Temporal seagrass mapping using machine learning and field-validated satellite imagery to inform restoration and management

Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping.

Choung, Y.-J. and Jung, D. 2021. Comparison of machine and deep learning methods for mapping sea farms using high-resolution satellite image. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 420–423. Coconut Creek (Florida), ISSN 0749–0208. Previous research had shown that the supervised machine learning approach performed better than unsupervised machine learning for mapping sea farms using a high-resolution satellite image. The present work compares a support vector machine (SVM), which represents the supervised machine learning approach, and a deep neural network (DNN), which represents the deep learning approach, for mapping sea farms using KOMPSAT-3 satellite images acquired in the South Sea of South Korea. First, coastal maps were generated from the image source given by SVM and DNN. Next, the above-water and underwater farms were detected separately from both the maps based on the minimum and maximum thresholds. Finally, the detection accuracy of both the above-water and underwater farms from both coastal maps was assessed. Statistical results showed that deep learning (DNN) provided better performance than machine learning (SVM) for detecting above-water farms from the given high-resolution satellite image, while both DNN and SVM yielded the same performance for underwater farms. However, a few errors occurred in the detection because of the limitations of the pixel-based classification approaches. In future research, the deep learning algorithm combined with object-based classification, such as the convolutional neural network, can be used to detect sea farms from the given high-resolution image more accurately.

This study uses bibliometric analysis to systematically map research trends, knowledge structure and evolution of information technology (IT) in hospital antimicrobial stewardship (AMS) over the last 20 years. A Web of Science Core Collection search (2000-2025) yielded 258 English-language publications on IT applications in hospital AMS. A bibliometric analysis, utilising CiteSpace and Bibliometrix, quantitatively evaluated domain evolution through temporal analysis, network mapping, keyword clustering, burst detection and examination of highly cited publications. The bibliometric analysis reveals a fluctuating yet overall increasing trend in annual publications, peaking at 49 articles in 2024. The US (131 publications) and European nations demonstrate significant research output (centrality >0.2), with major collaborative networks coalescing around institutions including Harvard University and Imperial College London. Keyword analysis identifies 'AMS' as a core theme, closely associated with technological keywords such as 'machine learning (ML)', 'clinical decision support systems (CDSS)', 'electronic health records (EHR)' and 'artificial intelligence (AI)'. Emerging trends suggest a shift in research focus from foundational strategies to data-driven prediction of antimicrobial resistance (AMR) and precision interventions. Highly cited literature emphasises the integration of EHR and ML technologies for optimising prescriptions and predicting resistance patterns. IT-driven AMS research has shifted from empirical management to data science. Despite EHR integration, and ML and CDSS support, challenges remain in data standardisation, technical deployment and ethics. Future work must emphasise global collaboration, standardisation, design refinement and ethical guidelines, and provide clear algorithm explanations to enhance AMR mitigation.

The integration of Artificial Intelligence (AI) into Industry 4.0 has revolutionized industrial processes through the implementation of intelligent automation, predictive analytics, and interconnected systems. This study conducts a comprehensive bibliometric analysis to map the research trends, thematic evolution, influential authors, and international collaboration networks within the domain of AI and Industry 4.0. Data were retrieved from the Scopus database, focusing on peer-reviewed journal articles published between 2013 and 2024. Using VOSviewer for data visualization, the analysis reveals five major thematic clusters, with “Industry 4.0,” “machine learning,” “Internet of Things,” and “smart manufacturing” as dominant keywords. The temporal mapping indicates a shift from core technical research toward more strategic themes such as sustainability, digital transformation, and Industry 5.0. Author collaboration networks show regional clusters with limited interdisciplinary integration, while country-level analysis highlights India, Germany, China, and Italy as major contributors. The findings emphasize the field's dynamic growth and underscore the need for more inclusive, cross-disciplinary, and globally connected research agendas to fully realize the potential of AI in the context of Industry 4.0.

Amidst the continually changing climate and the rise in natural disasters, it is crucial to strengthen resilience against these calamities. This chapter explores the dynamic intersection of machine learning and natural disasters, revealing how advanced technologies reshape disaster management. In the face of escalating challenges posed by earthquakes, floods, and wildfires, machine learning emerges as an innovative solution, offering proactive approaches beyond conventional reactive methods. The narrative unfolds by tracing the evolution of disaster management, highlighting the transformative impact of machine learning on early warning systems. It explores predictive analytics and risk assessment, elucidating how machine learning algorithms leverage historical data and real-time information to deepen our understanding of disaster vulnerabilities. Beyond prediction, the discourse extends to the pivotal role of machine learning in optimizing response and recovery efforts—efficiently allocating resources and fostering recovery planning. A critical dimension of this integration emerges in the analysis of remote sensing and satellite imagery, where machine learning algorithms enable more accurate and timely disaster monitoring. The exploration extends further, unraveling the interconnectedness of various hazards and emphasizing how machine learning facilitates a holistic understanding. The synergy between machine learning and traditional knowledge systems comes to the forefront, recognizing the significance of integrating local wisdom into predictive models. The discourse broadens to encompass policy implications, international collaboration, and ethical considerations embedded in machine learning for disaster management. The integration of machine learning in humanitarian aid efforts and its contribution to environmental sustainability are scrutinized, offering a comprehensive understanding of the multifaceted relationship between machine learning and natural disasters. In the ever-evolving landscape of natural disaster management, the fusion of machine learning and human expertise opens new avenues for innovation. One emerging trend is the integration of real-time social media data into machine learning algorithms. By analyzing user-generated content, sentiment analysis, and geospatial information from platforms like Twitter and Facebook, these algorithms can provide rapid insights into the unfolding dynamics of a disaster. This not only enhances the timeliness of response efforts but also fosters a more community-centric approach, incorporating the voices and experiences of those directly affected. The potential of generative adversarial networks to simulate and predict complex disaster scenarios offers a proactive paradigm shift in disaster management by enabling stakeholders to refine strategies and adapt to evolving challenges through realistic simulations. As the chapter charts the course forward, it concludes by exploring emerging trends and innovations in the symbiotic relationship between machine learning and natural disaster management.

