Fusing Earth Observation, Volunteered Geographic Information and Artificial Intelligence for improved Land Management

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

The growing availability of Earth Observation (EO) data enables monitoring of terrestrial ecosystems at unprecedented spatio-temporal resolutions. In practice, however, effective use of EO data remains constrained by substantial technical barriers. Working with raw, multi-modal EO imagery requires specialised domain expertise, large data transfers, access to high-performance computing infrastructure, and advanced machine learning (ML) skills. These requirements limit the accessibility of EO-based analytics for many downstream applications.Geospatial foundation models (GFMs) provide a promising alternative by learning general-purpose representations from large volumes of unlabeled EO data. By decoupling representation learning from downstream task modelling, GFMs allow users to exploit expressive features from modern deep learning models with limited EO or deep learning expertise and modest computational resources.In this work, we investigate embedding-based GFM workflows for forest disturbance monitoring, where timely inference and regional customisation are often more critical than maximising absolute predictive accuracy. Forest disturbances such as logging, windthrows, fires, pests, and diseases can occur abruptly and require rapid detection to support conservation, policy-making, and risk-management efforts.Machine learning methods for forest disturbance detection from EO data are well established and have shown strong performance on regional benchmarks. However, much of this work remains confined to academic demonstrations and is rarely translated into operational monitoring systems. Existing forest monitoring tools, including those aggregated by Global Forest Watch, typically rely on region- and sensor-specific models with limited feature expressivity. These systems may benefit from the rich multi-modal and spatio-temporal representations learned by GFMs, provided such embeddings can be accessed through scalable and practical deployment pipelines.We build on a pipeline designed to deliver on-demand, location- and time-specific geospatial embeddings as a service. Embeddings are generated server-side from raw EO data, compressed, and distributed as lightweight representations. End users interact only with these embeddings, which can be analysed using simple models such as linear probes or small decoders. This approach removes the need for the user to manipulate raw EO data, download large multi-modal datasets, or train and deploy large deep learning models, enabling rapid adaptation to local contexts with limited annotations and modest computational resources.We present preliminary results demonstrating the feasibility of this approach for forest disturbance detection and discuss its strengths and limitations relative to bespoke, fully supervised image-based models. While GFMs may not be optimal for applications with abundant annotations and stringent accuracy requirements, embedding-based services are particularly well suited to time-sensitive and regionally adaptive monitoring scenarios. Overall, this work illustrates how releasing geospatial embeddings as a product or service can lower barriers to EO-based forest monitoring and support faster, more inclusive environmental decision-making.

The appearance of OpenStreetMap (OSM) in 2004 sparked a phenomenon known as Volunteered Geographic Information (VGI). Today, VGI comes in many flavours (e.g. toponyms, GPS tracks, geo-tagged photos, micro-blogging or complete topographic maps) and from various sources. One subject that has attracted research interest from the early days of VGI is how good such datasets are and how to combine them with authoritative datasets. To this end, the paper explores three intertwined subjects from a quality point of view First, we examine the topo-semantic consistency of OSM data by evaluating a number of rules between polygonal and linear features and then paying special attention to quality of Points of Interest (POIs). A number of topo-semantic rules will be used to evaluate the valididy of features’ location. The focus then turns to the use of geo-tagged photos to evaluate the location and type of OSM data and to disambiguate topological issues that arise when different OSM layers overlap.

