Multi-stage Methodology Research Articles

Implementation of High Availability Based on Load Balancing and Failover Method in E-commerce Using MySQL Database

The rapid growth of e-commerce has brought about challenges in managing traffic loads and ensuring the availability of database servers. One critical issue is what happens when the main database server fails, potentially leading to downtime or data loss. This research aims to address these problems by optimizing server resource distribution and implementing data replication across multiple database servers. The goal is to enhance system efficiency, prevent server overloads, ensure data availability, and minimize downtime or data loss when server failures occur.This research utilizes a multi-stage methodology. First, data collection was conducted to gather relevant information. In the second stage, a needs analysis was performed to identify system requirements. The third stage involved designing the system architecture, including the creation of entity-relationship diagrams (ERD), flowcharts, and network topologies. In the final stage, the system was implemented through database replication and the application of failover methods.The results demonstrate that the failover mechanism operates effectively, ensuring that data is successfully replicated across all database servers. This confirms that the system achieves high availability and minimizes the risk of data loss or service downtime. The implications of this research provide a reliable solution for improving the resilience and performance of e-commerce systems, particularly in managing database server failures.

Video Essay about Cinema: Ways of Representing and Interpreting Dominant Content in a Polycode Media Text The object of research in the article was video essays

The object of research in the article was video essays published on the KinoPoisk YouTube channel. A multi-stage methodology for studying polycode media text was proposed, which is characterized by a system of applied techniques: genre, structural-semantic, conceptual analysis of media text; methodology for analyzing the polycode structure of a media text. Using the example of the analysis of the video essay «Why One Flew Over the Cuckoo’s Nest is a Great Cinema», the constitutive genre features of the media genre, which are determined by the content dominants of the media text, are highlighted: title/title (sometimes subtitle) of the video essay; the compositional division of the video essay into parts, which are also given headings – this structure organizes the narrative of the video essay; description (annotation) of the video and time codes where the headings of the parts are written; movie quotes (visuals); fragments of the original soundtrack of the quoted films; musical accompaniment of video essay; semantically and stylistically complicated voice-over text (with the obligatory use of intertextual signs of various types); specific installation. The multi-level semantic structure of the video essay was studied and it was concluded that the content dominant of the video essay being studied is the concept of «Freedom» – one of the basic linguocultural concepts of the Russian language picture of the world. It has been established that the leading code through which the dominant content of a video essay is interpreted is verbal, supported and reinforced by other codes and subcodes of the polycode structure of the media genre. The analysis of the media text allows us to conclude that the author’s selection of film works, interpretation and interpretation of their content dominants, the tone of the discussion, «fitting» the film into the historical and cultural context, objectification of the value system of the collective author allows one to effectively influence the worldview of the mass addressee, forming and/or reframing his ideas about key ethical and cultural concepts, about historical and modern realities and events.

The Investigation on the Application of Digital Technologies for Logistics Business Competitiveness

The logistics sector's performance is integral to nations' economic and industrial progress. The advent of digital technologies marks a monumental historical shift, impacting worldwide transformations in structural organization, business strategies, economic principles, and regulatory frameworks. The landscape of logistics management provides a fertile ground for integrating these digital advancements, as exemplified by the dynamic emergence of logistics 4.0. This study evaluates how implementing digital technology profoundly influences logistics management's competitiveness in the business context. The study investigates applying digital technologies within the logistics sector using multi-stage methodology, which integrated different methods: the formation of hierarchy of quantitative methods and the application of statistical data analysis methods. Statistical data analysis helps to collect knowledge about logistics business, the application of digital technologies and its performance within European Union (EU) countries. The application of hierarchy of quantitative methods allows to compare the applications of various technologies with digital technologies and the competitiveness of various sectors with logistics sector competitiveness. These findings accentuate the intricate interplay between technological advancement and competitiveness within the logistics sector. The study contributes to a deeper comprehension of how digital technologies reshape competitive dynamics and lays the foundation for refined logistics practices in an increasingly digital-driven world.

