Akademik Veri Yönetim Sistemi
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, cilt.1, sa.1, ss.1-30, 2026 (Hakemli Dergi)
Given the
growing number of applications in autonomous driving and urban digital twins,
the development of effective solutions for urban point cloud classification is
of extreme interest for the R&D community and commercial sector.
State-of-the-art neural networks commonly lack adequate cross-dataset
generalisation ability, mainly due to varying sensors and data collection
platforms, object shape differences, as well as the presence of
under-represented objects and imbalanced classes, especially with dense and
high-resolution reality-based 3D data. This work demonstrates how the recently
released ESTATE (A large dataset of under-represented urban objects) dataset
(https://github.com/3DOM-FBK/ESTATE), full of thousands of under-represented
urban objects such as traffic lights, electrical poles, pylons, and ventilation
units spread over 13 classes, can improve the performance of state-of-the-art
point cloud classification algorithms. Experiments with different neural
networks and several testing configurations with sensor-specific inputs
(coordinate, intensity, and colour) show the effectiveness of this dataset in
enhancing the classification capabilities and increasing cross-dataset
generalisation. Moreover, not only the adaptation of object classification
networks to the semantic segmentation pipeline is introduced, but also the
improvement of semantic segmentation performance by increasing the distribution
of under-represented classes with the ESTATE dataset. Thanks to 3D urban
objects from all around the world in the ESTATE dataset, the model’s
applicability for classifying an entirely different dataset is also
demonstrated.