Exploring different combinations of data and methods for urban land use analysis: a survey
Authors
Abstract
Modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information of land use and functional roles of the places that integrate urban areas. In the last years, driven by increased availability of geo-referenced data from social or embedded sensors and remote sensing (RS) images, various methods become popular for land use analysis. This paper addresses the various methods employed in this context, as well as needed data and respective categorization, best applications of each method, and their comparison. We focus on approaches based on data from RS images, open maps and various categories of crowdsourced data. We identify and discuss the way these approaches use Data Mining (DM) and Machine Learning (ML). From our initial study we concluded thateven using the same methods and the same kind of data, results depend on spatial configuration of the data, accordingly to the specificity of each region. The work described in this paper is intended to provide relevant contributions to method selection for knowledge discovery
for city planning and management, taking into consideration available data and the pros and cons of each technique.