Methodology of evaluating finished goods warehouse performance through lean methods

This paper considers the problem of evaluating the effectiveness of the finished goods warehouse of a manufacturing company, in which a modified TPM method - Total Productive Management (TPM2) - was applied to improve productivity. A multi-stage methodology was proposed, including a decision to modify the system, determination of the scope of changes, monitoring the results obtained and a multi-criteria evaluation of the changes made. The decision to make modifications to the existing system was motivated by the lower-than-expected quality of customer service (frequent delivery delays). With regard to the transport department, lean flow pillar activities were focused on analysing losses (muda) in warehouse processes (product loading and package unloading). The purpose of these activities was to minimise interruptions in warehouse processes (product loading and package unloading). "The steps for solving the problem" methodology based on Deming's PDCA cycle was used to solve the problem. The analysis covered, among other things, the information flow processes between production planning and the customer service department, the planning processes of the dispatcher, the efficiency of the loading processes, and the causes of interruptions in warehouse operations. The analyses employed the chronometry of selected works, the 5W + 1H method and the Pareto method. By using the 5S method and some characteristics of the SMED method, the organisation of loading work was decisively changed (shunting yard changes, appropriate buffers for transport equipment). The changes introduced in the system were monitored for several months. Appropriately defined OEE indicators were used to assess the behaviour of the system after the changes. The indicators consider the use of available warehouse time, the efficiency of the loading process and the quality of the tasks performed. The results that can be achieved are presented using the specific example of the finished goods warehouse of a manufacturing company in the FMCG sector.

From responsible sourcing of wastes to sustainable energy consumption in the blue hydrogen supply chain: Case of nearshoring in Nuevo Leon

Despite global uncertainties, Mexico has become the largest supplier to the United States, emphasizing its growing role in cultivating resilient supply chains. Nearshoring practices from Mexico and Canada to the U.S. present new production opportunities. Increasingly, U.S. consumers place a premium on sustainable consumption, so blue hydrogen plants must be sourced responsibly. This paper firstly goes beyond economic motivations, aiming to formulate a multi-stage methodology for assessing suppliers of municipal solid waste for blue hydrogen plants, marking a pioneering effort in catering to the environmentally conscious preferences of U.S. consumers. The purpose of this work is to examine a case study of Blue Hydrogen production in Nuevo Leon, emphasizing the importance of developing responsible sourcing criteria for waste suppliers to limited blue hydrogen plants. The developed hybrid approach integrates various methodologies, including Critical Systems Heuristics (CSH), fuzzy rough sets, hierarchical fuzzy rough analysis, Fuzzy rough WASPAS, TOPSIS, and Best-Worst Method (BWM), providing a comprehensive framework for decision-making under uncertainty. Key findings include the identification of water conservation (E2) and job creation (S2) as the top environmental and social criteria, respectively. Supplier WC2 was identified as the top-performing waste collector, achieving a final score of 4.77. This structured methodology prioritizes product categories for hydrogen consumption, considering sustainability criteria and U.S. consumer preferences, thereby offering valuable insights for businesses navigating this evolving landscape.

From process-based to technology-driven: a study on functionalities as key elements of collaborative planning methods for construction projects

With the advancement of emerging technologies, significant attempts have been made to develop collaborative planning methods and to involve as many project stakeholders as possible in the construction project planning and control process. However, inadequate consideration has been paid to the characteristics, goals, and principles underlying these methods to meet the needs of collaboration for project planning between project teams. To deal with this, a multi-stage methodology was carried out to achieve the aims of this study. The first step was identifying collaborative planning methods and their functionalities in the construction sector. Further, a quantitative analysis based on Social Network Analysis (SNA) was conducted to determine the most frequently utilized functionalities in collaborative planning methods. The results revealed that process-based collaborative planning methods’ functionalities prioritized process and people-related characteristics such as team trust and promise, as well as social interactions, whereas technology-driven methods highlighted visualization along with collaboration and communication as a key element of collaborative planning. Subsequently, this study contributes to the body of construction project planning and control knowledge from both theoretical and practical perspectives by enhancing the understanding and sensemaking of project stakeholders towards the underlying concepts and objectives of collaborative planning methods.

