Numer czasopisma  

Geomatics and Environmental Engineering

ISSN: 1898-1135

Data wydania
Vol. 18
No. 3
Prawa dostępu
Dostęp: otwarty dostęp
Prawa: CC BY 4.0
Attribution 4.0 International
Uznanie autorstwa 4.0 Międzynarodowe (CC BY 4.0)

Rocznik czasopisma (rel.)
Rocznik czasopisma
Geomatics and Environmental Engineering
Vol. 18 (2024)
Artykuły numeru (rel.)
Otwarty dostęp
Error Analysis of Stonex X300 Laser Scanner Close-range Measurements
(Wydawnictwa AGH, 2024) Abed, Fanar M.; Jasim, Luma K.; BorI, Marwa M.
This research reports an error analysis of close-range measurements from a Stonex X300 laser scanner in order to address range uncertainty behavior based on indoor experiments under fixed environmental conditions. The analysis includes procedures for estimating the precision and accuracy of the observational errors estimated from the Stonex X300 observations and conducted at intervals of 5 m within a range of 5 to 30 m. The laser 3D point cloud data of the individual scans is analyzed following a roughness analysis prior to the implementation of a Levenberg–Marquardt iterative closest points (LM-ICP) registration. This leads to identifying the level of roughness that was encountered due to the range-finder’s limitations in close-ranging as well as measurements that were obtained from extreme incident angle signals. The measurements were processed using a statistical outlier removal (SOR) filter to reduce the noise impact toward a smoother data set. The geometric differences and the RMSE values in the 3D coordinate directions were computed and analyzed, which showed the potential of the Stonex X300 measurements in close-ranging following a careful statistical analysis. It was found that the error differences in the vertical direction had a consistent behavior when the range increased, whereas the errors in the horizontal direction varied. However, it is more common to produce errors in the vertical direction as compared to the horizontal one.
Otwarty dostęp
Building Semantic Segmentation Using UNet Convolutional Network on SpaceNet Public Data Sets for Monitoring Surrounding Area of Chan Chan (Peru)
(Wydawnictwa AGH, 2024) Chicchon, Miguel; Malinverni, Eva Savina; Sanità, Marsia; Pierdicca, Roberto; Colosi, Francesca; Trujillo, Francisco James León
The amount of damage to cultural heritage sites is increasing rapidly every year. This is due to inadequate heritage management and uncontrolled urban growth as well as unpredictable seismic and atmospheric events that manifest themselves in a continuously deteriorating ecosystem. Thus, applications of artificial intelligence (AI) in remote-sensing (RS) techniques (machine-learning and deep-learning algorithms) for monitoring archaeological sites have increased in recent years. This research involves the surrounding area of the archaeological site of Chan Chan in Peru in particular. An approach that is based on the use of AI algorithms for building footprint segmentation and change-detection analysis by means of RS images is proposed. It involves a UNet convolutional network based on an EfficientNet B0 to B7 encoder. The network was trained on two public data sets from SpaceNet that were based on WV2 and WV3 satellite images: SpaceNet V1 (Rio), and SpaceNet V2 (Shanghai). In the pre-processing phase, the images from the two data sets have been equalized in order to improve their quality and avoid overfitting. The building segmentation has been performed on HRV images of the study area that were downloaded from Google Earth Pro. The value that was achieved in the IoU metric was around 70% in both experiments. The purpose of this proposed methodology is to assist scientists in drafting monitoring and conservation protocols based on already-recorded data in order to prevent future disasters and hazards.
Otwarty dostęp
Assessing Potential Distributions of Bird Endemic Species: Case Studies of Macrocephalon maleo and Rhyticeros cassidix and Their Threats
(Wydawnictwa AGH, 2024) Aldiansyah, Septianto; Risna; Saputra, Randi Adrian
Maleo and knobbed hornbill are bird species that are endemic on the island of Sulawesi, which is highly threatened by forest fires. Fires tend to destroy any affected species, however, it is not possible to survey the entire range of the original distribution of the two endemic bird species that are affected by forest fires due to practical constraints. Species distribution modeling using maximum entropy is considered to be an alternative to understanding the potential distribution area of species against the threat of forest fires. The prediction model from MaxEnt all have AUC values of greater than 0.70, which means that the model is good enough to classify the records of the presence of M. maleo and R. cassidix along with the past forest fires. The environmental variables that affect the distribution of M. maleo are its distance from hot water, rivers, and roads, while the distribution of R. cassidix is strongly influenced by its distance from roads, settlements, and rivers. Forest fire distribution is mostly influenced by soil type, land-use land cover, and rainfall. It is predicted that around 238,690 and 677,070 ha of the potential distribution of M. maleo and R. cassidix, respectively, are potentially disturbed and affected by forest fires. However, this number much greater outside conservation areas. The results of this study can be used by the government of the Republic of Indonesia (especially the Ministry of Environment and Forestry) for determining conservation actions for both species in the future.
Otwarty dostęp
Geomatics-enabled Interdisciplinary Approach Based on Geospatial Data Processing for Hydrogeological Risk-analysis
(Wydawnictwa AGH, 2024) Di Stefano, Francesco; Chiappini, Stefano; Sanità, Marsia; Pierdicca, Roberto; Malinverni, Eva Savina
Hydrogeological risks that are associated with rivers have emerged as a significant concern worldwide, impacting both natural ecosystems and human settlements. This contribution presents an interdisciplinary project that leverages many technologies for data-acquisition and modeling to comprehensively analyze and manage risks in riverine environments. The project integrates geomatics, geological, and hydrological techniques to provide a holistic understanding of river dynamics and their associated hazards. As a central component of this project, geomatics plays a pivotal role in instrumental field surveying through the deployment of photogrammetry and LiDAR instruments. Remote-sensing data from satellite imagery further enriches the project’s temporal analysis capabilities. By analyzing this data over time, researchers can monitor changes in river patterns, land use, and climate-related variables, this helps identify trends and potential triggers for hydrological events. To manage and integrate the vast amount of geospatial information that is generated, a geodatabase within a geographic information system (GIS) has been established. It enables efficient data storage, retrieval, and analysis, fostering collaboration among multidisciplinary researcher teams. This system offers tools for risk-assessment, modeling, and scenario planning, these allow for proactive measures for mitigating hydrological risks.
Otwarty dostęp
Improving Traffic-noise-mitigation Strategies with LiDAR-based 3D Tree-canopy Analysis
(Wydawnictwa AGH, 2024) Wickramathilaka, Nevil; Ujang, Uznir; Azri, Suhaibah
The leaves on trees absorb road noise and serve as noise barriers. Tree structures such as tree belts and isolated trees have various methods for absorbing sounds. The depth, surface area, and noise-absorption coefficient of trees contribute to noise absorption. Therefore, this study aims to address this issue of traffic-noise pollution through the use of trees, in particular, by analyzing the noise-absorption coefficient of leaves, the surface area of the leaves, and the depths of the trees. However, the study stresses the need for 3D tree-canopy visualization to identify these factors. To achieve this, the study used LiDAR point clouds to provide accurate data for the convex hull visualizations of canopies. Additionally, a formulated equation for calculating traffic noise after absorption has been suggested by combining the traffic-noise absorption and Henk de Kluijver traffic-noise models. The study also compares the effectiveness of tree belts and isolated trees in reducing noise pollution, concluding that, below a canopy of trees, there is no noise reduction. Finally, the study has demonstrated that the number and sizes of leaves affect noise absorption, showing that noise pollution can be reduced by 1 to 3 dB(A) in the research area by using trees.
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