title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð½ÐµÐ·Ð°ÑÐµÐ³Ð¸ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½ÐµÐ´Ð²Ð¸Ð¶Ð¸Ð¼Ð¾ÑÑÐ¸ Ð½Ð° Ð·ÐµÐ¼ÐµÐ»ÑÐ½ÑÑ ÑÑÐ°ÑÑÐºÐ°Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a program for detecting unregistered buildings on land plots
Creators: name: Ð ÑÐ±ÐºÐ¸Ð½, ÐÐ»ÐµÐºÑÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ñ
ÐºÐ°Ð´Ð°ÑÑÑ
Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð³ÐµÐ¾Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²ÐµÐ½Ð½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ
ÑÐ¿ÑÑÐ½Ð¸ÐºÐ¾Ð²ÑÐµ ÑÐ½Ð¸Ð¼ÐºÐ¸
ReFineNet
ÑÑÐµÑ Ð½ÐµÐ´Ð²Ð¸Ð¶Ð¸Ð¼Ð¾ÑÑÐ¸
automation
cadastre
artificial intelligence
machine learning
geospatial data
satellite images
real estate accounting

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description: The given work addresses an important task â automating the process of detecting unregistered real estate objects on land plots using artificial intelligence (AI) and machine learning technologies. This improves the efficiency of real estate accounting, which is critical for taxation and legal management of land resources. Objectives addressed during the study: 1. Collection and processing of geospatial data: Study of data sources such as satellite images and aerial photographs to obtain current information about land plots. 2. Development and training of a neural network: Creation of algorithms for analyzing and recognizing buildings based on processed images. 3. Integration with cadastral data: Comparing the analysis results with official registration information to identify unregistered objects. 4. Testing and validation: Verifying the accuracy and reliability of the developed system on real data. The development and implementation of a software product for the automated detection of unregistered real estate objects will optimize the accounting and management of land resources, increase the efficiency of taxation, and enhance legal clarity. The work uses the Python programming language and the RefineNet neural network architecture for semantic image segmentation. This allows precise identification and classification of real estate objects on land plots. The development includes the use of computer vision and machine learning methods to analyze geospatial data, automating the object detection process, reducing human involvement, and speeding up data processing. Results: 1. A software program was created that analyzes satellite and aerial images for the automated detection of unregistered buildings. 2. A neural network was developed and trained to recognize various types of buildings. 3. The program was successfully integrated with cadastral data to verify and identify unregistered objects. 4. Testing was conducted, showing high accuracy and reliability of the system, with first and second kind errors of less than 10%. The work demonstrates the effectiveness of applying artificial intelligence methods to solve real estate accounting tasks. The developed software contributes to the improvement of land resource management and increases transparency in the field of taxation and legal regulation of real estate.