Mapping Purposes Research Articles

CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo

SegContrast TEST paved the way for contrastive learning on outdoor point clouds. Its original formulation targeted individual scans in applications like autonomous driving and object detection. However, mobile mapping purposes such as digital twin cities and urban planning require large-scale dense datasets to capture the full complexity and diversity present in outdoor environments. In this paper, the SegContrast method is revisited and adapted to overcome its limitations associated with mobile mapping datasets, namely the scarcity of contrastive pairs and memory constraints. To overcome the scarcity of contrastive pairs, we propose the merging of heterogeneous datasets. However, this merging is not a straightforward procedure due to the variety of size and number of points in the point clouds of these datasets. Therefore, a data augmentation approach is designed to create a vast number of segments while optimizing the size of the point cloud samples to the allocated memory. This methodology, called CLOUDSPAM, guarantees the performance of the self-supervised model for both small- and large-scale mobile mapping point clouds. Overall, the results demonstrate the benefits of utilizing datasets with a wide range of densities and class diversity. CLOUDSPAM matched the state of the art on the KITTI-360 dataset, with a 63.6% mIoU, and came in second place on the Toronto-3D dataset. Finally, CLOUDSPAM achieved competitive results against its fully supervised counterpart with only 10% of labeled data.

Green Behaviour Engagement Towards the Achievement of Sustainable Health: A Systematic Review and Bibliometric Analysis

Background The short-term actions generated by the proposed SDGs by the United Nations promote sustainability and raise the probability of finding the best global crisis solution. This research builds on previous studies that demonstrate the interconnectedness and reciprocal support of Goals 3 and 12 in their pursuit of societal transformation by altering production and consumption patterns. Focus of the Article This study set out to fill a knowledge need by examining the current state of the literature on the topic of how SDGs interact with one another. The paper primarily addressed SDGs 3 and 12. Research Question (a) How could green behaviour promote sustainable health, and what is the present status of research in this field across nations, research communities, academic journals, and professional organizations? (b) How, with research topic as the key domain, can we make progress towards Sustainable Development Goals 3 and 12 through the social marketing?. Importance to the Social Marketing Field The purpose of this paper is to provide a plan for bringing about societal transformation by means of sustained, short-term efforts in the realms of production and consumption, undertaken with the backing of the nation’s stakeholders. Doing so makes long-term success in altering health-related behaviours (prevention, protection, and persistence in the face of health hazards) more likely. Despite their best efforts to enhance the well-being of societies through social transformation, the authors believe that social marketers may be steering communities in an unsustainable direction. Method Thus, a systematic review and bibliometric analysis were conducted to ascertain the breadth of the research underlying the title by analyzing 135 Scopus publications from 74 journals from 2011–2022 and graphically presented and tabulated the trends, important authors, nations, research papers, and keyword co-occurrences. Biblioshiny, a Bibliometrix R-tool used for the purpose of scientific mapping. In addition, the alignment of study of keywords by taking social marketing discipline into account was justified through systematic review. Results Research on the interplay between SDGs and other factors is required, as 27% of the data remains unexplained. Short-term sustainable behaviours that may enhance health outcomes are combined in this study to better inform policymakers and marketers about consumers’ long-term commitment to sustainable green consumption. Recommendations for Research According to the study’s results, government agencies should create social marketing initiatives to influence the public’s behaviour and promote the health and well-being benefits associated with sustainable production and consumption. Additionally, they should provide incentives to businesses to expand their range of sustainable commodities. Businesses have a shared responsibility to develop, communicate and supply customers with the advantages of sustainable offerings, and enhance the demand for these products through a green marketing approach while reducing their prices. The ecosystem as a whole will reap the benefits of everyone’s efforts, as well as the health and happiness that comes with it. Limitations From 2011 through 2022, the Scopus database’s articles and review papers were used in the study. Additional knowledge in this area might be gained from a comparison of databases like Web of Science, Dimensions, Google Scholar, etc. The study had no geographic focus. Therefore, future research should assess this field’s national and regional potential.

