This repository contains all the input and output data (including maps) related to Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm. In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.

During the last decade, there has been an explosive growth in survey data and deep learning techniques, both of which have enabled great advances for astronomy. The amount of data from various surveys from multiple epochs with a wide range of wavelengths, albeit with varying brightness and quality, is overwhelming, and leveraging information from overlapping observations from different surveys has limitless potential in understanding galaxy formation and evolution. Synthetic galaxy image generation using physical models has been an important tool for survey data analysis, while deep learning generative models show great promise. In this paper, we present a novel approach for robustly expanding and improving survey data through cross survey feature translation. We trained two types of neural networks to map images from the Sloan Digital Sky Survey (SDSS) to corresponding images from the Dark Energy Survey (DES). This map was used to generate false DES representations of SDSS images, increasing the brightness and S/N while retaining important morphological information. We substantiate the robustness of our method by generating DES representations of SDSS images from outside the overlapping region, showing that the brightness and quality are improved even when the source images are of lower quality than the training images. Finally, we highlight several images in which the reconstruction process appears to have removed large artifacts from SDSS images. While only an initial application, our method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.

This repository contains the image files from Survey2Survey: a deep learning generative model approach for cross-survey image mapping. Please cite https://arxiv.org/abs/2011.07124 if you use this data in a publication. For more information, contact Brandon Buncher at buncher2(at)illinois.edu --- Directory structure --- tutorial.ipynb demonstrates how to load the image files (uploaded here as tarballs). Images were obtained from the SDSS DR16 cutout server (https://skyserver.sdss.org/dr16/en/help/docs/api.aspx) and DES DR1 cutout server (https://des.ncsa.illinois.edu/desaccess/

./sdss_train/ and ./des_train/ contain the original SDSS and DES images used to train the neural network (Stripe82) ./sdss_test/ and ./des_test/ contain the original SDSS and DES images used for the validation dataset (Stripe82) ./sdss_ext/ contain images from the external SDSS dataset (SDSS images without a DES counterpart, outside Stripe82) ./cae and ./cyclegan contain images generated by the CAE and CycleGAN, respectively. train_decoded/ and test_decoded/ contain the reconstructions of the images from the training dataset and test dataset, respectively. external_decoded/ contain the DES-like image reconstructions of SDSS objects from the external dataset (outside Stripe82).

This dataset includes sample data for the Netherlands to run the machine learning baseline as described in the paper titled Machine learning for large-scale crop yield forecasting, accessible at https://doi.org/10.1016/j.agsy.2020.103016. The software implementation of the machine learning baseline is available at: https://github.com/BigDataWUR/MLforCropYieldForecasting.

Notes:

The NUTS classification (Nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU and the UK (see Eurostat, 2016) for more details).

Data

The dataset consists of 11 CSV files. They are formatted to work as sample inputs to the machine learning baseline.

1. Crop Area Fractions (NUTS2, NUTS1): We aggregated the predictions of the machine learning baseline from NUTS2 to national (NUTS0) level by weighting them on the modeled crop area. Cerrani and López Lozano (2017) have described in detail the algorithm used to model crop areas for different NUTS levels. The data comes from the MARS Crop Yield Forecasting System (MCYFS) of European Commission's Joint Research Centre (JRC) (see Lecerf et al., 2019).
2. Centroids (NUTS2): Data includes latitude, longitude and distance to coast of the centroids of NUTS2 regions.
3. Meteo Daily Data and Meteo Dekadal Data (NUTS2): The data comes from MCYFS (see EC-JRC, 2020). By default, the implementation uses daily data.
4. Remote Sensing Data (NUTS2, see Copernicus Global Land Service, 2020): Data includes fraction of absorbed photosynthetically active radiation (FAPAR) aggregated to NUTS2.
5. Soil Data: Data includes soil moisture information that can be used to calculate soil water holding capacity. The data comes from MCYFS (see Lecerf et al., 2019).
6. WOFOST data (NUTS2): The World Food Studies (WOFOST) crop model (van Diepen et al., 1989; Supit et al., 1994; de Wit et al. 2019) is a simulation model for the quantitative analysis of the growth and production of annual field crops. It is a mechanistic, dynamic model that explains daily crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. The crop simulation is fed by weather, soil and crop data. Observed meteorological data is interpolated on a regular 25 km grid using a method based on the distance, altitude and climatic region similarity between the center of grid cells and weather stations (see Van der Goot, 1998). WOFOST runs on the intersection between the 25 km meteorological grid and soil units based on the European soil map (http://esdac.jrc.ec.europa.eu/). In order to have the output data aggregated to administrative regions such as countries or provinces, simulation units are further intersected with the boundaries of these regions. The outputs at soil unit (STU) level are aggregated to grid level in an area weighted manner. Gridded simulations are aggregated to lowest NUTS level 3 considering the arable land area of each grid, derived from GLOBCOVER and CORINE Land Cover (Cerrani and Lopez Lozano, 2017). From NUTS3 to higher levels, crop area fractions for the current year, retrieved from Eurostat, are used to weight and aggregate the output (Cerrani and Lopez Lozano, 2017).
7. National yield statistics (NUTS0): These are the official Eurostat national yield statistics (Eurostat, 2020a). We used these yield statistics as reference to compare the machine learning predictions aggregated to NUTS0 and the actual MCYFS forecasts (see van der Velde and Nisini, 2019).
8. Regional yield statistics (NUTS2): We used NUTS2 yield statistics as labels to train and evaluate machine learning algorithms. We got NUTS2 yield statistics from The Central Bureau of Statistics (CBS) of the Netherlands (NL-CBS, 2020).
9. Past MCYFS Yield Forecasts (NUTS0): These are actual forecasts made by MCYFS in the past (see van der Velde and Nisini, 2019). We used the official Eurostat national yield statistics (see point 7 above) as the reference to compare the machine learning predictions aggregated to NUTS0 and MCYFS forecasts.

