{"id":11887,"date":"2025-06-16T13:58:21","date_gmt":"2025-06-16T11:58:21","guid":{"rendered":"https:\/\/edihadria.eu\/?p=11887"},"modified":"2025-06-16T13:58:21","modified_gmt":"2025-06-16T11:58:21","slug":"digitalna-transformacija-u-upravljanju-prostorom-infrastrukturom-ai-rjesenje-za-geogrupa-d-o-o-uz-podrsku-edih-adria","status":"publish","type":"post","link":"https:\/\/edihadria.eu\/en\/digitalna-transformacija-u-upravljanju-prostorom-infrastrukturom-ai-rjesenje-za-geogrupa-d-o-o-uz-podrsku-edih-adria\/","title":{"rendered":"Digital transformation in space infrastructure management: AI solution for Geogrupa d.o.o. with the support of EDIH Adria"},"content":{"rendered":"

Challenges<\/strong><\/p>\n

Geogrupa d.o.o. has recognized the possibility of using artificial intelligence to identify and update data on traffic signs and public lighting poles. The mapping of these infrastructure elements has traditionally relied on field visits, manual records and data transmission, which has been a lengthy work, often resulting in uneven accuracy and increased operational costs. Given the growing demand for more accurate and faster management of municipal infrastructure, there was a need to improve and digitize this process with the aim of reducing the time needed to perform these activities, and reducing the cost.<\/p>\n

Solution<\/strong><\/p>\n

Through the \u2018Test Before Invest\u2019 (TBI) service and with the professional support of the EDIH Adria consortium, Geogroup tested the potential of using artificial intelligence (AI) and computer vision models to address these problems. The aim was to test whether AI and computer vision models can be applied in order to automate and digitize the process of locating infrastructure elements with the aim of relieving employees and speeding up the process.<\/p>\n

As part of the TBI activity, AI machine learning models were tested for the detection of communal infrastructure elements:<\/p>\n

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  1. Traffic sign detection:<\/strong> Using models based on YOLO (You Only Look Once) architecture, a prototype system for automatic recognition and classification of traffic signs from street scene images was developed. The system enables fast and precise marking of signs in space, with the additional possibility of categorizing identified traffic signs (e.g. stop, right of advantage, dead end, etc.). The main goal was to automate the process of identifying the coordinates of traffic signs and their categorization and cataloguing.<\/li>\n
  2. Detection of public lighting poles:<\/strong> In the same technological framework, an approach for the identification of public lighting poles has been tested. Models were trained on a representative sample of data from a dozen shots of street scenes made by a rectangular camera with data on camera coordinates and direction of movement, using manually marked examples as a basis for training and validation of the model.<\/li>\n<\/ol>\n

    The system prototype, developed as part of the project, enables the visualization of detections through a simple and clear user interface created using Streamlit and Leaflet OpenStreetMap mapping tools. The system has shown high precision at the test set, and the results suggest a clear potential for integration into existing infrastructure detection processes in public areas or as a potential for offering additional services organized by Geogrupa d.o.o .<\/p>\n

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    Figure 1 \u2013 Display of interactive map with detected traffic signs and public lighting poles<\/em><\/p>\n

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    Figure 2 \u2013 Table of object detections with information on coordinates, class and number of images on the basis of which coordinates were calculated<\/em><\/p>\n

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    Figure 3 \u2013 Illustration of details of up to three images excluded from the videos on the basis of which object coordinates were calculated<\/em><\/p>\n

    Results and Benefits<\/strong><\/p>\n

    Testing conducted as part of TBI activities has shown great potential for the application of AI technologies in the processing of street imagery and the management of infrastructure elements, enabling:<\/p>\n