Cloud-based Platform Research Articles

Landsat image classification using a deep learning model and multiple-source training samples

ABSTRACT Public release of the entire Landsat data archive and cloud-based geocomputation platforms have greatly facilitated land-cover mapping worldwide. The acquisition of training samples presents a significant challenge in mapping. In this study, we have developed an approach based on transfer learning to creatively use existing global land-cover products and training samples to resolve the training sample issue. We pretrained a deep neural network (DNN) model using low-quality samples automatically generated from existing global land-cover products. The pretrained model was then fine-tuned using high-quality training samples gathered from the training samples shared by authors of existing studies. We implemented this approach to generate a land-cover map for Beijing City, China, in 2015. Using the fine-tuned DNN model, we achieved an overall accuracy (OA) of 86.4% and a kappa coefficient of 0.796, based on independent validation samples. The accuracy was the same and even surpassed that of existing land-cover maps for Beijing. The fine-tuned DNN outperformed the random forest (RF) (OA = 70.2%, kappa = 0.588) and support vector machine (SVM) models (OA = 68.7%, kappa = 0.555) using the same training samples, showing that the need for training samples in land-cover classification can be met by combining deep learning models, existing global land-cover products, and recycled training samples.

Short communication: Dairy Victory™ Platform: A Novel Benchmarking Platform to Empower Economic Decisions on Dairy Farms.

In the face of diminishing economic margins, dairy farmers globally are compelled to maintain economic competitiveness. Benchmarking emerges as a strategic tool to establish new, achievable improvement objectives that balance ambition with practicality. This typically requires integrating diverse data sources such as feed, milk production, diet, and market prices. However, many farms lack essential, cow-level data like daily milk output and milk income over feed cost (IOFC), which hampers economic performance visibility and informed decision-making. To address this challenge, we introduce “Dairy Victory™ platform (DVP)” a novel cloud-based benchmarking platform designed to simplify complex analyses. The DVP uniquely calculates zootechnical and economic key performance indicators (KPIs) at the cow and herd levels and benchmarks these against a dynamically selected cohort of farms, facilitating comparisons across various farm sizes, and milk production levels. The primary objective of this paper is to demonstrate the utility of DVP as a decision-making tool in supporting farmers and consultants with both operational and strategic decisions. It emphasizes leveraging benchmarking information to enhance decision-making processes, thereby highlighting the significant value that DVP brings to farmers, consultants, and dairy farm stakeholders. Our study analyzed data from 712 farms from December 2023, focusing on several KPIs, such as milk production, milk quality, feed efficiency, and IOFC using Dairy Herd Improvement (DHI) records. This approach showcases DV's ability to utilize minimal data inputs for detailed analysis, leveraging peer performance to set desirable and/or achievable goals. We present a case study demonstrating how the DVP platform can guide dairy farms using anonymized peer data. This approach enables users to potentially improve their IOFC by up to 35%. Our findings highlight DVP's potential as a powerful tool for generating insightful analyses, simulations, and recommendations, primarily from test-day data, but also integrated with market and estimated data. This supports more strategic decision-making in dairy management, including automatic goal-setting based on peer performance.

Remote cutaneous confocal microscopy: A multicentric prospective study evaluating diagnostic accuracy for melanoma and keratinocyte carcinoma in tertiary settings

Cutaneous confocal microscopy (CCCM) facilitates in vivo visualisation of skin at a cellular level. Use of a "store and forward" approach for remote CCM interpretation (Remote-CCM) across multiple sites has not been tested and may increase access to non-invasive diagnosis. To test the diagnostic accuracy and safety of Remote-CCM. We prospectively recruited lesions selected for biopsy for skin malignancy across five Australian tertiary dermatology centres. CCM, clinical and dermoscopy images were acquired pre-biopsy and accessed by a cloud-based platform for interpretation by CCM readers. CCM diagnosis was compared to histopathology results. Amongst the 201 lesions included, melanoma was the most common malignancy (34/72, 47.2%). Of the 89 lesions (44.8%) potentially 'saved' from biopsy, 80 (90%) were truly benign lesions and 9 (10.1%) were missed malignant lesions of MIS (n=7) and SCC (n=2). No invasive melanomas were missed. Sensitivity of remote-CCM for detection of malignancy was 89% (95%CI 79-95%) and specificity was 64% (95%CI 55-73%). The study recruited from high-risk populations and excluded lesions that were not biopsied. Remote-CCM has comparable accuracy to bedside-CCM and safely reduces unnecessary biopsies. Potential SCCs are not appropriate for remote-CCM. Follow-up of borderline melanocytic lesions is recommended.

IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New EnergyConservation

An IoT-enabled motor drive vehicle integrates Internet of Things (IoT) technology with traditional vehicle systems to enhance control, monitoring, and automation. Sensors and connected devices within the vehicle collect real-time data on parameters such as speed, battery status, motor performance, and environmental conditions. This data is transmitted to cloud-based platforms for analysis, enabling remote diagnostics, predictive maintenance, and optimization of vehicle performance. IoT integration also facilitates features like vehicle tracking, smart navigation, and user-specific adjustments, improving overall efficiency, safety, and user experience. This paper investigates the utilization of IoT enabled Programmable Logic Controller (PLC) technology to enhance fault detection in new energy vehicle (NEV) motor drive systems. With the increasing adoption of electric vehicles, ensuring the safety and reliability of motor drive systems becomes paramount. The study begins with a comprehensive review of existing literature, examining various fault detection methodologies and technologies. Subsequently, simulation analyses are conducted to evaluate the performance of IOT enabled PLC-based fault detection algorithms under different operating conditions. This paper presents the numerical results of fault detection in new energy vehicle (NEV) motor drive systems using Programmable Logic Controller (IOT enabled PLC) technology. Through comprehensive simulations and experimental validations, the IOT enabled PLC-based fault detection algorithms achieved an average detection accuracy of 93% across various fault scenarios. The response time of the fault detection system.

CataEx: A multi-task export tool for the Google Earth Engine data catalog

Satellite imagery is provided by different missions such as ASTER, MODIS, Sentinel, Landsat, IKONOS, GeoEye, SPOT, WorldView, Pléaides, or RapidEye. One of the major encumbrances is the digital volume that satellite imagery claims during download, storage, and processing. This inconvenience has been overcome since 2010 by the Google Earth Engine, a cloud-based platform for global geospatial analysis dedicated to users who are not necessarily remote sensing specialists.However, compatibility with traditional desktop or web-based GIS software remains tricky as bringing satellite imagery from the Google Earth Engine to another software requires a coded export via JavaScript or Python.We present the multi-functional code tool CataEx in JavaScript to exemplify several essential types of computations (i.e., filtering of image collections, cloud masking, index and histogram generation, and layer creation) before exporting images as GeoTIFFs. CataEx is kept deliberately simple without much "sophisticated" code language to allow JavaScript beginners to get familiar with basic coding concepts and develop their own scripts.

Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.

Forest Fire Hazard Forecasting Based on Google Earth Engine Open Satellite Data

Introduction. Forest fires cause significant damage to both the natural fund and the national economy. In recent years, their harmful influence has increased due to global warming, and in Ukraine also due to the armed conflict. Thus, traditional methods of patrolling (ground and air) are not only costly, but also dangerous due to mines and possible shelling of border areas. Therefore, the role of surveillance using satellite systems is increasing. Space monitoring is more efficient and covers a larger area of ??the Earth's surface. Another important advantage is open access to information. The purpose of the paper is to build a mathematical model for determining fire danger based on climatic and biophysical satellite data for the forests of Ukraine, as well as a similar climatic zone with the possibility of further scaling to other climatic regions and types of vegetation cover. To adhere to the principles of open science, Google Earth Engine (GEE), a cloud-based platform that provides open access to dynamic collections of pre-processed Earth remote sensing results, was chosen. Results. Climatic and biophysical data for the forests of Ukraine for the years 2017-2020 were collected using the tools of the Python library for working with GEE data. Further, the obtained data were processed by two methods: linear (PCA) and non-linear (UMAP) in order to obtain statistically independent attributes. Both obtained datasets were subjected to statistical processing using the Bayesian method. Finally, for each point on the map for which information was collected, an indicator was calculated that predicted fire danger if the obtained value was greater than 1 and its absence in the opposite case. The resulting model showed its efficiency on training data. On the test dataset (data on Polish forests for the same period), the results turned out to be worse, in particular, the model using PCA did not predict absence of fire danger, and the model using UMAP generally showed lower performance. This can be due to both the imperfection of the model and the small size of the test dataset or factors unrelated to natural processes (in particular the human factor). Conclusions. An approach to forest fires forecasting based on satellite data is proposed. The obtained results indicate that the model is already effective at this stage, although machine learning methods have not yet been applied. However, it needs further improvement, so work on the model will be continued. Along with improving the quality of forecasts, attention will be paid to the geographic expansion and the creation of a web application. Keywords: fire danger in forests, satellite data, correlation, PCA, UMAP, hard-to-reach areas.