Trends in Emotional Labor Research: Bibliometric and Science Mapping Analysis

Emotional labor is about employees in the service sector involving their emotions while performing the service job as required. Emotions are also used for commercial purposes. The study analyzes data on emotional labor through bibliometric and science mapping methods. For the analyses, data between 1982 and 2024 are scanned in the Web of Science (WoS) database, and data for 2,925 documents are accessed. After loading the data, the VOSviewer program is utilized to analyze it for bibliometric and science mapping purposes. Subsequently, the investigation unveiles the foremost authors, countries, journals, institutions, and keywords in emotional labor research. This is followed by analyzing their co-authorship, co-citation, bibliographic coupling, and keyword co-occurrence models. As a result, in emotional labor research, it is found that Alicia A. Grandey is the most productive and most cited author, the USA is the most productive country, Frontiers in Psychology is the most productive journal, and the most frequently encountered keywords are burnout, surface acting, job satisfaction, deep acting, and emotional exhaustion, respectively. The analysis findings reveal a collaborative nexus among authors, countries, and institutions, offering visual network maps and comprehensive insights into emotional labor's present status and trends within the scientific literature.

Visualization Of Spatial Distribution Of Village Development Index A Review Of Location Data In West Java Province

The spatial distribution of the Village Development Index (IDM) is the result of village development analysis for the purposes of mapping and assessing variations in the level of village development results in a region. The aim of the research is to analyze the spatial distribution of IDM, which is a comprehensive indicator that covers social, economic and environmental aspects in assessing village development progress. The methodology used in this research includes spatial analysis using a spatial data integration system with secondary data (statistical data). The combination of the internet of things (IoT) with WebGIS makes it possible to integrate data from various sources, in the form of spatial and non-spatial data into one easily accessible platform. This allows for more in-depth and accurate data analysis; and it is possible to disseminate information quickly and widely because it can be accessed by various stakeholders. That spatial distribution of the IDM can be used as an important tool in planning for a more inclusive and sustainable regional development. With WebGIS, data collected from IoT devices can be processed and analyzed to producing interactive maps and reports that will help in a better decision-making process, the research results can be accessed at https://jabar-e.netlify.app.

Benthic habitat mapping for estimating seagrass carbon stock across Takabonerate Islands, Indonesia

Data on benthic habitat cover are essential for monitoring and managing coastal areas, providing valuable ecosystem services for local communities. Moreover, seagrass extent is crucial for estimating carbon stock using Tier-1 calculations. On the other side, remote sensing technology has proven effective in mapping benthic habitat cover, including seagrass, over large local areas. Therefore, this research aims to map specific benthic habitats in a local area to obtain high-accuracy estimates of seagrass extent for carbon stock estimation. The study was conducted in the Takabonerate Islands, Indonesia, using Sentinel-2A imagery. The study involved field data processing, which enabled the mapping of eight benthic habitat classes using Sentinel-2A images, along with the steps for Sentinel-2A image processing for benthic habitat mapping. The benthic habitat map was produced following image correction steps, including atmospheric, sunglint, and water column corrections, followed by image classification using a Neural Net Classifier (NNC). Seagrass carbon stock estimation was performed using the Tier-1 equation from IPCC 2013. The study revealed a total benthic habitat area of 57,538.04 ha, with a seagrass area of 3127.42 ha for carbon stock estimation. The potential seagrass carbon stock in the study area was estimated to range from 337.76 GgC/ha to 31.27 GgC/ha using the Tier-1 equation. The overall accuracy of the benthic habitat map was 80.5 %, calculated using a confusion matrix table. The spatial information from this study can be used by local coastal managers and governments for baseline and monitoring purposes. The key interpretations from this study provide valuable findings that can be adapted and improved for future local mapping purposes.

Revisiting the 2023 wildfire season in Canada

The area burned by wildfires in Canada in 2023 is unprecedented in historical records. To help ensure the safety of communities and support the mobilization of firefighting resources, rapid detection of areas affected by wildfires is required. Satellite data are ideally suited to provide near real-time wildfire information over large areas. At the same time, clouds, smoke, and haze can obscure the collection of observations from sensors typically used for mapping purposes. Established methods using coarse spatial resolution satellites (e.g., MODIS, VIIRS) rely upon the combination of daily revisit to enable the rapid and reliable detection of large active fires, in full or in part, and the application of modeling (including spatial buffering) to infer additional, yet still obscured, areas. While timely, these initial maps of wildfire-impacted areas do not capture small fires (those smaller than 200 ha) and, importantly, are not intended to differentiate unburned areas within fire perimeters. To address these limitations, we used data from Sentinel-2A and -2B, and Landsat-8 and -9, which form a virtual constellation of four satellites to revisit and map burned area in Canada's forested ecosystems for the 2023 fire season. Availing upon the high temporal data density and using the Tracking Intra- and Inter-year Change algorithm (TIIC), an aggregate seasonal mapping of wildfires resulted in a total area affected by wildfires in 2023 of 12.74 Mha. Within this total area, 9.51 Mha of treed land cover was impacted. Shrubs and wetlands comprised most of the remaining non-treed area that was burned. Using a 2022 map of aboveground treed biomass (AGB), approximately 0.649 Pg of AGB was impacted by 2023 wildfires, representing an 11-fold increase in AGB impacts relative to a long-term annual average of treed AGB loss. Differences between the estimate of total burned area reported herein and the total burned area indicated by the Natural Resources Canada (NRCan) Fire M3 hotspot fire perimeters (18.64 Mha) were analyzed. Overall, estimates of burned area differed by 5.9 Mha, including over 1.13 Mha of water identified as burned within the NRCan perimeters. Differences in land cover and AGB impacts between the two products were also investigated and quantified. TIIC enables the near-continuous capture of areas impacted by fire through the fire season, allowing for within-year refinement of total burned area, rapid interrogation of land cover types impacted, and estimation of associated biomass consequences.

