Processing airborne LiDAR point cloud for solar cadasters: A review

This paper reviews existing literature in the critical role of processing Lidar point cloud data for generating Digital Elevation Models (DEMs)— Digital Surface Models (DSMs) and Digital Terrain Models (DTMs)—to develop solar cadasters, which are essential for optimizing solar energy deployment in urban environments. With a primary focus on DSMs, due to their significant role in the development of solar cadasters, the paper evaluates the influence of various interpolation techniques used in the reviewed literature for generating DEMs from LiDAR point cloud data. The review examines how interpolation methods affect key factors like spatial resolution, elevation accuracy, and building edge preservation, and identifies the most efficient interpolation techniques for generating DSMs tailored for solar cadaster applications. In addition, the paper highlights emerging trends in applying Machine Learning (ML) to improve DSM generation, providing insights into how these techniques enhance model accuracy and classification. The findings offer a foundation for advancing solar PV rooftop assessments using solar cadasters, contributing to more sustainable and resilient urban energy planning. • The gap in the literature giving detailed impact of various interpolation techniques on LiDAR point cloud data to create DEM is covered. • Impact of DEM on solar cadasters is evaluated based on the literature. • The systematic definition and review of traditional and non-traditional interpolation techniques for DEM creation from the literature is reviewed. • The best Interpolation technique to create DSM raster for solar cadasters is acquired: TIN and Maximum value interpolation show effectiveness in overall performance for all the models. In addition, progressive interpolation technique outperforms other traditional techniques in its particular study on DSMs. • Influence of upcoming machine learning trends for the betterment of DSM creation is discussed.