Development of machine learning prediction models for systemic inflammatory response following controlled exposure to a live attenuated influenza vaccine in healthy adults using multimodal wearable biosensors in Canada: a single-centre, prospective controlled trial.

PurposeThis paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.Design/methodology/approachAutomated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.FindingsValidation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.Research limitations/implicationsOur attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.Practical implicationsImproving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.Social implicationsOur literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.Originality/valueUnlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Cropland extent serves as a critical determinant for advancing various Sustainable Development Goals (SDGs), and satellite-derived data has been widely used to generate cropland extent maps. To further address the global mission of high-resolution cropland extent mapping, the Food and Agriculture Organization (FAO) and the United Nations Office on Drugs and Crime (UNODC) have proposed "cropland extent mapping" as a focal theme for the 2023 Geo-AI Challenge. Organized by the International Telecommunication Union (ITU) in collaboration with the Zindi platform, tasks of this challenge are divided into two components: (1) annual cropland extent mapping in Sudan and Iran, and (2) temporal cropland mapping in Afghanistan, with pre-provided training samples across all three test regions. Throughout the challenge duration, a total of 74 participating teams submitted their solutions, with the top five teams selected based on classification accuracy of cropland extent maps, innovative methodology, and effectiveness of oral presentation. Several key scientific questions were addressed during the challenge, including optimal classification feature selection, comparative analysis of diverse Machine Learning (ML) models, and fine-tuning of ML algorithms. Importantly, all datasets, scripts, and technical reports resulting from the challenge are openly accessible to the public domain, thereby fostering collaborative advancements within the agricultural remote sensing community.

The fitness of the systems in which Machine Learning (ML) is used depends greatly on good-quality data. Specifications on what makes a good-quality dataset have traditionally been defined by the needs of the data users—typically analysts and engineers. Our article critically examines the extent to which established data quality frameworks are applicable to contemporary use cases in ML. Using a review of recent literature at the intersection of ML, data management, and human-computer interaction, we find that the classical “fitness-for-use” view of data quality can benefit from a more stage-specific approach that is sensitive to where in the ML lifecycle the data are encountered. This helps practitioners to plan their data quality tasks in a manner that meets the needs of the stakeholders who will encounter the dataset, whether it be data subjects, software developers or organisations. We therefore propose a new treatment of traditional data quality criteria by structuring them according to two dimensions: (1) the stage of the ML lifecycle where the use case occurs vs. (2) the main categories of data quality that can be pursued (intrinsic, contextual, representational and accessibility). To illustrate how this works in practice, we contribute a temporal mapping of the various data quality requirements that are important at different stages of the ML data pipeline. We also share some implications for data practitioners and organisations that wish to enhance their data management routines in preparation for ML.

Active machine learning (ML) models have started replacing the conventional methods of crops health management and farm productivity with predictive analytics. The aim of this research paper is to anticipate the health of coconut trees, an important tropical crop, with the help of ML applications. These coconut palms are strong in economic value but also ecologically significant, and they are vulnerable to an assortment of diseases and pests which, if they are allowed to flourish, may substantially reduce yield. Conventional methods of assessing coconut tree health require observations made in the field and delayed diagnostic results, which are both labor intensive. We note that coconut tree health issues have been addressed using advanced ML models for early detection and prediction in this paper. Several ML algorithms are analyzed in the study for data from several sources like satellite imagery, drone based sensors, and field data, including Convolutional Neural Networks (CNNs), Random Forest and Support Vector Machines (SVMs). With integration of these data sources, ML models can find patterns, anomalies in health problems. The paper also describes the modeling of predictive models which can project potential outbreak of the disease and pest infestation using historical data and real time observations. Various ML models are proven to have high accuracy in detecting early signs of disease and stress in coconut trees. The use of remote sensing data in conjunction with ML algorithm results in tremendous increase in predictive capability that facilitates timely interventions and directed management strategies. This paper presents a case studies on the implementation of ML models to improve coconut tree health management with the highlight of the practical benefits and challenges of the technologies involved.

