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Aerial LiDAR Applications in Urban Environments

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (13 May 2022) | Viewed by 12317

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Special Issue Editors

Dr. Jesús Balado Frías

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Guest Editor

Geotech Group, CINTECX, University of Vigo, 36310 Vigo, Spain
Interests: LiDAR; point cloud; laser scanner; computational geometry; machine learning; classification; deep learning; physical accessibility
Special Issues, Collections and Topics in MDPI journals

Dr. Lucía Díaz-Vilariño

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Guest Editor

Applied Geotechnologies Research Group, Campus Universitario de Vigo, Universidade de Vigo, CINTECX, As Lagoas, Marcosende, 36310 Vigo, Spain
Interests: point cloud processing; 3D digital modeling; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A great deal of information is concentrated in urban environments concentrate as most of the population lives in cities and surroundings. The population is increasing so rapidly that managing and interpreting urban data is often difficult. Urban expansion is happening unevenly, and even when planned, various unexpected problems can arise that endanger the comfort of citizens.

LiDAR systems have been demonstrated to be a way of acquiring urban environments quickly and accurately. Aerial laser scanning technology—installed in UAVs, aircraft, or helicopters—can capture large areas without being limited to the movement of the laser scanner on the ground. The availability of aerial point clouds is also becoming increasingly common, with entire cities and countries already having been captured.

The use of aerial LiDAR still faces some difficulties. On one hand, aerial point clouds are commonly affected by occlusions, causing an incomplete representation of the urban environment. On the other hand, aerial point clouds are composed of a massive number of coordinates that are required from the development of automated processing methods to extract the useful information from the application they are intended to be used.

This Special Issue will collect recent advances in the use of aerial LiDAR in urban environment applications. We welcome submissions that cover but are not limited to the following specific topics:

UAV-based LiDAR acquisition
Aerial LiDAR data quality: analysis and correction
Correction of occlusions in point cloud: methods and algorithms
3D mathematical morphology applications
Advances in artificial intelligence for recognizing objects in aerial urban point clouds
Advances in urban reconstruction from aerial point clouds
Advances in LiDAR visualization: discrete and continuous level of detail
Direct applications of aerial point clouds
Applications of models obtained from aerial point clouds

Dr. Jesús Balado Frías
Dr. Lucía Díaz-Vilariño
Guest Editors

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Keywords

Aerial point clouds
LiDAR
Urban environments
Level of detail
Smart cities
Data fusion

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Published Papers (3 papers)

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Research

22 pages, 22857 KiB

Open AccessArticle

UAV-Based Multi-Sensor Data Fusion for Urban Land Cover Mapping Using a Deep Convolutional Neural Network

by Ahmed Elamin and Ahmed El-Rabbany

Remote Sens. 2022, 14(17), 4298; https://doi.org/10.3390/rs14174298 - 31 Aug 2022

Cited by 18 | Viewed by 3089

Abstract

Accurate and up-to-date land cover classification information is essential for many applications, such as land-use change detection, global environmental change, and forest management, among others. Unoccupied aerial systems (UASs) provide the advantage of flexible and rapid data acquisition at low cost compared to conventional platforms, such as satellite and airborne systems. UASs are often equipped with high spatial resolution cameras and/or light detection and ranging (LiDAR). However, the high spatial resolution imagery has a high information content, which makes land cover classification quite challenging. Recently, deep convolutional neural networks (DCNNs) have been effectively applied to remote sensing applications, which overcome the drawback of traditional techniques. In this research, a low-cost UAV-based multi-sensor data fusion model was developed for land cover classification based on a DCNN. For the purpose of this research, two datasets were collected at two different urban locations using two different UASs. A DCNN model, based on U-net with Resnet101 as a backbone, was used to train and test the fused image/LiDAR data. The maximum likelihood and support vector machine techniques were used as a reference for classifier comparison. It was shown that the proposed DCNN approach improved the overall accuracy of land cover classification for the first dataset by 15% compared to the reference classifiers. In addition, the overall accuracy of land cover classification improved by 7%, and the precision, recall, and F-measure improved by 18% when the fused image/LiDAR data were used compared to the images only. The trained DCNN model was also tested on the second dataset, and the obtained results were largely similar to those of the first dataset. Full article

(This article belongs to the Special Issue Aerial LiDAR Applications in Urban Environments)

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16 pages, 48529 KiB

Open AccessArticle

Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network

by Sani Success Ojogbane, Shattri Mansor, Bahareh Kalantar, Zailani Bin Khuzaimah, Helmi Zulhaidi Mohd Shafri and Naonori Ueda

Remote Sens. 2021, 13(23), 4803; https://doi.org/10.3390/rs13234803 - 26 Nov 2021

Cited by 6 | Viewed by 2726

Abstract

The detection of buildings in the city is essential in several geospatial domains and for decision-making regarding intelligence for city planning, tax collection, project management, revenue generation, and smart cities, among other areas. In the past, the classical approach used for building detection was by using the imagery and it entailed human–computer interaction, which was a daunting proposition. To tackle this task, a novel network based on an end-to-end deep learning framework is proposed to detect and classify buildings features. The proposed CNN has three parallel stream channels: the first is the high-resolution aerial imagery, while the second stream is the digital surface model (DSM). The third was fixed on extracting deep features using the fusion of channel one and channel two, respectively. Furthermore, the channel has eight group convolution blocks of 2D convolution with three max-pooling layers. The proposed model’s efficiency and dependability were tested on three different categories of complex urban building structures in the study area. Then, morphological operations were applied to the extracted building footprints to increase the uniformity of the building boundaries and produce improved building perimeters. Thus, our approach bridges a significant gap in detecting building objects in diverse environments; the overall accuracy (OA) and kappa coefficient of the proposed method are greater than 80% and 0.605, respectively. The findings support the proposed framework and methodologies’ efficacy and effectiveness at extracting buildings from complex environments. Full article

(This article belongs to the Special Issue Aerial LiDAR Applications in Urban Environments)

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36 pages, 3939 KiB

Open AccessArticle

Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping

by Yasmine Megahed, Ahmed Shaker and Wai Yeung Yan

Remote Sens. 2021, 13(4), 814; https://doi.org/10.3390/rs13040814 - 23 Feb 2021

Cited by 17 | Viewed by 4417

Abstract

The World Health Organization has reported that the number of worldwide urban residents is expected to reach 70% of the total world population by 2050. In the face of challenges brought about by the demographic transition, there is an urgent need to improve the accuracy of urban land-use mappings to more efficiently inform about urban planning processes. Decision-makers rely on accurate urban mappings to properly assess current plans and to develop new ones. This study investigates the effects of including conventional spectral signatures acquired by different sensors on the classification of airborne LiDAR (Light Detection and Ranging) point clouds using multiple feature spaces. The proposed method applied three machine learning algorithms—ML (Maximum Likelihood), SVM (Support Vector Machines), and MLP (Multilayer Perceptron Neural Network)—to classify LiDAR point clouds of a residential urban area after being geo-registered to aerial photos. The overall classification accuracy passed 97%, with height as the only geometric feature in the classifying space. Misclassifications occurred among different classes due to independent acquisition of aerial and LiDAR data as well as shadow and orthorectification problems from aerial images. Nevertheless, the outcomes are promising as they surpassed those achieved with large geometric feature spaces and are encouraging since the approach is computationally reasonable and integrates radiometric properties from affordable sensors. Full article

(This article belongs to the Special Issue Aerial LiDAR Applications in Urban Environments)

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