Remote Sensing Data Preparation for Recognition and Classification of Building Roofs

Abstract

Buildings are among the most significant urban infrastructure that directly affects citizens’ livelihood. Knowledge about their rooftops is essential not only for implementing different Levels of Detail (LoD) in 3D city models but also for performing urban analyses related to usage potential (solar, green, social), construction assessment, maintenance, etc. At the same time, the more detailed information we have about the urban environment, the more adequate urban digital twins we can create. This paper proposes an approach for dataset preparation using an orthophoto with a resolution of 10 cm. The goal is to obtain roof images into separate GeoTIFFs categorised by type (flat, pitched, complex) in a way suitable for feeding rooftop classification models. Although the dataset is initially elaborated for rooftop classification, it can be applied to developing other deep-learning models related to roof recognition, segmentation, and usage potential estimation. The dataset consists of 3617 roofs covering the Lozenets district of Sofia, Bulgaria. During its preparation, the local-specific context is considered.

Keywords

• building rooftop classification
• roof dataset preparation
• orthophoto processing