Artificial-Intelligence Cloud-Based Platform to Support Shared Decision-Making in the Locoregional Treatment of Breast Cancer: Protocol for a Multidimensional Evaluation Embedded in the CINDERELLA Clinical Trial.

Shared decision-making (SDM) plays a crucial role in breast cancer care by empowering patients and reducing decision regret. Patient decision aids (PtDAs) are valuable tools for facilitating SDM, now available in digital and artificial intelligence (AI)-powered formats to offer increasingly personalized contents. The ongoing CINDERELLA clinical trial (ClinicalTrials.gov: NCT05196269) evaluates an innovative AI cloud-based approach using a web platform and a mobile application (CINDERELLA APProach) versus the conventional approach to support SDM in breast cancer patients undergoing locoregional treatment. This protocol outlines a trial-based multidimensional evaluation, encompassing economic, financial, implementability, and environmental considerations associated with the CINDERELLA APProach. A within-trial cost-consequence and cost-utility analysis from a societal perspective will be performed using patient-level data on outcomes and resource use. The latter will be valued in monetary terms using country-specific unit costs or patient valuations. A budget impact analysis will be performed over 1 and 5 years from the budget holder perspectives. The CINDERELLA APProach implementability will be assessed through an evaluation of its usability, acceptability, organizational impact, and overall feasibility. The environmental impact will be quantitatively assessed across several dimensions, such as quantity, appropriateness, and emissions, supplemented by qualitative insights. Overall, data for the evaluation will be gathered from patient questionnaires, interviews with patients and managers, focus groups with healthcare professionals, and app electronic data. A thorough understanding of the broad consequences of the CINDERELLA APProach may foster its successful translation into real-world settings, hopefully benefiting breast cancer patients and clinical practice.

Designing a Data Dashboard for Health Departments and Overdose Fatality Review Teams

IntroductionOverdose Fatality Review (OFR) teams within health departments rely on data from multiple isolated sources to study the opioid crisis. Recently, we linked electronic health record data with state agency data to form a regional Substance Misuse Data Commons (SMDC) using privacy-preserving record linkage. Our goal in this study was to use human factors design principles to design a data dashboard for OFR teams using the linked datasets that will overcome current barriers within OFR workflows. MethodsWe utilized NASA task load surveys, semi-structured interviews, and design sessions with end users to identify data needs for an optimal dashboard design. We assessed current workloads, data collection processes, and desired future state. We subsequently performed iterative design sessions for the generation and evaluation of low-fidelity prototypes. To overcome issues with privacy and security, we used synthetic data in a cloud-based platform to represent the SMDC for simulation. ResultsEleven OFR organizers participated. Pre-dashboard surveys on existing workflow showed high mental workload associated with data aggregation and case review, identifying a need for more accessible, comprehensive data. In our low-fidelity dashboard demo with synthetic data, iterative design adjustments were made in data visualization, storyline organizations, and theme-based data aggregation across pre-hospital and hospital data. ConclusionsWe refined the data dashboard prototype into a high-fidelity version, set for further usability and human factors evaluation. We addressed privacy and security concerns through synthetic data use while the real-world data is maintained in a HIPAA-secure Azure cloud environment with access for approved users.

