Michael A. Chapman

TL;DR: An end-to-end spectral–spatial residual network that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification and achieves the state-of-the-art HSI classification accuracy in agricultural, rural–urban, and urban data sets.

TL;DR: Wang et al. as mentioned in this paper proposed a curb-based method for road surface extraction from mobile laser scanning (MLS) point clouds, which first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation difference thresholds.

TL;DR: The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.

TL;DR: This paper describes the development of automated algorithms for extracting road features (road surfaces, road markings, and pavement cracks) from MLS point cloud data and concludes that MLS is a reliable and cost-effective alternative for rapid road inspection.

TL;DR: Using high-spatial-resolution multispectral imagery alone is insufficient for achieving highly accurate and reliable thematic mapping of urban areas, so current advances in object-based image analysis and machine learning algorithms are taken to reduce manual image interpretation and automate feature selection in a classification process.