has been completed.

The logs should contain messages such as “Starting attempt to fix blob mappings in feature store” and “Completed fixing blob mappings in feature store” with additional details at the first start for each feature store / publication.

In order to avoid the possible caching of incorrect tiles by Mapproxy, it is recommended to switch off the bsp-mapproxy service for the duration of the process (e.g. by setting the configuration key override_replicas.bsp-mapproxy to 0 before the deployment).

Interested in learning more about how hale»connect can make INSPIRE easy for you? Click here.

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.

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.

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 😉