Three-dimensional reconstruction of wear particle surfaces using multi-focus image sequence

Wear particles hold morphological traits that are linked to wear rate and mechanisms. Wear particle analysis is vital in the context of wear monitoring. Current methods can be used in three-dimensional (3D) reconstruction for particle type recognition and wear mechanism identification. A 3D reconstruction method based on microscopic focusing and image processing is here proposed. A fixed-focus quasi-planar strategy is built using the defocusing principle. The stationary wavelet transform is employed to assess the degree of focus for the purpose of depth mapping, facilitating the construction of 3D particle models. The model is then optimized for surface morphology to address noise and unevenness. The reconstruction results of the proposed approach were compared with that of laser scanning confocal microscope with average errors of arithmetic mean height (Sa), root-mean-square height (Sq), and kurtosis (Sku) at 2.81%, 2.96%, and 10.06%, respectively. The algorithm is non-contact and adaptable, offering value across various fields.

Preliminary results of the autonomous radiation mapping in Malaysian Nuclear Agency

Autonomous radiation mapping refers to the use of autonomous robots to perform radiation mapping to measure and analyse the levels of radiation intensity of a target area. The primary purpose of the radiation mapping is to uncover the dose distribution across the target area and identify the presence of any hotspots. In this paper, the findings of the autonomous radiation mapping experiments at selected sites in Malaysian Nuclear Agency were presented. The experiments were conducted with a radiation mapping robot namely Autonomous Mobile Robot for Accurate Radiation Detection and Mapping (AMoRA) developed in the previous research. The results consisting of radiation maps of the target areas were presented and discussed.

The Distribution of Public Tobacco Cessation Clinics in Relation to the Socioeconomic Status of the Malaysian Population

Objective: To identify, locate, and examine the distribution of public tobacco cessation clinics in all Malaysian states using a Geographical Information System (GIS) and determine the relationship between the distribution of public tobacco cessation clinics and the population's socioeconomic status. Material and Methods: All registered public tobacco cessation clinics in each state were identified through the mQuit website. Based on the extracted addresses, each clinic's coordinates (longitudes and latitudes) were traced using Google Maps and transferred into Geographical Information System (GIS) for mapping purposes. Results: Wilayah Persekutuan (W.P.) Kuala Lumpur recorded the lowest ratio of public tobacco cessation clinics to the number of smokers, with 1:25,265 (one clinic to 25,265 smokers), while the highest ratio was in W.P. Putrajaya, with 1:2,118. In relation to socioeconomic status, the highest mean household income was recorded in W.P. Kuala Lumpur (mean gross income: RM 11,692), the same state with the lowest ratio of public tobacco cessation clinics. In contrast, Kelantan had the lowest mean household income (mean gross income: RM 4,214), and the ratio of public tobacco cessation clinics to the number of smokers was 1:8,383. The distribution of public tobacco cessation clinics was highly concentrated in the capital area compared to other areas in Malaysia. Conclusion: This study demonstrated the distribution of public tobacco cessation clinics using GIS. The findings showed an unequal distribution of public tobacco cessation clinics, with low distribution, especially in Sabah and W.P. Kuala Lumpur.

