Periodic Reporting for period 2 - VOLTA (innoVation in geOspatiaL and 3D daTA)
Okres sprawozdawczy: 2019-06-01 do 2022-11-30
VOLTA is a RISE MSCA project that offers and performs secondments, investigations, training activities and networking to facilitate knowledge exchange and career progresses in the geospatial field (i.e. photogrammetry, remote sensing, GIS, surveying, etc.). The geospatial field produces, delivers and uses geospatial data (i.e. data with a geographic component) for applications in smart cities, forestry, location-based services, 3D city modelling, navigation, mapping, heritage documentation, energy efficiency, etc.
VOLTA want to reach the following objectives:
- to develop new methodologies to extract accurate geometric information from images – at various scale and from different sensors – in a more automatic, reliable, repeatable and productive manner than current state-of-the-art;
- to fuse sensors and data in order to exploit the intrinsic advantages of each one and generate products with superior quality characteristics than from a single (sort of) sensor;
- to automatically classify and understand (2D and 3D) geospatial data, including 2D/3D feature extraction;
- to exploit Cloud computing for faster processing and to provide access to large quantities of geodata.
The project, within it researches, events and secondments, wants to demonstrate and enlarge the benefits of geospatial data and technologies, investigating open issues, offering new processing solutions and unveiling how the geospatial field is evolving and bringing benefits to the society.
VOLTA includes 13 partners from research and academic institutions, geospatial companies and mapping agencies.
VOLTA want to reach the following objectives:
- to develop new methodologies to extract accurate geometric information from images – at various scale and from different sensors – in a more automatic, reliable, repeatable and productive manner than current state-of-the-art;
- to fuse sensors and data in order to exploit the intrinsic advantages of each one and generate products with superior quality characteristics than from a single (sort of) sensor;
- to automatically classify and understand (2D and 3D) geospatial data, including 2D/3D feature extraction;
- to exploit Cloud computing for faster processing and to provide access to large quantities of geodata.
The project, within it researches, events and secondments, wants to demonstrate and enlarge the benefits of geospatial data and technologies, investigating open issues, offering new processing solutions and unveiling how the geospatial field is evolving and bringing benefits to the society.
VOLTA includes 13 partners from research and academic institutions, geospatial companies and mapping agencies.
The project has achieved most of its objectives for the 1st reporting period with relatively minor deviations (less executed secondments with respect to planned). All WPs are running and performing their tasks within the executed secondments.
The main tasks and topics covered during the secondments focused on the following research topics:
- radiometric investigation for better image processing;
- improvement of tie point extraction;
- evaluation of existing open source and commercial photogrammetric solutions for 3D data generation;
- aerial triangulation with oblique datasets;
- improvement of 3D information extraction from imagery;
- multi-modal data merging for environmental applications;
- hyperspectral data combination for the classification of house roofs;
- LiDAR and multi-camera systems integration;
- merging of spatial data with vector GIS data;
- road junk modelling;
- semantic classification of images and orthoimages using machine learning algorithms;
- evaluation of point cloud classification algorithms in production environment;
- 3D building models generation from LiDAR point clouds;
- investigation of Cloud computing performances for geospatial applications.
- etc.
Some outcomes of the executed secondments lead to joint presentations and publications at international conferences. Non-academic partners involved in the project (i.e. geospatial companies and mapping agencies) benefited the networking and know-how exchange activities in order to be better aligned with state-of-the-art solutions (sensors and algorithms).
The main tasks and topics covered during the secondments focused on the following research topics:
- radiometric investigation for better image processing;
- improvement of tie point extraction;
- evaluation of existing open source and commercial photogrammetric solutions for 3D data generation;
- aerial triangulation with oblique datasets;
- improvement of 3D information extraction from imagery;
- multi-modal data merging for environmental applications;
- hyperspectral data combination for the classification of house roofs;
- LiDAR and multi-camera systems integration;
- merging of spatial data with vector GIS data;
- road junk modelling;
- semantic classification of images and orthoimages using machine learning algorithms;
- evaluation of point cloud classification algorithms in production environment;
- 3D building models generation from LiDAR point clouds;
- investigation of Cloud computing performances for geospatial applications.
- etc.
Some outcomes of the executed secondments lead to joint presentations and publications at international conferences. Non-academic partners involved in the project (i.e. geospatial companies and mapping agencies) benefited the networking and know-how exchange activities in order to be better aligned with state-of-the-art solutions (sensors and algorithms).
The project activities obtained interesting results beyond the actual state-of-the-art in the geospatial field.
Some outcomes lead to some joint presentations at conferences and publications in journals or conferences.
The main progresses include:
- advanced triangulation of oblique datasets (WP2);
- 3D information extraction from imagery (WP2);
- multi-modal / hyperspectral data merging for environmental applications (WP3);
- LiDAR and multi-camera systems integration for better 3D urban modeling (WP4);
- use of machine learning methods for semantic classification of images and point clouds (WP4);
- 3D building modeling of urban areas from dense point clouds (WP5);
Mapping agencies and geospatial companies involved in VOLTA benefited the R&D activities with the academic and research partners for their daily production line and could innovate some of their solutions.
Some outcomes lead to some joint presentations at conferences and publications in journals or conferences.
The main progresses include:
- advanced triangulation of oblique datasets (WP2);
- 3D information extraction from imagery (WP2);
- multi-modal / hyperspectral data merging for environmental applications (WP3);
- LiDAR and multi-camera systems integration for better 3D urban modeling (WP4);
- use of machine learning methods for semantic classification of images and point clouds (WP4);
- 3D building modeling of urban areas from dense point clouds (WP5);
Mapping agencies and geospatial companies involved in VOLTA benefited the R&D activities with the academic and research partners for their daily production line and could innovate some of their solutions.