Abstract:
The mass appearance of illegal and unregistered in the Unified
State Register of Real Estate (USRRE) real estate objects complicates cadastral
registration for many entities at the territorial and administrative levels.
Traditional methods of identifying objects of this type, based on manual analysis
of geospatial data, are labor-intensive and time-consuming.
To improve the efficiency of this process, it is proposed to automate the
detection of objects in aerial photographs by solving the instance segmentation
problem using the Mask R-CNN deep learning model. The article describes the
preparation of a dataset for this model, examines the main quality metrics,
and analyzes the results obtained. The efficiency of the Mask R-CNN model
in practice is shown for solving the problem of detecting construction projects
that are not registered in the USRRE. (Linked article texts in English and in
Russian).
Key words and phrases:Cadastral registration, aerial photography analysis, instance segmentation, Mask R-CNN, PyTorch.
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