| Abstrakt: |
The fusion of LiDAR and visual image data reshapes urban landscape analysis, underpinning applications in urban planning, infrastructure development, and environmental monitoring. This paper delves into the pivotal realm of combined LiDAR and visual image data preprocessing, which lays the foundation for accurate and meaningful urban landscape analysis. In this study, we explore the intricate process of harmonizing LiDAR's precise 3D geometry with the rich visual context offered by images. By devising methodologies for seamless data fusion, we navigate the challenges of coordinate alignment, calibration, and feature extraction. The calibrated integration yields a robust dataset for advanced analysis. Our research highlights the transformative potential of this preprocessing strategy, presenting novel findings that include the development of a GFLN (Geometric Feature Learning Network) model for addressing challenges posed by unstructured LiDAR point clouds. Acting as a quality control mechanism, the GFLN model further advances the field by enhancing the accuracy and reliability of urban landscape analysis. Our proposed GNLN method performs better than previous methods by achieving a mAcc of 79.19%. GFLN also took significantly less time to train, with only 145 s. From 3D feature extraction to urban change detection, this framework empowers diverse analytical avenues. Leveraging the synergy of LiDAR and visual data, this study invites practitioners and researchers to embrace an enriched toolkit for holistic urban landscape analysis. [ABSTRACT FROM AUTHOR] |
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