A Hyperspectral and RGB Dataset for Building Facade Segmentation
This work provides a new dataset for building facade segmentation, addressing a domain-specific need in light industry environments, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of classifying building materials by introducing the LIB-HSI dataset, which includes hyperspectral and RGB images with 44 classes across nine categories, and they applied deep learning semantic segmentation algorithms to achieve this classification.
Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications. This work introduces an HSI dataset of building facades in a light industry environment with the aim of classifying different building materials in a scene. The dataset is called the Light Industrial Building HSI (LIB-HSI) dataset. This dataset consists of nine categories and 44 classes. In this study, we investigated deep learning based semantic segmentation algorithms on RGB and hyperspectral images to classify various building materials, such as timber, brick and concrete.