Development of Machine Learning Algorithms Mapping Tender - 144444232

Procurement Summary

Country: Czech Republic

Summary: Development of Machine Learning Algorithms Mapping Oval Depressions from Lidar Data Indicating Natural Hydrogen Deposits

Deadline: 11 Jul 2026

Posting Date: 02 Jul 2026

Other Information

Notice Type: Tender

TOT Ref.No.: 144444232

Document Ref. No.: N006/26/V00020718

Competition: ICB

Financier: Self Financed

Purchaser Ownership: Public

Tender Value: Refer Document

CPV Classification

71354100 - Digital mapping services

Purchaser's Detail

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Tender Details

Found one supplier of IT services and expert geological services for the development of a machine learning (ML) algorithm for mapping depression waves from LIDAR data indicating aquifer fractures. Algorithms must identify closed and unclosed circular and wave-shaped morphological depressions slabs and hundreds of meters with an age difference of dm and meters, it must be able to process the entire land R in DMR ZABAGED 5G resolution, but it must also be able to process other digital relief models based on LIDAR or other data in GeoTIF or cloud point formats. The input of the algorithm must be a map all depressions in the harvest ground in vector and raster format (SHP, GeoTIF, etc.). The content of the contract is the preparation of basic data for machine learning mapping the occurrence of commercial vodka both in Russia and in Australia, the USA and other countries, including a review of professional literature and an evaluation geological situation. The content of the contract is based on the calibration of the model based on thermal names (the data will be provided by the client) and the creation of the final algorithm, including, in addition to the analysis of LIDAR data, the results of the analysis of vegetation stress, mineral alterations, thermal anomalies (where there are available; will still be the contracting authority). ML data preparation, modeling, calibration and creation of the final algorithm cannot be divided into long tasks, separate completion would violate the integrity of the algorithm. It's not about finding an arbitrary wave of depression, it's about finding a wave of depression with a bond to the vodka, on geological structures. The overall assessment of the geological structure of each locality is an integral part of mapping from remote data.
Current status of ZP: Not completed Division into parts: No ZP identifier on the contracting authority's profile: P26V00020718 Type of entry procedure: Open call Procurement specifications: open call Kind of: Public contract for services Date of publication of ZP on the profile: 07/01/2026 06:49 AM Code from the CPV code: 71354100 Title from the CPV Code: Digital Mapping Subject title: Digital mapping Text field for the description of the place of fulfillment: Code from the NIPEZ code: 71354100-5 Name from the NIPEZ code: Digital mapping Main place of fulfillment: Digital mapping

Documents

 Tender Notice

144444232.pdf