Exploring Pedagogical Dynamics via Teachers’ Interactions with Digital Textbook Platforms

Understanding the pedagogical dynamics that occur when teachers interact with digital textbook devices (DTDs) in the artificial intelligence (AI)–enhanced teaching environment is critical for education success. Previous research on the use of DTDs has primarily focused on subjective and intrusive questionnaire surveys, with little attention paid to the accompanying data when teachers use AI tools. This study aims to mine teachers’ behaviors from a data-driven perspective, exploring actual practices as they integrate DTDs into their teaching. We collected teachers’ behavioral data from a cloud-based curriculum platform, covering four subjects over a four-month period. Then, we used the lag sequential analysis (LSA) technique to investigate state-change patterns and the motif discovery technique to uncover hidden sequential patterns in teachers’ behaviors. Furthermore, using two mutually dependent recurrent neural networks, we predicted teachers’ future behaviors while operating DTDs, providing a more complete understanding of their pedagogical dynamics. Our findings show significant behavioral patterns when teachers use AI–embedded digital devices in the smart teaching environment. Additionally, the predictive model performed well in predicting teachers’ future behaviors, which can be used to improve the design of human–computer interaction in digital textbooks.

Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins

Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient’s circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins.

A Comparative Analysis of Cloud-Based Healthcare Platforms through Effective Machine Learning Approaches

The integration of cloud computing and machine learning in healthcare platforms has revolutionized the delivery of medical services, offering scalable solutions for data storage, processing, and analysis. This study presents an overview of various cloud-based healthcare platforms, focusing on the effectiveness of machine learning approaches in enhancing patient care and operational efficiency, and compares the performance of different machine learning models employed in the platforms for diverse healthcare applications. The findings provide insights into the strengths and limitations of existing cloud-based healthcare solutions, guiding healthcare providers and policymakers in selecting optimal platforms for improved patient outcomes and resource utilization.

IoT-Enabled energy conservation in residential Buildings: Machine learning models for analyzing annual solar power consumption

This study investigates enhancing the self-consumption of solar panels by integrating thermal and electrical energy storage using lithium-ion batteries in a duplex building over a year. Machine learning algorithms are applied to understand energy usage patterns. An IoT-connected model is introduced to reduce electricity expenses by boosting consumption with solar energy and extending solar panel lifespan through optimization. The model includes lithium-ion batteries for energy storage and a thermal cooling system to regulate panel temperatures. Data collected from sensors monitoring occupancy, temperature, humidity, energy, and thermostats are sent to a cloud-based platform via IoT. Results show peak monthly energy conservation measures of 41.6 kW and 28 kW during summer for the two residences. Winter witnesses a reduction of 13.9 % and 7 % in energy consumption, while autumn sees an 18 % decrease for one residence and stability for the other. Real-time IoT monitoring aids model training, with the Gaussian Probability model emerging as the most accurate. Optimization analysis underscores the thermal cooling system’s efficacy in improving solar panel performance and reducing electricity costs. Integration of machine learning and IoT curtails energy consumption to 22 kWh and 15.7 kWh for the respective residences, offering inhabitants free energy from renewable sources and ensuring swift recovery of solar panel installation costs.

GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed.

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.

User guide for Social Determinants of Health Survey data in the All of Us Research Program.

Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions. This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey. We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.

Development of an IoT-Based Air Conditioning Meter for Real-Time Operational Status

Air conditioning systems have become a staple in households and businesses, particularly in regions with tropical climates, where maintaining indoor comfort is crucial. However, the widespread use of air conditioning units has led to a significant increase in energy consumption, contributing to higher electricity bills and environmental concerns, particularly in terms of carbon emissions. Traditional air conditioning units often operate without real-time monitoring, leading to inefficiencies and unnecessary energy use. In response to these challenges, there has been a growing interest in the development of smart, energy-efficient solutions that can monitor and control air conditioning systems remotely. The advent of the Internet of Things (IoT) has opened up new possibilities for integrating sensors and microcontrollers with cloud-based platforms, enabling real-time data collection and analysis. This research builds on these advancements by developing an IoT-based meter designed to monitor the operational status of air conditioning units, providing users with immediate feedback and enabling more efficient energy management.

