Airborne and Spaceborne Hyperspectral Remote Sensing in Urban Areas: Methods, Applications, and Trends

This study provides a comprehensive and systematic review of hyperspectral remote sensing in urban areas, with a focus on the evolving roles of airborne and spaceborne platforms. The main objective is to assess the state of the art and identify current trends, challenges, and opportunities arising from the scientific literature (the gray literature was intentionally not included). Despite the proven potential of hyperspectral imaging to discriminate between urban materials with high spectral similarity, its application in urban environments remains underexplored compared to natural settings. A systematic review of 1081 peer-reviewed articles published between 1993 and 2024 was conducted using the Scopus database, resulting in 113 selected publications. Articles were categorized by scope (application, method development, review), sensor type, image processing technique, and target application. Key methods include Spectral Unmixing, Machine Learning (ML) approaches such as Support Vector Machines and Random Forests, and Deep Learning (DL) models like Convolutional Neural Networks. The review reveals a historical reliance on airborne data due to their higher spatial resolution and the availability of benchmark datasets, while the use of spaceborne data has increased notably in recent years. Major urban applications identified include land cover classification, impervious surface detection, urban vegetation mapping, and Local Climate Zone analysis. However, limitations such as lack of training data and underutilization of data fusion techniques persist. ML methods currently dominate due to their robustness with small datasets, while DL adoption is growing but remains constrained by data and computational demands. This review highlights the growing maturity of hyperspectral remote sensing in urban studies and its potential for sustainable urban planning, environmental monitoring, and climate adaptation. Continued improvements in satellite missions and data accessibility will be key to transitioning from theoretical research to operational applications.