Developing a Social Vulnerability Index (SVI) for Risk Mapping

community, in Jilotepec, State of Mexico, with risk mapping purposes. Design/Methodology/Approach: A Social Vulnerability Index (SVI) was developed based on sociodemographic indicators, housing characteristics, and the conditions of the production systems. The information sources for the SVI indicators were obtained from official Basic Geostatistical Area (AGEB) censuses and from an online survey carried out in the community under study. The SVI was cross-referenced with data from four previously published natural hazard studies for risk mapping purposes. Results: The methodological proposal uses equal weight index statistical techniques to develop the SVI. Using online surveys is a clearly viable option for research studies that require to obtain more detailed data on housing characteristics, as well as the population’s perception of certain changes in precipitation and temperature patterns that are happening in their community and the measures, they take to face these natural phenomena. Study Limitations/Implications: Online surveys are relatively recent, which implies the need to design and implement validation and sampling mechanisms for the results. Findings/Conclusions: Risk mapping enabled the territorial visualization and identification of the communal vulnerability conditions, which facilitates a conceptual approach to the social reality of the population and will allow the formulation of potential future scenarios of climate change and the implementation of public policies.

STRATIGRAPHY AND PETROGRAPHY OF ROCKS IN KARANGTENGAH VILLAGE AND SURROUNDINGS, BABAKAN MADANG SUB-DISTRICT, BOGOR DISTRICT, WEST JAVA PROVINCE

Stratigraphy and petrography, integral components of geological science, are crucial for identifying mineral composition, rock texture, and characteristics, as well as understanding relationships between layers and layer positions. This study is located in Karangtengah Village and its Nearby Surroundings, Babakan Madang District, Bogor Regency, West Java Province. Physiographically, the study area is included in the Bogor Zone (Van Bemmelen, 1949). The research site is also incorporated in the Geological Map of Bogor Sheet, West Java. And specifically this research was conducted in the Cileungsi River. This river is included in the Jatiluhur Formation (Sudjatmiko, 1972). Stating the purpose of geological mapping, such as understanding regional geology, identifying mineral resources, or assessing geological hazards. Then, data collection in the field, such as conducting detailed field surveys to observe and record geological features, using field notebooks, GPS for plotting, and cameras for documentation, collecting rock samples for laboratory analysis (Petrography analysis and microfossil analysis). As a result of this study, there are four rock units in the Karangtengah area, with the order of formation from the oldest to the youngest. Firstly, Claystone Unit (Tmbl) consists of mudstones and sandstones, with fossils of P. transitoria and G. peripheroronda indicating an Early Miocene age (N8) and the depositional environment is the Outer Neritic. Second, the Old Andesite Intrusion (At) consists of andesite igneous rocks with Middle Miocene age and deposited in a terrestrial environment. Third, the Tuff Unit (Qt) is dominated by pyroclastic tuff rocks with Pleistocene age and deposited in a terrestrial environment. Finally, the Andesite Unit (Am) consists of andesite igneous rocks younger than the Pleistocene, penetrating the Tuff Unit (Qt) and the Claystone Unit, and deposited in a terrestrial environment.

Illuminating Sixteenth-century Measuring Methods and Map Design: New Findings from Pieter Pourbus’ Chorographic Maps

ABSTRACT Pieter Pourbus was a mid-sixteenth-century artist and mapmaker who crafted large-scale chorographic maps in the coastal area around Bruges (Flanders, The Low Countries). The topographic and planimetric accuracy of his maps has made researchers hypothesize that he applied the triangulation methods as first described by Gemma Frisius (1533). The actual application of triangulation for mapping purposes in the sixteenth century is however contested. A new macroscopic and GIS-integrated study of Pourbus’ maps has revealed traces of the production process (measuring, drafting, copying). The traces discovered on the map of Cadzand (compass circles, perforations and incised meridians and bearings) are unprecedented in the history of cartography, can be directly related to the step-by-step method and drawings of Frisius, and provide evidence of the use of triangulation in the sixteenth century. Other traces, such as squaring grids, pounced dots or map inserts, further clarify the methods Pourbus used for compiling and copying maps.

Validasi Kuesioner Accountant Career Adaptability Skills Pada Mahasiswa Politeknik

According to students enrolled in accounting study programs, the purpose of this study is to map the skills of an accounting graduate, particularly from vocational school. The purpose of this competence mapping is to help vocational education institutions (PTPV) determine which key competencies students need to possess to be prepared for a career in the workforce. Students in vocational colleges in East Kalimantan are the subjects of this study to gather their opinions on the essential skills of accountants. The core competencies are derived from the International Federation of Accountants (IFAC) lists five professional skills: intellectual, technical & functional, organizational & management, interpersonal & communication. Confirmatory Factor Analysis (CFA) is used in data analysis using SPSS AMOS 26 software. The five dimensions- that comprise 31 question items can be used to career adaptability skills for accountants. The findings of this study can be utilized to develop a career adaptability skills questionnaire for accountants that can be used by both the workplace and vocational education.

Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention

Abstract In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 $ {km}^2 $ and to the annual hourly $ {NO}_2 $ concentration in 2019 over the Oslo metropolitan region (1026 $ {km}^2 $ ). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.

Моніторинг та управління побутовими відходами у селищі Нова Прага: від несанкціонованих сміттєзвалищ до сучасних рішень

This publication addresses the problematic issues of unauthorized landfills, monitoring, and management of household waste using the example of the village of Nova Praha (Kirovohrad region), which is relevant and timely in the context of Ukraine's European integration and the implementation of the European waste management model. The object of this study is the unauthorized landfills in the village of Nova Praha, while the subject is the features of their location and the spatial planning aspects of handling, monitoring, and management. During the implementation of this study, a set of empirical (mapping, comparative analysis) and theoretical (synthesis, Annotationion) methods were applied. As a result of the monitoring conducted for subsequent mapping purposes, 9 household waste landfills and 6 construction waste landfills were identified within the relatively small area (50 km²) of the village. Their negative impact on the environment was established, and numerous violations of environmental legislation were detected. To address the suboptimal handling of household waste based on current legislative requirements, specific recommendations were developed for local authorities regarding the elimination of identified unauthorized landfills, the reclamation of disturbed lands, and the prevention of new ones. The practical significance of the developed recommendations lies in their potential use by local authorities to optimize the household waste management system in the interest of the sustainable development of the territorial community.

Some mathematical assumptions for accurate transformation parameters between WGS84 and Nord Sahara geodetic systems

Abstract The transformation of coordinates between different geodetic datums has been a common practice within the geospatial profession. Relating different geodetic datums mostly involves the use of conformal transformation techniques, which could produce results that are not very often satisfactory for certain geodetic, surveying, and mapping purposes. This has been attributed to the inability of the conformal models to resolve the lack (non-exhaustivity) and the heterogeneity of double points existing within the Algerian local geodetic networks. Indeed, the Algerian geodetic network is divided into two main zones completely different in accuracy and points exhaustivity. The North is better and well defined in precision; on the other hand, the South is poorly defined because of the lack of heterogeneity of the points. Mathematical algebraic methods give closed-form solutions to geodetic transformation problems, requiring a high-level computer programming background. In everyday usage, the closed-form solutions are much simpler and have a higher precision than earlier procedures. Thus, it can be predicted that these new solutions will find their place in practice. The present work deals with an important theoretical problem of geodesy: we are looking for a mathematical dependency between two spatial systems using common pairs of points whose coordinates are given in both systems. In geodesy and photogrammetry, the most often used procedure to move from one coordinate system to another is the 3D, seven-parameter similarity transformation (Bursa–Wolf, Molodensky–Badekas, and Helmert).

Penerapan Algoritma Dijkstra dalam Pemetaan UMKM Berbasis Android

This study uses the Java programming language and the Android platform to map Micro, Small, and Medium-Sized Enterprises (MSMEs) in the Deli Serdang area using the Dijkstra algorithm. The purpose of this MSME mapping is to give consumers utilizing a mobile-based Geographic Information System (GIS) information about the closest location of MSMEs. A software development methodology combining quantitative and qualitative methodologies is used in this study. Data about the location of MSME's was gathered via surveys and conversations with company owners. Using SketchUp software, this data was then processed and examined in order to create a visual representation of the locations of MSMEs in the Deli Serdang region. Additionally, Java was used in the development of Android-based mobile applications to provide an interactive and user-friendly display. The location of MSMEs is listed on this application, along with suggestions for the quickest path from the user's current location to the MSMEs they wish to visit. According to the study findings, the Dijkstra algorithm has been successful in recommending the shortest route and fastest travel time to MSMEs, enabling users of this application to locate MSME sites with accuracy and speed. Additionally, users may view comprehensive details about each MSME, including a description of the company, its hours of operation, and contact information. It is anticipated that this application will improve the area of Deli Serdang's information accessibility for tourists and prospective MSME customers. Aside from that, it is envisaged that it will boost MSMEs' competitiveness and promote them while supporting regional economic expansion.

Combining conventional technique with fluoroscopy integration module in accessory pathway ablation.

