Identifying Buildings Without Permits in Coastal Montenegro Using Deep Learning and Land Administration Modeling

In Montenegro, the registration of buildings constructed without permits largely depends on voluntary reporting by property owners, leading to incomplete and predominantly non-spatial records. Rapid urbanization, particularly in coastal areas, has increased the discrepancy between reported and unreported buildings without permits, challenging the effectiveness of existing land administration and cadastral systems. This study develops and evaluates an integrated geospatial framework for identifying unreported buildings without permits and incorporating them systematically into land administration workflows. The approach combines the analysis of high-resolution satellite and orthophoto imagery with the machine learning-based extraction of building structures (semantic and instance segmentation), which are then linked to existing cadastral and administrative records. The framework enables the identification of buildings that are unreported, previously reported cases, and complex situations requiring further verification, providing a basis for targeted administrative inspection. Additionally, the study proposes extending the current LADM (land administration domain model) country profile for Montenegro to accommodate automatically detected structures, their legal status, and associated data reliability. The results suggest that combining geospatial analysis with enhanced administrative modeling can support a more comprehensive, transparent, and data-driven management of buildings without construction permits, particularly in densely urbanized coastal regions. This approach demonstrates a practical pathway for improving the monitoring and documentation of unreported buildings in contexts where conventional reporting is incomplete or inconsistent.