News > Forests

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)