Butterfly Image Classification Using Convulational Neural Network[CNN

Butterfly species identification through image classification is now a major use of computer vision, utilizing supervised learning methods to classify different types of butterflies based on images. This article gives a detailed overview of the latest progress in classifying butterfly images through supervised learning techniques on Google Colab, a widely-used cloud-based platform for machine learning projects. The review starts by discussing the significance of precise butterfly categorization for biodiversity research and conservation endeavors. It then goes into specifics about different methods used in supervised learning for this purpose, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest neighbors (k-NN). The review discusses the pros and cons of using these methods on butterfly image data, emphasizing on accuracy, efficiency, and generalization. Special focus is placed on the preprocessing procedures necessary to improve image quality and extract features, including image augmentation, normalization, and feature scaling. The article also investigates various butterfly image datasets that are accessible to the public, analyzing how they are used for training and assessing classification models. Google Colab is highlighted as a potent instrument for creating and testing these models because of its convenience, user-friendliness, and compatibility with leading machine learning libraries such as TensorFlow and PyTorch. Furthermore, the article examines recent research and initiatives that have effectively utilized butterfly image categorization with Colab, demonstrating ideal methods and insight gained. Image Preprocessing, Feature Extraction, Machine Learning Libraries, TensorFlow, PyTorch, Image Augmentation, Publicly Available Datasets.

Enhanced machine learning approaches for OSA patient screening: model development and validation study

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7–84.1%), 69.9% (95% CI 69.8–69.9%), 86.5% (95% CI 81.6–91.3%), 61.5% (95% CI 56.6–66.4%), 53.2% (95% CI 47.7–58.7%), 65.9% (95% CI 60.2–71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

Minimizing THD Using Integrated Multilevel Maximum Power Tracking Inverters

This work proposes a new Modified Multilevel Inverter (MMLI) and provides a comprehensive comparison with Conventional Cascaded H-bridge Inverters. The MMLI features fewer switching devices compared to the conventional H-Bridge Inverter for 9-level voltages and higher. Maximum Power Point Tracking (MPPT) incorporated with a Boost converter ensures a constant output from photovoltaic (PV) arrays, which is then fed to the inverter to achieve the desired number of voltage levels. To enhance the performance and efficiency of the system, IoT technologies were integrated for real-time monitoring and control. Smart sensors and cloud-based platforms were utilized for data collection and analysis, enabling precise control of the MPPT and inverter systems. The integration of IoT resulted in significant improvements in the system's dynamic response, energy conversion efficiency, and overall reliability. The results were validated through simulations in Simulink, with outcomes presented and compared for voltage waveform and harmonic spectrum. The integration of IoT technologies provided substantial benefits, showcasing the interdisciplinary approach of this research in reducing Total Harmonic Distortion (THD) while optimizing inverter operations

Remote sensing of artisanal and small-scale mining: A review of scalable mapping approaches

Artisanal and small-scale mining (ASM) significantly influences the socio-economic development of many low-to-middle-income countries, albeit sometimes at the expense of environmental and human health. Characterized by its labor-intensive extraction from confined (<5 ha) or peripheral mineral reserves, congregated ASM practices can rival the spatial footprint of industrial mines. The unregulated and informal nature of many ASM activities presents monitoring challenges that remote sensing (RS) methods aim to address. While local-scale ASM mapping has seen success, scaling these methods to regional or global levels remains unclear. We review literature on mapping ASM to determine: (1) if studies represent the global distribution and diversity of ASM activities, (2) how ASM's unique characteristics influence the choice of RS methods, and (3) which RS approaches are the most accurate and cost-effective. We found current studies disproportionately focused on ASM regions in Africa, which highlights the need to extend the research to other regions with unique ASM characteristics, such as coal and sand mining in India and China. The selection of RS approaches is heavily influenced by local ASM contexts, the scale of analysis, and resource constraints such as funding for high-resolution imagery and validation data availability. We argue that accurate regional-scale ASM mapping (>100,000 km2) requires innovative combinations of data and methods to overcome data management and storage challenges. Local community participation, including miners, is vital for on-ground mapping and monitoring capacity. We outline a research agenda needed to develop a range of approaches for mapping and monitoring ASM in under-studied regions. By synthesizing effective methods, we provide a foundation for generating accurate and comprehensive spatial data, addressing the issues of inaccurate and incomplete data that global ASM platforms aim to resolve. This spatial data can guide policymakers, NGOs, and businesses in making informed decisions and targeted interventions to improve ASM sector safety, sustainability, and efficiency. Leveraging cloud-based geoprocessing platforms, with regularly updated global satellite image archives, combined with crowd-sourced on-ground information offers a potential solution for sustained regional-scale monitoring.