Accessory pathway (AP) ablation is a straightforward approach with high success rates, but the fluoroscopy time (FT) is significantly longer in conventional technique. Electroanatomical mapping systems (EMS), reduce the FT, but anatomical and activation mapping may prolong the procedure time (PT). The fluoroscopy integration module (FIM) uses prerecorded fluoroscopy images and allows ablation similar to conventional technique without creating an anatomical map. In this study, we investigated the effects of combining the FIM with traditional technique on PT, success, and radiation exposure. A total of 131 patients who had undergone AP ablation were included in our study. In 37 patients, right and left anterior oblique (RAO-LAO) images were acquired after catheter placement and integrated with the FIM. The ablation procedure was then similar to the conventional technique, but without the use of fluoroscopy. For the purpose of acceleration, anatomical and activation maps have not been created. Contact-force catheters were not used. 94 patients underwent conventional ablation using fluoroscopy only. FIM into AP ablation procedures led to a significant reduction in radiation exposure, lowering FT from 7.4 to 2.8 min (p < .001) and dose-area product from 12.47 to 5.8 μGym² (p < .001). While the FIM group experienced a reasonable longer PT (69 vs. 50 min p < .001). FIM reduces FT regardless of operator experience and location of APs CONCLUSION: Combining FIM integration with conventional AP ablation offers reduced radiation exposure without compromising success rates and complication.

A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods

In this work, we propose a deep-learning-based mapping model for simulating and predicting the flow field of Reynolds-averaged Navier–Stokes (RANS) and large eddy simulation (LES) of propellers. The model employs image processing and computer vision methods to process the two-dimensional propeller RANS and LES simulation data. First, images are obtained by simulating the flow fields with the location data used to acquire a set of features specific to the corresponding positions. Second, the regression models for the flow fields and the mapping between the two different flow fields are established to predict the LES flow field at that position. Specifically, we utilize a deep convolutional neural network (CNN) for feature extraction from the flow field, which is then integrated with a nonlinear module for the purposes of regression and mapping. The effectiveness and accuracy of the proposed model in flow field prediction are demonstrated by its application to propeller RANS and LES simulations. It is shown that the overall error rate between the LES flow field predictions generated using this method and actual flow field data is 7.92%. Additionally, we also evaluate the model’s generalization ability, stability, and robustness by testing it on the data of propeller flow fields at different Reynolds numbers. The results verify the applicability of the proposed model in various problems of flow field simulation and prediction.

INTEGRATION OF IPHONE LiDAR WITH QUADCOPTER AND FIXED WING UAV PHOTOGRAMMETRY FOR THE FORESTRY APPLICATIONS

Abstract. The recent innovations in remote sensing technologies have given rise to the efficient mapping and monitoring of forests. The developments in the sensor implementation have mainly focused on optimizing the payload of the UAV system and allowed the users to acquire the data simultaneously with a range of active and passive sensors like high-resolution RGB cameras and multispectral cameras LiDAR (Laser Imaging Detection and Ranging). The main objective of this research contribution is to combine the Digital Elevation Model (DEMs) from quadcopter Unmanned Aerial Vehicles (UAVs), Fixed Wing UAV-based cameras, and iPhone datasets for the forest plots. The datasets from two vegetation seasons, namely leaf-off and leaf-on, were used to combine the Digital Elevation Models from different data acquisition platforms. This internship research work aims to create and experiment with new methods, techniques, and technologies for the applications of UAV photogrammetry and iPhone LiDAR in forest napping and inventory management. CHMs are also generated in this work which helps assess the conditions of the forests in the recreational areas, and the possibility of solutions like iPhone LiDAR and UAV photogrammetry would be highly efficient and economical. The leaf-off and leaf-on datasets were processed in Agisoft Metashape Professional software to generate dense point clouds for the forest plots. The point cloud from the leaf-on dataset was rasterized to generate a DSM whereas the leaf-off point cloud generated a DSM of the forest plots after ground filtering with Cloth Simulation Filter (CSF) plugin. The iPhone LiDAR point was also rasterized to a DTM product after pre-processing steps and noise removal. The Canopy Height Models (CHMs) were generated by subtracting UAV and iPhone LiDAR based DTMs from the UAV leaf on DSM. Finally, the accuracy assessment of CHMs from UAB datasets and their integration with iPhone LiDAR has been assessed using the accurate tree heights measured during the forest field visits. The proposed methodology can be used for forest mapping purposes where a moderate accuracy is requested.