Translating gardens into accessible multisensory tours for blind and partially sighted visitors: an exploratory case study

AbstractThis paper presents an exploratory case study which illustrates the first stage of our multi-stage methodology for translating gardens into a multisensory and accessible tour for blind and partially sighted (BPS) visitors and gathering feedback to be used in a future project on producing a smart audio descriptive guide to assist BPS visitors to appreciate gardens in a multisensory way. The key research questions are: how can the multisensory potential of gardens be translated into an accessible experience for BPS visitors? and to what extent can a smart audio descriptive guide enable access to gardens and provide multisensory visitor experiences primarily for BPS visitors? Our multi-stage methodology begins with an exploratory case study in which a group of BPS visitors were led by a human guide on a tour of the historic Hillsborough Castle and Gardens. The first stage involved the tour guide and accessibility expert working closely together to plan the multisensory garden tour. Then the actual tour of the gardens was carried out with a small group of BPS visitors. In-tour questions for the BPS visitors stimulated reflection and feedback, and interviews were carried out at the end of the tour. The paper presents some of the more significant observations which emerged from the tour, and draws conclusions about the extent to which a smart audio descriptive guide could provide many of the benefits of a human guide, and its advantages and inherent limitations. Some of these findings are relevant to those planning similar visits in other garden venues, including for broadening the application for universal access.

Assessing the adoption of the Yavuz Battleship application in the mixed reality environment using the technology acceptance model

This study concentrates on developing a mixed reality (MR) app for the historic ship Yavuz Battleship, also known as “SMS Goeben,” and assessing its acceptance using the technology acceptance model (TAM). Mixed reality blends real and virtual environments to create novel spatial experiences, bridging the gap between the virtual and real worlds. This technology enables designers to craft immersive, interactive environments that enhance users' perception of physical reality. The study employs a multi-stage methodology, including historical research, 3D model creation, digital information integration, Unity program transfer, MR environment development with the Mixed Reality Toolkit (MRTK), and implementation as a Microsoft Hololens 2 app. TAM is employed to gauge MR acceptance. Evaluation results affirm the Yavuz MR app's TAM alignment, providing structural support for similar architectural science inquiries. The research underscores the need to explore MR's architectural applications and TAM's role in measuring MR acceptance.

Multistage distribution expansion planning leveraging load flexibility

This paper presents the multistage distribution network planning problem, utilizing conventional and non-conventional expansion measures. Here, conventional network expansions, i.e. transformers and lines installations, compete with non-conventional load flexibility contracting. The proposed multistage planning methodology aims at minimizing the net present value of all investment and operational costs, while annuity calculations are used to make technologies with different characteristics comparable. A novel combination of the pseudo-dynamic multistage planning approach called forward fill-in with a single-stage planning approach that utilizes load flexibility is developed. The single-stage distribution network planning model is based on the Tabu Search metaheuristic, which has proven to be scalable for realistic large-scale networks. In a case study, the multistage model is applied to a two-stage planning problem. In contrast to single-stage methodologies, the multistage methodology demonstrates the value of load flexibility, accounting for the time value of money when analysing savings due to network investment deferrals. In the case study, flexibility contracting at a cost of 10 €/kW per year results in total cost savings of 15.68 %.

Predicting working sole Indian mothers' satisfaction towards work–family balance integrating the Kano model and weighted average method

PurposeThe main impetus of the current paper is to identify and prioritize the work–family balance (WFB) satisfaction attributes specifically for working sole mothers' by deploying the Kano technique and weighted average method.Design/methodology/approachA multi-stage methodology has been used in the present analysis. Initially, the Kano method has been utilized to categorize the WFB satisfaction attributes using a three-dimensional WFB satisfaction scale. Afterward, the satisfaction coefficient technique was employed on Kano outcomes to get the WFB satisfaction and dissatisfaction index. Subsequently, the weighted average method was employed to prioritize the WFB satisfaction attributes.FindingsFindings uncovered the significance of a non-linear association between WFB attributes and employed sole mothers' WFB satisfaction. The findings revealed that one-dimensional and must-be-based WFB satisfaction attributes are responsible for sole mothers' WFB satisfaction and employing organizations must not overlook them. Additionally, the results of weighted average method-based prioritization can help organizations to focus on particular WFB satisfaction criteria according to their priority level.Research limitations/implicationsThe findings are useful for WFB policy-makers and managers to formulate a suitable WFB strategy specifically for single mothers.Social implicationsResults provide a path for employers to minimize the work–family role conflict and societal dissatisfaction that helps sole mothers to attain the desired WFB.Originality/valueThis study first employed a novel approach that incorporates the Kano application with the weighted average method in order to prioritize the WFB satisfaction attributes for lone mothers.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0074

A Multi-Stage Methodology for Long-Term Open-Pit Mine Production Planning under Ore Grade Uncertainty

