Enhancing Aerial Data Semantic Segmentation with a Colour Range Mask Layer: A Deep Learning Approach

Recent advancements in airborne platforms equipped with ultra-high-resolution imaging sensors have significantly improved our capability to acquire detailed urban optical imagery.These systems offer exceptional capabilities for capturing highly precise and detailed urban data, paving the way for the generation of high-definition maps (HD maps) for innovative urban applications.However, manually extracting information from this data is a generally slow and labour-intensive process.Thus, employing deep learning algorithms for data extraction in such a context might be an alternative solution.Deep learning has revolutionised and transformed remote sensing and image analysis, especially in semantic segmentation, which divides images into meaningful regions.This transformative power of deep learning is particularly significant in urban analysis (e.g., urban planning, navigation, disaster management, and monitoring infrastructure), where detailed spatial information is crucial.Even though deep learning offers excellent potential, applying deep learning for semantic segmentation of images from urban environments presents several challenges.First, supervised deep learning algorithms require many training data to work effectively.Second, training and analysing ultrahigh-resolution (less than 5 cm) images with deep learning algorithms need large storage capacity, are computationally intensive and often require advanced data augmentation, pre-processing, and model optimisation techniques to achieve optimal results Zhu et al., (2017).