Thiruvananthapuram Aerial LiDAR Dataset (TALD): A Benchmark for Complex Urban Point Cloud

Abstract

Urban heterogeneity is influenced by population density, cultural factors, and historical background. Three-dimensional mapping using LiDAR is essential for capturing structural and spatial changes in complex urban areas. Machine learning-based algorithms for processing point clouds play vital role in transforming unprocessed LiDAR data into relevant information suitable for urban applications such as object recognition, and 3D mapping. This underscores the importance of having range of benchmark LiDAR datasets to enhance development, testing, and validation of algorithms tailored to various urban contexts. However, existing Airborne LiDAR Scanned (ALS) datasets represent a limited range of global land cover diversity. To address this gap, we introduce Thiruvananthapuram Aerial LiDAR Dataset (TALD), a benchmark dataset covering 9 square kilometers from Thiruvananthapuram, Kerala, India. This South Indian region exhibits high-density mixed urban development integrating both built and vegetative elements. TALD, derived from ALS point clouds, has an average point density of 12 points/m2 and includes colored LiDAR points classified into buildings, trees, shrubs, and ground. The dataset is created through systematic pre-processing, classification using automated algorithms and manual corrections, and instance segmentation for noise removal. It includes X, Y, Z coordinates (UTM 43N), RGB values, return number, number of returns, scan angle rank, and class designation. The Land-cover Diversity Index (LDI) is 1.49 for TALD, significantly higher than DALES (0.23) and ISPRS Vaihingen 3D (0.37), highlighting its focus on tropical urban environments with dense vegetation and complex infrastructure. TALD serves as a valuable benchmark for advancing point cloud processing, supporting urban mapping in challenging landscapes.