Interpreting Built-Up Areas on Sentinel-2 Imagery and Recognizing Slums in Semarang City

Abstract

This research examines the interpretation of built-up areas on Sentinel-2 imagery using interpretation keys and recognizes the appearance of slums from remote sensing data. The use of interpretation keys in visual interpretation is the first step for interpreters to recognize objects in the image. The research objectives were: 1) to interpret built-up areas; 2) to recognize slum conditions based on visual interpretation of sentinel-2 imagery and; 3) to determine the affordability of remote sensing data for slum parameters. The method used is a visual interpretation that is carried out in stages on the classification of built-up areas and settlements. The introduction of slum conditions is done by object interpretation in slum delineation. The result of this study is the mapping of built-up areas in Semarang City from the visual interpretation method obtained an accuracy of 87.5%. The effective use of interpretation keys at the land cover interpretation level are shape, pattern, size, and color. The condition of slums with remote sensing is visually depicted on the key interpretation of shape and pattern. The affordability of remote sensing data for slum parameters has limitations. The ability to extract remotely sensed data for slum parameters can help narrow down field studies in assessing slum parameters.