<p>Precision Agriculture (PA) applied on a widespread basis can be a building block for reduced ecosystem degradation without compromising food security. One problem of farmers in the implementation of PA applications is the lack of high spatial resolution soil information.</p><p>The EU-funded research project ‘pH-BB: Precision liming in Brandenburg’ aims at developing innovative nutrient management strategies based on proximal soil sensing data.  For this study, the pH-BB project provided us the data of the 361 soil samples, which were taken at an 800-hectare farm in Brandenburg close to Frankfurt (Oder) and analyzed in the laboratory for the texture fractions clay, silt, and sand. We used the Google Earth Engine to process remote sensing earth observation (EO) data of 1474 Sentinel-1 (S1) SAR and 85 cloud free Sentinel-2 (S2) scenes available at the study site during the period 2016-03-01 - 2021-11-09. Vegetation and soil indices were computed with optical S2 data and backscatter in VV and VH polarization were extracted from the S1 datasets. To derive long-term persistent patterns in the EO data, simple statistical parameters such as coefficient of variation, standard deviation, maximum pixel value, etc. were calculated along the temporal domain of the EO data. Together with calculated terrain attributes, 24 covariate grids were finally available for model building. Reference samples (rs) were randomly divided into a training dataset (70% of rs) and a validation dataset (30% of rs). Pixel values of the covariate datasets at the sampling locations were added to the rs datasets.</p><p>A random forest machine learning algorithm was applied to the training dataset to train two individual models for the alr-transformed target variables silt and sand using the covariates. The developed models were then applied to the gridded datasets to predict maps for the alr-transformed target variables silt and sand. The final maps of all 3 texture fractions clay, silt, and sand were computed by back-transforming the predicted alr silt and sand grids.</p><p>In the derived models EO covariates showed the highest level of importance. Comparison of the prediction results with the validation data set showed that the spatial distribution, of the clay, silt, and sand fractions was predicted, with a root mean square error (rmse) of 6.2, 5.3, and 9.7 mass-%, respectively. A classification of the predicted maps according to the German KA5 scheme showed that especially in the sand dominated soil classes the prediction errors were lower, whereas they increased in the loamy soil classes (dependent on the clay content).</p><p>With a rmse of 5.3 - 9.7 mass-%, the performance of the approach shows good potential for surface soil texture assessments even at high resolution or for global applications or as an initial guess for soil mapping with high resolution proximal soil sensing devices.</p>

Like many construction industry professions, quantity surveying (QS) has been around for many years and has undergone many changes to reflect developments within the wider industry and society. The proliferation of computers into the design process starting from the 1980’s leading up to the rise of Building Information Modelling has particularly led to significant changes in the design and construction landscape. In the UK for instance, the proliferation of BIM and possible demise of traditional Bill of Quantities, with the concurrent rise of smart buildings/cities with exploitation of Big Data (BD), artificial intelligence (AI) and Machine Learning (ML). It implies that QS practices need to reflect on emerging products and services that can promote construction quality and productivity as well as their own professional development. With the decline of traditional QS roles and increased focus on speed of construction, there may be opportunities for different roles for quantity surveyors when dealing with data-driven needs of advanced clients such as BIM managers and Project managers. Additionally, there is need to improve the market value for traditional QS practices when dealing with less innovative clients with less time constraints, which inadvertently contributes to a skills gap which will allow practices to charge more for the traditional services. This study is an exploratory research based on secondary data, which is aim at understanding BIM adoption and related technical advancements that represent innovative and emerging roles for QS professionals to meet the growing demands in the industry. The findings will ignite and support the need for changes in practice, professional education as well as attitudinal behaviour required toward the UK’s Construction 2025 goals. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Applied Science University Journal of Natural Sciences following peer review. The definitive publisher-authenticated version Seidu et al (2020) Innovative Changes in Quantity Surveying Practice through BIM, Big Data, Artificial Intelligence and Machine Learning, is available online at: DOI:10.18576/asuj/040105

Continual learning in medical devices: FDA's action plan and beyond

Although crowdsourcing drives much of the interest in Machine Learning (ML) in Geographic Information Science (GIScience), the impact of uncertainty of Volunteered Geographic Information (VGI) on ML has been insufficiently studied. This significantly hampers the application of ML in GIScience. In this paper, we briefly delineate five common stages of employing VGI in ML processes, introduce some examples, and then describe propagation of uncertainty of VGI.

