Conference Agenda

Smoothing aerial LiDAR point clouds is challenging, because they are often noisy, irregularly sampled, and sparse, as well as their inherent high degrees of freedom. Classic methods struggle on such datasets as they were designed for regularly sampled, dense datasets with moderate noise. To address the challenge, this paper proposes a constrained point cloud model with one degree of freedom. The point cloud model incorporates the sensing directions stored in the full waveform LiDAR datasets, and has theoretical advantages in terms of the statistical error bound for normal estimation. Based on the point cloud model, the directional total least square is formulated as a regularized convex optimization problem for points estimation on a tangent plane. Moreover, a non-convex regularizer along with the non-convex regularized directional total least square is proposed to improve the estimation

quality. To solve the proposed optimization problems, an accelerated Douglas-Rachford splitting algorithm is introduced. The proposed methods demonstrate better performances on simulated two-dimensional point clouds in terms of improved root-mean square-

error. For three-dimensional aerial LiDAR point clouds, implemented under the Savitzky-Golay filter framework with local

smoothness prior, the proposed methods demonstrate more smoothing power and robustness than the classic method.

Urban mapping for planning and monitoring requires high-resolution spatial data, especially in areas with high landcover diversity. Airborne LiDAR Scanning (ALS) provides accurate 3D point cloud data, but its classification remains challenging due to computational complexity, irregular point distribution, noise, mislabeling and outliers in the dataset. These challenges are amplified in dense urban environments with mixed vegetation and infrastructure. Existing local context-based classification methods consider all points equally, overlooking the impact of their spatial position of the point in the dataset. To address this, we propose a dynamic local context-based point confidence-based optimization that improves classification accuracy by leveraging the spatial context of each point. This approach selects points based on confidence levels derived from position indices in training data and predicted by binary classifiers in test data to enhance robustness of classifier. We evaluated the proposed approach using boosting-based machine learning classifiers on two datasets: Thiruvananthapuram Aerial LiDAR Dataset (TALD) from India and the ISPRS 3D semantic labeling dataset from Vaihingen, Germany. The results showed 90.3% accuracy on TALD and 90.0% on Vaihingen, achieving a 2-4% improvement over conventional local context-based classification.

Accurate interpretation and segmentation of 3D point clouds in real-world urban environments is a critical challenge in geospatial analysis, particularly due to the complexity of real-world scenes, inevitable data uncertainties, and potential annotation errors. This paper proposes a confidence-aware deep learning framework to refine the segmentation accuracy of real-world point cloud data. By incorporating multi-source information, such as aerial imagery, and embedding geospatial prior knowledge, this framework models data uncertainty through point-wise confidence scores. Besides, we design an iterative online learning strategy, allowing the network to improve both its predictions and the quality of training labels. Extensive experiments on large-scale airborne laser-scanned data demonstrate that our framework effectively enhances training data by reducing label noise and improving annotation quality, which leads to more robust, generalizable model performance. Our source code is publicly available at https://github.com/AutumnMoon00/RefineNet.

LiDAR sensors incorporated within modern smartphone and tablet devices enable relatively quick and inexpensive collection of ground-based LiDAR data applicable for ground truth mapping as needed for modelling understory vegetation. However, this LiDAR data often requires conversion and processing prior to research use. This study presents a workflow with algorithms utilizing structured query language (SQL) to efficiently process detailed rasterized features from LiDAR data collected by an iPhone Pro Max via the ForestScanner app. After transformation of the LiDAR data, SQL has been employed to voxelize the LiDAR data from which rasterized features have been derived. Various cell sizes for voxels and subsequent pixels have been investigated, leading to a recommended spatial resolution of 0.05 m for cell size dimension. SQL provides precise control for advanced querying to process ground-based LiDAR data for vegetational modelling applications.

In the era of information revolution, with data being the fuel of AI and analytics, efficient information extraction from LiDAR point clouds becomes indispensable for solving real-world problems and aiding decision-making in geospatial domain. Despite having geometric richness, the massive LiDAR point clouds are not only computationally demanding but also lack inherent semantics. The lack of semantics in LiDAR constrains effective data analysis. This paper presents a novel workflow by incorporating Deep Learning derived embeddings as attributes in the geospatial database for the spatio-semantic querying on Aerial LiDAR point clouds. This work leverages AI-based indexing, such as IVFFlat(Inverted File Index with Flat Quantization) on LiDAR point clouds for fast retrieval of queries. The pgPointCloud and pgVector extensions of PostgreSQL aid in importing point clouds into the database and performing similarity-based query retrieval on the embedding space of the point clouds. The methodology developed in this paper explores how semantic embeddings can handle inadequate semantics of point clouds by enabling direct and complex 3D intelligent queries within the database environment, thereby overcoming the limitations of traditional LiDAR representations. Few queries presented in this paper highlight the applications of this proposed framework in individual tree detection, tree species identification, utility management, urban planning and anomaly detection.

The development of global geographic information resource products involves massive information processing of PB-level multimodal spatiotemporal data, and faces technical challenges brought by the global scale. In response to the challenges, we have made technological innovations to break through the key technologies for the development of global geographic information data products. With the main themes of "intelligent interpretation of typical elements, multi-source geographic data mining, and intelligent hybrid compilation", we have conducted and completed the overall technical research on the construction of global geographic information resources, formed an autonomous construction capability. Firstly, through crowd-sourced data mining and fusion technology to achieve content information extraction and knowledge fusion; Secondly, using multiple source data features, fast automatic extraction and integration of elements based on deep learning models was processed, and produce digital line graph data based on intelligent hybrid compilation. Based on the automatic feature extraction technology of deep learning, the production of digital line graph data products has been updated, and the accuracy evaluation has reached over 85%.