Clasificación espacial del suelo urbano por el valor especulativo del suelo e imágenes MSI satelitales usando K-MEANS, Huancayo, Perú

Abstract

The city of Huancayo, like other intermediate cities in Latin America, faces problems of poorly planned land-use changes and a rapid dynamic of the urban land market. The scarce and outdated information on the urban territory impedes the adequate classification of urban areas, limiting the form of its intervention. The purpose of this research was the adoption of unassisted and mixed methods for the spatial classification of urban areas, considering the speculative land value, the proportion of urbanized land, and other geospatial variables. Among the data collection media, Multi-Spectral Imagery (MSI) from the Sentinel-2 satellite, the primary road system, and a sample of direct observation points, were used. The processed data were incorporated into georeferenced maps, to which urban limits and official slopes were added. During data processing, the K-Means algorithm was used, together with other machine learning and assisted judgment methods. As a result, an objective classification of urban areas was obtained, which differs from the existing planning.

Spatiotemporal Evolution and Mechanisms of Polder Land Use in the “Water-Polder-Village” System: A Case Study of Gaochun District in Nanjing, China

“…Next, we applied the K-means algorithm, an unsupervised machine learning technique, to classify land use in the polder area using two years of data. Studies have shown that K-means is effective for image classification, urban spatial analysis, and land-use classification [28,29,34]. The land use categories considered in this study are shown in Figure 2.…”