Abstract

Rapid changes that are taking place in the urban environment have significant impact on urban growth. Most cities and urban regions all over the world compete to increase resident and visitor satisfaction. The growing requirements and rapidity of introducing new technologies to all aspects of residents' lives force cities and urban regions to implement "smart cities" concepts in their activities. Real estate is one of the principal anthropogenic components of urban environment thus become a subject of thorough multidisciplinary analysis in the field of data requiring spatial information systems. Recent advances in information technology, combined with the increased availability of high-resolution imagery from Earth observation, create an opportunity to use new sources of data that enable to identify, monitor, and solved many of urban environmental problem. The aim of the paper is to elaborate precise, complete and detailed property information with the use of remote sensing observations in a suitable numerical algorithm. The authors concentrate on providing one of the most important, and probably the most lacking, feature describing properties - building usable floor area (BUFA). The solution is elaborated in the form of an automatic algorithm based on machine learning and computer vision technology related to LiDAR (big data), close range images with respect to spatial information systems requirements. The obtained results related to BUFA estimation in comparison to the state-of-the-art results are satisfactory and may increase the reliability of decision-making in investment, fiscal, registration and planning aspects.

Citations

Authors (4)