The strategic planning of open pit operations defines the best strategy for extraction of the mineral deposit to maximize the net present value. The process of strategic planning must deal with several sources of uncertainty; therefore, many authors have proposed models to incorporate it at each of its stages: Computation of the ultimate pit, optimization of pushbacks, and production scheduling. However, most works address it at each level independently, with few aiming at the whole process. In this work, we propose a methodology based on new mathematical optimization models and the application of conditional simulation of the deposit for addressing the geological uncertainty at all stages. We test the method in a real case study and evaluate whether incorporating uncertainty increases the quality of the solutions. Moreover, we benefit from our integrated framework to evaluate the relative impact of uncertainty at each stage. This could be used by decision-makers as a guide for detecting risks and focusing efforts.

Digital Image Text Recognition Using Machine Learning Algorithms

Abstract: Text recognition is used in various fields like document analysis, picture labeling, and text analysis, etc. The process of recognizing text from image is very crucial.. Employing Maximally Stable Extremal Regions (MSER) and Optical Character Recognition (OCR) algorithms have drastically changed the accuracy and reliability of text detection from photos. Our proposed work suggests a model for text recognition that makes use of both the MSER and OCR algorithms' benefits for the purpose of accuracy improvement and reliability of the digital text extraction procedure. OCR follows cutting-edge techniques to distinguish and identify individual characters, serving as the essential building block for text recognition. However, these algorithms may struggle when dealing with complex backdrops, blurry photos, or text that is structured in an atypical way. We use MSER approach, which excels at recognizing text sections by finding maximally stable regions across various severities and scales, to solve these constraints. The suggested model employs a multi-stage methodology. The MSER algorithm is used for extracting the likely text spots from the input image first. To boost OCR performance, these zones are then fine-tuned using pre-processing techniques including noise reduction and picture enhancement. The OCR system next processes the cleaned-up sections, identifying each region's text using machine learning and pattern recognition methods. The text that is recognized is then further processed to increase the accuracy and refine these findings. Thus, compared to most other models, the text recognition model that is built utilizing the MSER and the CNN (OCR, a component of CNN) algorithm performs better

Multistage Detection of Tetrodotoxin Traces in Diodon hystrix Collected in El Salvador.

This study describes a multistage methodology to detect minute amounts of tetrodotoxin in fishes, a plan that may be broadened to include other marine organisms. This methodology was applied to porcupinefish (Diodon hystrix) collected in Punta Chiquirín, El Salvador. A three-stage approach along with post-acquisition processing was employed, to wit: (a) Sample screening by selected reaction monitoring (HPLC-MS/MS-SRM) analyses to quickly identify possible toxin presence via a LC/MS/MS API 3200 system with a triple quadrupole; (b) HPLC-HRFTMS-full scan analyses using an ion trap-Orbitrap spectrometer combined with an MZmine 2-enhanced dereplication-like workflow to collect high-resolution mass spectra; and (c) HPLC-HRMS2 analyses. This is the first time tetrodotoxin has been reported in D. hystrix specimens collected in El Salvador.

A CNN-Based Chest Infection Diagnostic Model: A Multistage Multiclass Isolated and Developed Transfer Learning Framework

In 2019, a deadly coronaviral infection (COVID-19) that infected millions of people globally was detected in China. This fatal virus affects the respiratory system and currently spreads to more than 200 nations worldwide. COVID-19 may be found using a chest X-ray scan, a reliable imaging method. Although an expert may examine an X-ray scan manually, this process takes a lot of time. Therefore, deep convolutional neural networks (CNNs) may be utilized to automate this procedure. In this work, at the first step, a novel isolated 19-layer CNN model is developed from scratch to detect chest infections using X-rays. Then, the developed model is reutilized to distinguish the type of chest infection, such as COVID-19, fibrosis, pneumonia, and tuberculosis, using the transfer learning approach. Stochastic gradient descent with momentum is utilized to optimize the model. The proposed multistage framework shows 98.85% and 97% classification accuracies for chest infection detection (binary classification between normal and patient) and four-class subclassification (COVID-19, fibrosis, pneumonia, and tuberculosis) for an online chest X-ray dataset. The reliability of the proposed multistage CNN model was further validated through a new dataset, showing an accuracy of 98.5%. The proposed multistage methodology took minimal training time compared to publically available pretrained models. Therefore, the presented multistage deep learning framework can help doctors in clinical practices.