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical tasks, the general aim of Machine Learning is to construct algorithms able to generalize and predict in previously unseen situations based on some set of examples. Given some finite information, Machine Learning provides ways to exract knowledge, describe, explain and predict from data. Kernel Methods are one of the most successful branches of Machine Learning. They allow applying linear algorithms with well-founded properties such as generalization ability, to non-linear real-life problems. Support Vector Machine is a well-known example of a kernel method, which has found a wide range of applications in data analysis nowadays. In many practical applications, some additional prior knowledge is often available. This can be the knowledge about the data domain, invariant transformations, inner geometrical structures in data, some properties of the underlying process, etc. If used smartly, this information can provide significant improvement to any data processing algorithm. Thus, it is important to develop methods for incorporating prior knowledge into data-dependent models. The main objective of this thesis is to investigate approaches towards learning with kernel methods using prior knowledge. Invariant learning with kernel methods is considered in more details. In the first part of the thesis, kernels are developed which incorporate prior knowledge on invariant transformations. They apply when the desired transformation produce an object around every example, assuming that all points in the given object share the same class. Different types of objects, including hard geometrical objects and distributions are considered. These kernels were then applied for images classification with Support Vector Machines. Next, algorithms which specifically include prior knowledge are considered. An algorithm which linearly classifies distributions by their domain was developed. It is constructed such that it allows to apply kernels to solve non-linear tasks. Thus, it combines the discriminative power of support vector machines and the well-developed framework of generative models. It can be applied to a number of real-life tasks which include data represented as distributions. In the last part of the thesis, the use of unlabelled data as a source of prior knowledge is considered. The technique of modelling the unlabelled data with a graph is taken as a baseline from semi-supervised manifold learning. For classification problems, we use this apporach for building graph models of invariant manifolds. For regression problems, we use unlabelled data to take into account the inner geometry of the input space. To conclude, in this thesis we developed a number of approaches for incorporating some prior knowledge into kernel methods. We proposed invariant kernels for existing algorithms, developed new algorithms and adapted a technique taken from semi-supervised learning for invariant learning. In all these cases, links with related state-of-the-art approaches were investigated. Several illustrative experiments were carried out on real data on optical character recognition, face image classification, brain-computer interfaces, and a number of benchmark and synthetic datasets.

Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies that are redefining cloud computing. The incorporation of AI and machine learning into cloud platforms improves productivity, scalability, and flexibility, allowing organizations to handle massive amounts of data and make intelligent choices in real time. This study investigates the symbiotic relationship between AI/ML and cloud computing, focusing on how cloud architecture provides the computational capacity needed for AI and ML models while AI improves cloud resource allocation. The article discusses key breakthroughs such as AI-driven automation in cloud operations, predictive analytics, and intelligent application deployment. The report emphasizes the democratization of AI capabilities via cloud services, which make them available to small and medium-sized organizations (SMEs) without requiring considerable infrastructure investment. It also investigates the security hurdles, ethical concerns, and compliance issues that come with data-intensive AI systems on the cloud. Real-world applications include AI-powered recommendation systems, fraud detection, and tailored consumer experiences, demonstrating the strength of this synergy. The report indicates that incorporating AI and ML into cloud computing is critical for expanding technological landscapes, driving creativity, and helping enterprises to remain competitive. However, addressing data privacy, ethical AI usage, and fair access to cloud-based AI resources are crucial for long-term progress. This study provides insights for researchers and practitioners on leveraging AI/ML in the cloud to meet evolving technological and business needs.