The paper is devoted to agro-ecological classification of agricultural land using modern methods of geo-information data analysis and machine learning. Background. There are few works in the literature, which cover the issues of accuracy of machine learning (MLL) models for agro-ecological grouping of agricultural land. A large number of raster information layers are used to improve the accuracy of land classification from satellite images. This considerably increases the time of training and testing MMOs, producing thematic maps of agricultural land classification. This approach requires considerably high computing resources and a considerable amount of computer RAM. Raster GIS data models occupy a much larger volume than vector models. In this regard, research on automated agro-ecological (grouping, classification) of agricultural land using vector GIS models is of practical importance. Purpose. The aim of the study is to apply GIS methods, remote sensing (ERS) data and machine learning methods for agricultural land grouping. Materials and methods. The materials used were synthesized multispectral high spatial resolution Sentinel-2A images, maps of vegetation indices NDVI (Normalized Difference Vegetation Index), OSAVI (Optimized Soil Adjusted Vegetation Index), EVI2 (Enhanced Vegetation Index2), NDWI (Normalized Difference Water Index), SAVI, PVI, GDVI, MCARI, NDRE, TSAVI; topographic map, ALOS DSM (30 m/pixel) and ALOS PALSAR (12.5 m/pixel) satellite images, soil map and field survey results. Field measurements were carried out using the Triump-2 satellite geodetic receiver and included determination of coordinates of characteristic points of land plot boundaries, relief elements, and soil survey. Digital spatial model of land use was created using GIS ArcGIS and QGIS, Python engineering libraries were used in the machine learning process. Results. Agro-ecological grouping of lands was realized by the example of the farm "Zerno Sibiri" of Novosibirsk region using the following machine learning methods: Random Forest (RF) method, Decision Tree (DT), k-nearest neighbours method (KNN). The best accuracy is the RF machine learning model. The accuracy of the model averaged 97.9% (with training 99.9%, testing 98.8%, and cross validation 95.0%). The Root Mean Square Error (RMSE) is 0.006: for training sample 0.001; test sample 0.076; validation sample 0.123 respectively). The mean kappa coefficient was 0.97 (1.00 for the training sample; 0.982 for the test sample, and 0.927 for the validation sample). Conclusion. The offered method of agro-ecological grouping of agricultural lands by means of GIS, RS data and machine learning methods enabled to distinguish informative quantitative indicators of the relief. The main essence of the proposed method is to create a machine learning model (MLM) based on a spatial dataset. The spatial dataset is formed using geoinformation analysis methods and includes: geomorphometric maps, maps of agrometeorological parameters, soil map, on-farm land management map and operational-territorial units of land classification. The application of vector data model allowed for agro-ecological grouping of agricultural lands in automated mode, to accelerate labor-intensive process of raster data recognition, to increase objectivity of the work. The suggested method of agro-ecological agro-ecological grouping of lands allows taking into account the totality of relief and soil-ecological conditions indicators with the help of geoinformation analysis methods, remote sensing and machine learning.

Recent advances in algal bloom detection and prediction technology using machine learning

Deep learning and machine learning are the top ranking techniques applied in objects classification in remote sensing data. We have conducted a meta-analysis and find out that feature selection is an important achievement in Machine Learning algorithms however, the following challenges were identified; Machine learning need large datasets for training and satellite images contain a lot of noise which may be classify as an object so it is not suitable for object detection in satellite images, Detection accuracy in machine learning depend on the quality of training datasets and finally Biased feature selection may led to the incorrect classification of objects in satellite images. While Most of the deep learning techniques suffer from data preprocessing problems especially when applying in satellite images because satellite images contain a lot of noise. Therefore the requirement of quality and quantity of training datasets is very high. The designed, development, improvement and adjustment of deep learning techniques to suit a specific research is still rely on the experience of the developer which is also a challenging issue. Application of deep learning techniques in remote sense data are still in an infant state because based on our review only few numbers of articles are published from Africa countries. We have suggested that quantum computational intelligence to be applied in remote sensing data analysis.

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.

At present, the vegetation area around the world is shrinking due to the development of construction area in both urban and rural areas. It is very important to expand the present vegetation area to meet the food requirements of all people in world. In order to cope with this aspect, the present vegetation areas should be detected. In this paper, the vegetation areas in remote satellite images are detected and segmented using machine learning and deep learning algorithms. The machine learning algorithm Support Vector Machine (SVM) consists of preprocessing, feature extraction and classification modules where the deep learning algorithm consists of data augmentation and Convolutional Neural Networks (CNN) classification module. In this paper, the conventional CNN architecture is modified in this paper as the novelty in order to improve the classification accuracy of the proposed satellite image system. The segmented vegetation area is compared with manually segmented images in order to evaluate the performance of the proposed system. The developed CNN architecture produces features itself in each Convolutional layers. The CNN based vegetation area segmentation method achieves 96.03% of SEN, 98.12% of SPE and 98.07% of ACC and SVM based vegetation area segmentation method achieves 94.12% of SEN, 96.67% of SPE and 97.01% of ACC.