A hybrid multi-stage methodology for remaining useful life prediction of control system: Subsea Christmas tree as a case study

With the improvement of control system composition and operation process complexity, the uncertainty in its operation process increases and real-time observation data is difficult to obtain, and the influence of noise also exists in the process of signal acquisition, which brings more difficulties to the prediction of the remaining useful life (RUL). To solve these problems, a hybrid multi-stage methodology for RUL prediction of control system is proposed. The variant of unscented Kalman filter (UKF) utilizes dynamic Bayesian networks (DBNs) for uncertainty analysis in the process of prediction using UKF, to analyze RUL of nonlinear degenerate systems. In the prophase of prediction, the dynamic unscented Kalman filter models calculate the distribution of random faults and process noise, match the degradation stage of the system and obtain the operation data. Then, optimize the degradation process of the system, and the covariance and the optimal estimate of the system are calculated by cyclic iteration. The real degradation process of control system is simulated by optimizing the results, so as to compensate for the lack of accurate measurement of the real degradation process. The proposed method can improve the accuracy of RUL prediction and enhance the robustness of the prediction model. The methodology is verified by subsea Christmas tree with electro-hydraulic compound control.

Flood-resilient governance in Okanagan valley of British Columbia: current practices and future directives

The governance of flood resilience comprises an array of policy instruments that can be applied to reduce damages and risks to the communities. Policy instruments available at the local level tend to have the greatest impact on communities’ ability to absorb shock and resume normal operation. This study is the first attempt to analyze policy instruments and regulations guiding the progression of flood resilience measures in the Okanagan Valley, British Columbia. It aims to offer recommendations specific to the local context and identify the best practices to improve flood resilience of the communities. Therefore, the study adopted a multistage methodology including literature review, stakeholder consultations with the organizations (consisting of municipalities, regional districts, and First Nations), and quantitative analysis using present/absent criteria to measure prevalence of given indicators and five flood resilience classes. The results suggest that the flood resilience policies and strategies of 22 organizations in the Okanagan valley are predominantly focused on flood preparation and mitigation, whereas policies related to during and post-flood management are still in infancy and do not offer satisfactory support. Notably, some organizations are proactive in recognizing looming flood risks, thus enacting essential policy measures to strengthen resilience. These results further highlight that these organizations have focused on a variety of measures to improve resilience, such as spatial planning, building construction setbacks, enhancing natural environment, protecting riparian areas, and stormwater management. As well, current flood-resilient practices by the Prairies (Calgary) paved the way for how both regions can learn from each other. The study sheds light on broader takeaways of what the findings of this study mean for other world regions. The study results are of great importance to the organizations across the Okanagan Valley as they lay the groundwork for resource mobilization to achieve flood resilience and sustainability.

Principles of Forming a Strategy for Reducing the Psychological Tension of Social Network Users

Aims: The purpose of the study was to develop principles of a strategy for influencing the psychological state of social network users using the example of the Russian-language segment of Twitter, one of the reasons for which is the lack of awareness about aspects of the coronavirus infection. Background: In contrast to the existing works on mood management and Emotion Regulation Strategies, there are principles based not on emotional regulation (cognitive reappraisal and expressive suppression), but on information processing of the content of social media messages and forums. Objective: The objective of the study was to develop principles of a strategy for reducing the psychological tension of social network users (further – Strategy) based on the Russian-language segment of Twitter. Methods: The proposed research methodology includes a study of the discussion field in the active forum of the Runet (the qualitative aspect of emotionality as a reflection of psychological tension) and the Russian-language segment of Twitter (the quantitative aspect of terminology frequency). The qualitative research consisted in isolating the sensitive words used by vaccine opponents to describe their beliefs. A multi-stage methodology has been developed for the meaningful analysis of Twitter users’ messages. Results: . The result of the study is a methodology for developing principles of the Strategy. Based on this methodology, the following aspects of the problem have been developed: 1) the principle of clarifying the definition of psychological tension; 2) the principle of comparing the user and scientific meanings of terms, taking into account the contexts of their use; 3) the principle of contextual comparison of the user’s and scientific meanings of the term; 4) the principle of visual popularization of scientific knowledge. Conclusion: An original methodology was created for developing principles of the Strategy. In contrast to the existing works on mood management and Emotion Regulation Strategies, there are principles based not on emotional regulation (cognitive reappraisal and expressive suppression [1]), but on information processing of the content of social media messages and forums. Other: A new approach to reducing the psychological tension of social media users can contribute to sharing timely, accurate and positive information about COVID-19, and reduce excessive discussions about COVID-19, which can positively affect the psychological well-being of the general public.

