Research On Matching And Quality Evaluation Of Crowd-source Buildings Data

With the continuous advancement of urbanization and the increasingly high level of urban development,people's demand for basic geographic information data is particularly urgent.At the same time,with the continuous improvement of science and technology,the way of obtaining data has been changing constantly.From the earliest relying on surveying and mapping personnel to conduct field surveys,it has gradually transformed into acquiring and updating geographic data through remote sensing images.However,limited by time cycle,acquisition cost and incomplete data sharing among different departments,the update cycle of surveying and mapping data is long.Besides,it also cannot effectively meet the needs of the economy and society.In recent years,with the development of the crowd-source data,geographic information data has been added.Among them,Open Street Map(OSM),as one of the most widely used data sources,has the advantages of timeliness,rich data types,and free sharing.Therefore,in view of the other data has the shortcomings of updated high cost,poor timeliness,research on matching and data quality problems between OSM data and other data,is a hot issue of the current study.This paper selects OSM building data as the data source,takes other building data as the reference,and studies the matching and data quality evaluation based on OSM building data.The main work content is as follows:(1)Aiming at the problem of low matching accuracy of global matching caused by the non-systematic deformation of OSM data,the concept of Urban Blocks was introduced.That is,taking the city block as the basic unit,the research regionalization as a whole is part.Meanwhile,the principal component analysis method is used to extract geometric similarity factors for buildings in each block,and a comprehensive index function is constructed to determine the matching relationship between buildings from different data sources,thereby improving the overall matching accuracy.(2)In view of the fact that the existing surface entity matching algorithm based on topology and spatial similarity cannot accurately describe the local characteristics of the surface entity due to the shape similarity factor,and the method of determining the candidate matching set is prone to miss matching.This paper first establishes a topological model for the initial matching and screening of surface entity matching,and then uses the spatial similarity factor to perform weighted synthesis to determine the final matching relationship,which can effectively solve the one-to-one and one-to-many matching problems.In the meantime,in the process of spatial similarity judgment,the multistage string length function is introduced to describe the shape,which can better identify the local features of complex surface entities.This paper introduces a method to quickly determine the combination of candidate matching surface entities to avoid the mismatching problem caused by position deviation.(3)The existing OSM buildings quality evaluation method only adopting a single shape factor has some limitations.In this paper,based on the coefficient of variation method,the geometric consistency of OSM surface data is evaluated by using the weighted average method and integrating the shape,position,size and other factors.Experiments show that the evaluation method is feasible and avoids the limitation of single factor.