Earth observation (EO) data are remote sensing image data obtained by air-borne or space-borne sensors, which has the characters of distribution and heterogeneity. Because these data are owned by different space agencies. With Petabyte-scale data volumes of Earth Observation (EO) obtained, the EO data has been one of the most important part of big data. The characters of distribution and heterogeneity of big EO data storage and processing systems owned by different space agencies brings challenges to integrate and use these data. To meet the big EO data challenges, the new generation Spatial data infrastructure is needed to provide the solution and implementation technique to big EO data storage, access, process and analysis. The concept and architecture of Next-generation national EO Spatial Data Infrastructure (EO-SDI) is proposed in the paper. Key techniques are introduced to solve the complexity of distributed and heterogeneous EO data integration and on-demand processing. An interoperable national EO data system of systems is developed to integrate distributed EO archives of eight EO data centers.

In this modern world, while people are used to living a life of competition and a fast environment, artificial intelligence (AI) and machine learning have been established to serve people. AI and machine learning developed on the concept of repetition and learning. Now some companies use AI to replace or support humans in jobs that operate according to patterns. AI and machine learning are unlike humans, they have no bias since they are not exhausted. Also, employers do not have to pay for overtime. Some companies even have AI to calculate risk and return for management to make decisions. In recent years, many companies start considering using AI and machine learning to replace human employees for positions that do not require particular special skills which need by humans. Recently, machine learning has been developed to generate pictures from the text. This means even people who are not good at art at all can command AI to generate pictures, particularly for them. Accessing those AI is simply just by logging in to the program online. When generating pictures is so easy as that, what will AI artists play next role in art circulation? In this paper, we conduct mixed methodology by qualitative and quantitative methods to research the tendency of AI artists in the future.

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

Artificial Intelligence (AI) has grown extensively in recent times and already encroaches into our lives, for example, with smart cars and smart homes. AI current and future trends in areas such as smart cars, face recognition, robotic surgeries, or automatic disease classification, focus on hybridization to improve the performance of smart expert systems. Despite the huge growth of various AI and machine learning techniques, such as decision trees, data mining, or deep convergence learning, there are still many challenges and threats which limit the performance of these smart expert systems. This special issue focuses on new directions in hybrid AI and machine learning methodologies to address current research problems. It focuses on bringing together leading-edge articles on hybrid AI techniques for smart expert systems for various applications. From about 25 articles submitted, 4 papers were selected based on the review process. Each paper was reviewed by at least two independent reviewers and undertook at least one round of review. Their contributions are summarized below. Raghavendra et al. (2019) has proposed a research work on accurate and early detection of breast cancer using effective imaging modalities with medical image analysis. The research results have shown that two-dimensional synthesized mammogram imaging and conventional full-field digital mammography (FFDM) are two important imaging modalities which can be used for screening breast cancer. The developed method achieved an average performance of 92.9% accuracy using a probabilistic neural network classifier for FFDM images with tenfold cross validation. The obtained results can be used as a distinct system in rural hospitals. Gupta et al. (2020) has done their research studies on to detect infected leaves using machine learning techniques. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private data set of 236 images. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for the classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. Hammad et al. (2020) has found a new authentication approach based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals. This work shows two novel deep neural network (DNN) models (convolutional neural network (CNN) and ResNet-Attention) using ECG signals for human authentication and the proposed CNN algorithm achieved an accuracy of 98.59% and 99.72% using PTB and CYBHi, respectively. The results obtained by the developed model proved that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. Teles et al. (2020) has devised a decision support system on credit operations which can provide different estimations of expected recovery based on the same data sets. This research work classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. Results reveal that while linear regression can be used for the prediction of a continuous variable in the credit process using collateral, it is not suitable for categorical data. Out of linear and logistic regressions, linear regression is more applicable in determining groups of variables that can significantly predict an outcome. We would like to take this opportunity to thank Dr. Jon G. Hall, Editor-in-Chief of Expert Systems, The Journal of Knowledge Engineering, for the privilege to edit this special issue. Gustavo Ramírez González is a professor at department of telematics engineering in Universidad del Cauca, Colombia. His research interests include image processing, secure communication, and machine learning. He has published several research papers in reputed journals and served as a Guest Editor for several Special Issues at many journals.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.