A Multistage Methodology Approach for Constraints Prioritisation of Facility Layout Problem (FLP) in Enhancing the Performance of Building Energy Efficiency

Facility Layout Problem (FLP) is gaining increasing attention among researchers; it is a term relating to the poor layout of facilities as a significant contributing factor of poor performance. FLP is of paramount importance when determining inefficiencies in large room layouts, such as a library building, since the building’s layout closely influences air distribution and impacts on human comfort. Thus, this can lead to unnecessary high energy use to mitigate any inefficiencies. This problem is classified as an NP-hard problem (non-deterministic polynomial-time hardness), considering the various factors influencing thermal factors and layout design. However, previous research shows a lack of consideration of FLP for large rooms. It is identified that various types of constraints are considered in the layout problem literature, and penalty-based constraints are often being prioritised by mere human judgement and intuition. Hence, the accuracy of the objective decision-making is questionable. Therefore, this study proposes a multi-stage methodology to determine the weight of each constraint for FLP by using a multi-criteria decision making (MCDM) method specific to a library building as a case study exemplar. This study’s main focus is to determine penalty based constraints in meta-heuristic approaches for the effective use of FLP. This study concludes by advocating that the proposed methodological approach can be used to identify the most significant constraints in FLP.

Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty

The benefits of more comprehensive energy use and promotion of renewable energy (RE) consumption enable large-scale microgrid deployment. However, the uncertainty of renewable energy sources and the diversity of load types pose a threat to the microgrid's stability. Recently, energy scheduling optimization for microgrids (MGs) has been primarily based on ideal models, but incorporating as many real-world characteristics as feasible can improve the reliability of the optimization outcome. This paper provides a multi-stage methodology for solving the energy management optimization (EMO) problem of MG under uncertainty considering carbon trading market and demand side response (DSR). To begin, scenario analysis method was used to address the uncertainty associated with RE in MG, and four typical scenarios of renewable energy were generated. Then, the flexible configuration and operational constraints of each power source in MG are dealt with under the premise of considering the carbon trading market. The third stage involved merging the characteristics of various load types and analyzing the response impacts of different percentage residential and industrial loads, respectively, using the price-based and load-transfer-based DSR approaches. Finally, quantum particle swarm optimization (QPSO) algorithm was used to obtain the optimal solution. The acquired results demonstrate the efficacy of the proposed multi-stage energy optimization framework, and support the following two conclusions: 1) The carbon trading market policy contributes to the reduction of carbon emissions and fossil fuel consumption. 2) A high load participation rate in DSR can increase MG operation economics by up to 27.48% compared to not considering DSR.

Mapping Tree Canopy in Urban Environments Using Point Clouds from Airborne Laser Scanning and Street Level Imagery

Resilient cities incorporate a social, ecological, and technological systems perspective through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have compared and combined two of the most common data sources for updating urban tree inventories: Airborne Laser Scanning (ALS) point clouds combined with aerial orthophotographs, and street-level imagery from Google Street View (GSV). Depending on the data source, different methodologies were used to identify the trees. In the first stage, the use of individual tree detection techniques in ALS point clouds was compared with the detection of objects (trees) on street level images using computer vision (CV) techniques. In both cases, a high success rate or recall (number of true positive with respect to all detectable trees) was obtained, where between 85.07% and 86.42% of the trees were well-identified, although many false positives (FPs) or trees that did not exist or that had been confused with other objects were always identified. In order to reduce these errors or FPs, a second stage was designed, where FP debugging was performed through two methodologies: (a) based on the automatic checking of all possible trees with street level images, and (b) through a machine learning binary classification model trained with spectral data from orthophotographs. After this second stage, the recall decreased to about 75% (between 71.43 and 78.18 depending on the procedure used) but most of the false positives were eliminated. The results obtained with both data sources were robust and accurate. We can conclude that the results obtained with the different methodologies are very similar, where the main difference resides in the access to the starting information. While the use of street-level images only allows for the detection of trees growing in trafficable streets and is a source of information that is usually paid for, the use of ALS and aerial orthophotographs allows for the location of trees anywhere in the city, including public and private parks and gardens, and in many countries, these data are freely available.