Deep learning techniques have been widely applied to classify tree species and segment tree structures. However, most recent studies have focused on the canopy and trunk segmentation, neglecting the branch segmentation. In this study, we proposed a new approach involving the use of the PointNet++ model for segmenting the canopy, trunk, and branches of trees. We introduced a preprocessing method for training LiDAR point cloud data specific to trees and identified an optimal learning environment for the PointNet++ model. We created two learning environments with varying numbers of representative points (between 2048 and 8192) for the PointNet++ model. To validate the performance of our approach, we empirically evaluated the model using LiDAR point cloud data obtained from 435 tree samples scanned by terrestrial LiDAR. These tree samples comprised Korean red pine, Korean pine, and Japanese larch species. When segmenting the canopy, trunk, and branches using the PointNet++ model, we found that resampling 25,000–30,000 points was suitable. The best performance was achieved when the number of representative points was set to 4096.

The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in mapping the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field.

The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.

A top‐ k spatial keyword (T k Sk) query ranks objects based on the distance to the query location and textual relevance to the query keywords. Several solutions have been proposed for top‐ k spatial keyword queries. However, most of the studies focus on Euclidean space or only investigate the snapshot queries where both the query and data object are static. A few algorithms study T k Sk queries in undirected road networks where each edge is undirected and the distance between two points is the length of the shortest path connecting them. However, T k Sk queries have not been thoroughly investigated in directed and dynamic spatial networks where each edge has a particular orientation and its weight changes according to the traffic conditions. Therefore, in this study, we address this problem by presenting a new method, called COSK, for processing continuous top‐k spatial keyword queries for moving queries in directed and dynamic road networks. We first propose an efficient framework to process snapshot T k SK queries. Furthermore, we propose a safe‐exit‐based approach to monitor the validity of the results for moving T k SK queries. Our experimental results demonstrate that COSK significantly outperforms existing techniques in terms of query processing time and communication cost.

Top-k spatial preference queries rank objects based on the score of feature objects in their spatial neighborhood. Top-k preference queries are crucial for a wide range of location based services such as hotel browsing and apartment searching. In recent years, a lot of research has been conducted on processing of top-k spatial preference queries in Euclidean space. While few algorithms study top-k preference queries in road networks, they all focus on undirected road networks. In this paper, we investigate the problem of processing the top-k spatial preference queries in directed road networks where each road segment has a particular orientation. Computation of data object scores requires examining the scores of each feature object in its spatial neighborhood. This may cause the computational delay, thus resulting in a high query processing time. In this paper, we address this problem by proposing a pruning and grouping of feature objects to reduce the number of feature objects. Furthermore, we present an efficient algorithm called TOPS that can process top-k spatial preference queries in directed road networks. Experimental results indicate that our algorithm significantly reduces the query processing time compared to period solution for a wide range of problem settings.

Advances in mobile technologies and map-based applications enables users to utilize sophisticated spatial queries, including ${k}$ -nearest neighbor and shortest path queries. Often, location-based servers are used to handle multiple simultaneous queries because of the popularity of map-based applications. This study focuses on the efficient processing of multiple concurrent ${k}$ -farthest neighbor ( ${k}$ FN) queries in road networks. For a positive integer $k$ , query point $q$ , and set of data points $P$ , a ${k}$ FN query returns $k$ data points farthest from the query point $q$ . For addressing multiple concurrent spatial queries, traditional location-based servers based on one-query-at-a-time processing are unsuitable owing to high redundant computation costs. Therefore, we propose a group processing of multiple ${k}$ FN (GMP) algorithm to process multiple ${k}$ FN queries in road networks. The proposed GMP algorithm uses group computation to avoid the redundant computation of network distances between the query and data points. The experiments using real-world roadmaps demonstrate the proposed solution’s effectiveness and efficiency.