Setting interoperability between BIM and Geomodelling

International audienc

Geotechnical data standardization and management for BIM and Smart Cities

International audienc

Standardisation des Données Géotechniques pour le BIM - De MINnD à IDBE Geotech

International audienc

GeoScience DWG Welcome

International audienc

Standardizing geomodeling and geotechnical data exchanges: the SCUDDD and MINND projects

International audienc

OGC GeoScience DWG: a forum to enhance and organize geoscience data standardization

International audienc

Implementing INSPIRE: the BRGM road trip INSPIRE Conference

International audienceImplementing INSPIRE is not something that you can address like a one shot action. If your data evolve, your “INSPIRE maker system” should evolve too. Maintaining two different systems to expose the same data is not sustainable for most of organizations then solutions must be found to include those updating processes. Efficient INSPIRE system architecture should then be strong enough to expose correctly your data, and flexible enough to authorize possibilities of evolution. Then implementing INSPIRE is much more than a mapping exercise to new vocabularies and features. This presentation shows the actions lead by BRGM in order to provide geosciences data through INSPIRE. It gives feedback on past actions, technologies tested and then draw the path BRGM currently follow to reach INSPIRE compliancy. Finally it emphasis crucial innovations and actions that should be done to make INSPIRE road a more quiet trip

What If ?

International audienc

MINnD UC8-GT - Standardisation des données Géotechniques pour les Infrastructures Souterraines

International audienc

Geospatial data fusion and development of interoperable services to assess water needs at watershed scale

De nos jours, la préservation de l’environnement constitue un enjeu prioritaire. La compréhension des phénomènes environnementaux passe par l’étude et la combinaison d’un nombre croissant de données hétérogènes. De nombreuses initiatives internationales (INSPIRE, GEOSS) visent à encourager le partage et l’échange de ces données. Dans ce sujet de recherche, nous traitons de l’intérêt de mettre à disposition des modèles scientifiques sur le web. Nous montrons l’intérêt d’utiliser des applications s’appuyant sur des données géoréférencées et présentons des méthodes et des moyens répondant aux exigences d’interopérabilité. Nous illustrons notre approche par l’implémentation de modèles d’estimation des besoins en eau agricoles et domestiques fonctionnant à diverses échelles spatiales et temporelles. Un prototype basé sur une architecture entièrement orientée services web a été développé. L’outil s’appuie sur les standards Web Feature Service (WFS), Sensor Observation Service (SOS) et Web Processing Service (WPS) de l’OGC. Enfin, la prise en compte des imperfections des données est également abordée avec l’intégration de méthodes d’analyse de sensibilité et de propagation de l’incertitude.Nowadays, preservation of the environment is a main priority. Understanding of environmental phenomena requires the study and the combination of an increasing number of heterogeneous data. Several international initiatives (INSPIRE, GEOSS) aims to encourage the sharing and exchange of those data.In this thesis, the interest of making scientific models available on the web is discussed. The value of using applications based on geospatial data is demonstrated. Several methods and means that satisfy the requirements of interoperability are also purposed.Our approach is illustrated by the implementation of models for estimating agricultural and domestic water requirements. Those models can be used at different spatial scales and temporal granularities. A prototype based on a complete web service oriented architecture was developed. The tool is based on the OGC standards Web Feature Service (WFS), Sensor Observation Service (SOS) and Web Processing Service (WPS).Finally, taking into account the imperfections of the data is also discussed with the integration of methods for sensitivity analysis and uncertainty propagation