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Open Access January 11, 2025 Endnote/Zotero/Mendeley (RIS) BibTeX

Exploring LiDAR Applications for Urban Feature Detection: Leveraging AI for Enhanced Feature Extraction from LiDAR Data

Abstract The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is [...] Read more.
The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.
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Open Access November 30, 2022 Endnote/Zotero/Mendeley (RIS) BibTeX

A Review of Application of LiDAR and Geospatial Modeling for Detection of Buildings Using Artificial Intelligence Approaches

Abstract Today, the presentation of a three-dimensional model of real-world features is very important and widely used and has attracted the attention of researchers in various fields, including surveying and spatial information systems, and those interested in the three-dimensional reconstruction of buildings. The building is the key part of the information in a three-dimensional city model, so extracting [...] Read more.
Today, the presentation of a three-dimensional model of real-world features is very important and widely used and has attracted the attention of researchers in various fields, including surveying and spatial information systems, and those interested in the three-dimensional reconstruction of buildings. The building is the key part of the information in a three-dimensional city model, so extracting and modeling buildings from remote sensing data is an important step in building a digital model of a city. LiDAR technology due to its ability to map in all three modes of one-dimensional, two-dimensional, and three-dimensional is a suitable solution to provide hyperspectral and comprehensive images of the building in an urban environment. In this review article, a comprehensive review of the methods used in identifying buildings from the past to the present and appropriate solutions for the future is discussed.
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Open Access February 17, 2022 Endnote/Zotero/Mendeley (RIS) BibTeX

A Geospatial Analysis Approach for Mapping and Ranking of Tourist Corridors in Mashhad Metropolis Iran

Abstract Mashhad is one of the important metropolitans in the northeast of Iran with over 25 million tourists per year. After evaluating the physical space of Mashhad in terms of semantics and identity with the aim of promoting tourism, 127 valuable places with cultural, historical, and religious values were identified, assessed, and analyzed using a Geographic Information Systems (GIS). Therefore, the [...] Read more.
Mashhad is one of the important metropolitans in the northeast of Iran with over 25 million tourists per year. After evaluating the physical space of Mashhad in terms of semantics and identity with the aim of promoting tourism, 127 valuable places with cultural, historical, and religious values were identified, assessed, and analyzed using a Geographic Information Systems (GIS). Therefore, the appropriate distribution of tourist routes for travelers to visit the city of Mashhad was done and 10 corridors and zones were selected. The basis of this choice was the existence of a valuable place at origin and destination of the routes and the existence of appropriate tourism, commercial, welfare and cultural infrastructure and ability to access various uses. Percentage and number of valuable places in each proposed route necessarily meant percentage of the total, excluding repetition in other routes.
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Keyword:   Alan Forghani

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