Possibilities of Self-Learning in Spatial Data Acquisition (Estonian Land Board)

This public procurement aims to develop data processing methodologies for the automatic collection of spatial data and the detection of changes within five data groups: changes in roads, changes in flowing water bodies, changes in buildings, building storeys, and building access points. The methodology will be created by combining data from the Estonian Land Board (Maa- ja Ruumiamet) and datasets from other institutions. The expected outcomes include prototype models whose suitability will be tested using use cases from the Rescue Board and the Building Register, as well as within the data acquisition processes of the Estonian Land Board. Information from market consultations preceding this procurement is available for download. The contract award date mentioned in the tender notice is indicative, with contract execution expected to commence upon signing. This initiative leverages self-learning (machine learning/AI) techniques to enhance the efficiency and accuracy of spatial data management and updates.

This is a services contract in the information and communication technology sector, with a focus on artificial intelligence. Located in Estonia, Europe, this opportunity is open to firms and consortiums.

Published through TED - Tenders Electronic Daily (EU), a national government procurement portal. Public procurement tenders follow the country's national bidding regulations and may have specific eligibility and documentation requirements for services in the information and communication technology sector. Service contracts are typically evaluated on both technical quality and price, and may require bidders to demonstrate relevant experience and qualified personnel. This contract has already been awarded. The information is published for transparency and market intelligence purposes.