Rapid advancements in technology and the evolution of new requirements in the fast-growing space applications market require continuous innovation and the adoption of new strategies. With Space 4.0, the space sector is entering a new era revolutionising the design, production and management of space products. New digital technologies for data analytics and smart integrated services are among the latest technical challenges.
GeoVille successfully manages to lead (and/or adapt to) innovation processes through substantial resources allocated to in-house competence building. Moreover, GeoVille conducts research and innovation in the frame of national and international scientific activities, constantly integrating new technologies, widening the field of innovative applications and enhancing connectivity with other sectors.
Please find below a selection of our latest innovations and ongoing R&D initiatives:
The smarticipate platform will make open data available to citizens in an understandable format. By doing so, it will transform open data from a little used resource to a vital tool to plan the future of a city.
Through the platform, users will be able to see proposed urban planning changes on a 2D or 3D map of their city. If the user has an idea to improve the proposal, they can make the change directly, observing their alterations in real time. Other users can also see the new proposal and comment on it.
If potential changes violate any legal or policy barriers, the intelligent system will inform the user and gives detailed reasons based on the data provided. In addition to making changes to urban design, citizens will be able to feed in data from their own locality, improving data sets.
The LandSense Citizen Observatory aims to aggregate innovative EO technologies, mobile devices, community-based environmental monitoring, data collection, interpretation and information delivery systems to empower communities to monitor and report on their environment. A key component of the project is the LandSense Engagement Platform. Various communities will be able to actively participate within the LandSense engagement platform through a variety of interactive tools and functions to facilitate information transfer, assessment, valuation, uptake and exploitation of environmental data and results. The platform will offer collaborative mapping functionalities to allow citizens to view, analyze and share data collected from different campaigns and create their own maps, individually and collaboratively. In addition, citizens can participate in ongoing LandSense demonstration cases using their own devices (e.g. mobile phones and tablets), through interactive reporting and gaming applications, as well as launching their own campaigns.
EO VAS aims to reshape the Earth observation value chain by breaking “downstream” component and significantly simplifying development and delivery of Earth observation adding value services.
The main technological novelty lies in a cost effective approach that uses resources only when somebody is asking for results, thus making the services more affordable. Functional novelty is expressed through its connecting characteristics - connecting end-user communities with developers, who can build their services on top of initial toolset and commercialize them through the platform without the need for large investments in processing and storage infrastructure or data processing developments.
Information markets in the sectors of agriculture, forestry, water and environmental management require quantitative measurements of vegetation water content and biomass, all of which are not currently available as operational services on required scales. VegetationDynamics4.0 aims to investigate the capacity of both Sentinel-1 and Sentinel-2 data for the monitoring of vegetation water content, enabled through an unparalleled global validation effort and rigorous scientific schedule. Highly innovative methods will be employed aiming at 1) establishment of situ measurements within a global processing system framework and 2) development of empirical models relating multi-sensor VWC indices across heterogeneous sites world-wide. Finally, the relationship between S-1 CR data, S-2 vegetation indices and derived land cover, Vegetation Optical Depth (VOD) estimates from Advanced Scatterometer (ASCAT) and quantitative vegetation variables (height, water content, and biomass) will be analysed from field to regional scale.