Crop ID and name mapping

2 : grain maize

6 : sugar beets

7 : potatoes

90 : soft wheat

93 : sunflower

95 : spring barley

Acknowledgements

We would like to thank S. Niemeyer from the European Commission’s Joint Research Centre (JRC) for the permission to provide open access to the Netherlands data. Similarly, we would like to thank M. van der Velde, L. Nisini and I. Cerrani from JRC for sharing with us past MCYFS forecasts and Eurostat national yield statistics.

Tagged image tiles as well as the Faster-RCNN framework for automatic extraction of road intersection points from USGS historical maps of the United States of America. The data and code have been prepared for the paper entitled "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" submitted to "International Journal of Geographic Information Science". The image tiles have been tagged manually. The Faster RCNN framework (see https://arxiv.org/abs/1611.10012) was captured from:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

The global Electronic Chart Display and Information System (ECDIS) market size is estimated at USD XXX million in 2025 and is projected to reach USD XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The growth of the market is attributed to the increasing adoption of ECDIS systems in the maritime industry due to its enhanced safety, efficiency, and compliance with international regulations. The key drivers of the market include the increasing demand for improved navigational safety, the growing adoption of digital technologies in the maritime industry, and the rising awareness of environmental regulations. The market is also witnessing emerging trends such as the adoption of cloud-based ECDIS systems, the integration of artificial intelligence and machine learning technologies, and the development of autonomous navigation systems. However, the market faces challenges such as the high cost of implementation and maintenance of ECDIS systems, the lack of skilled personnel, and the cybersecurity concerns associated with digital navigation systems.

This project aims to validate the deep-learning-based segmentation tool, FastSurfer, against the established tool, FreeSurfer, in a pediatric cohort. Using MRI data from 448 subjects aged 4-18 years. . Age-specific growth charts for 15 brain regions were generated, illustrating varying growth patterns and potential clinical applications. The morphometric data, percentile curves, and code are publicly accessible.

The Knowledge Area Mapping Map market is experiencing robust growth, driven by the increasing need for efficient knowledge management and improved organizational learning within enterprises. The market's expansion is fueled by the rising adoption of digital transformation initiatives across various industries, leading to a surge in data volume and complexity. Organizations are increasingly recognizing the value of effectively mapping their knowledge assets to optimize resource allocation, enhance collaboration, and drive innovation. This market is segmented by application (e.g., training and development, research and development, strategic planning) and type (e.g., software, services). While precise market sizing requires specific data, considering a similar technology market's average CAGR of 15% and a current market size estimate of $500 million (2025), we can project a steady increase in market value, reaching approximately $700 million by 2026 and continuing this upward trend throughout the forecast period. This growth is influenced by factors such as advancements in artificial intelligence (AI) and machine learning (ML), enhancing knowledge mapping capabilities. However, challenges remain, including the integration of knowledge mapping solutions with existing enterprise systems and the need for skilled professionals to effectively manage and interpret the resulting maps. The geographic distribution of the market shows significant potential across various regions. North America and Europe currently hold a dominant market share due to the higher adoption of advanced technologies and a strong focus on knowledge management practices. However, Asia Pacific is expected to show substantial growth in the coming years due to increasing digitalization and a large pool of potential users in countries like China and India. Competitive landscape analysis reveals a mix of established players and emerging startups offering various solutions. The market’s growth will be shaped by ongoing technological advancements, regulatory changes influencing data privacy, and the evolving needs of different industry verticals. Further research into specific application areas and regional markets will provide a more granular understanding of the growth trajectory and opportunities within this dynamic space.

According to Cognitive Market Research, the global AI Server Market size will be USD 143524.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 35.20% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 53104.18 million in 2025 and will grow at a compound annual growth rate (CAGR) of 33.0% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 41622.19 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 34445.95 million in 2025 and will grow at a compound annual growth rate (CAGR) of 37.2% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 5453.94 million in 2025 and will grow at a compound annual growth rate (CAGR) of 34.2% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 5740.99 million in 2025. It will grow at a compound annual growth rate (CAGR) of 34.5% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 3157.55 million in 2025. It will grow at a compound annual growth rate (CAGR) of 34.9% from 2025 to 2033.
Inference server category is the fastest growing segment of the AI Server industry

Market Dynamics of AI Server Market

Key Drivers for AI Server Market

Increasing Demand for High-Performance AI Computing Infrastructure Across Key Sectors

A major driver of the AI server market is the rising demand for advanced computing infrastructure as artificial intelligence becomes central to sectors like healthcare, finance, and autonomous technology. With the growing integration of generative AI and machine learning across industries, there is an urgent need for high-performance servers to support complex AI workloads. This trend is prompting major players such as Dell, HPE, and Lenovo to ramp up investments in AI server solutions. Lenovo, for instance, recently saw a significant boost in revenue—up by 20% in a single quarter—largely due to increased spending on AI infrastructure, helping the company outperform profit expectations and support a broader recovery in the tech sector.

Growing Demand for Cloud-Based AI Services and Its Impact on the AI Server Market

The expansion of cloud-based AI services is a key driver propelling the AI server market forward. As organizations increasingly adopt AI for business intelligence, automation, and customer engagement, cloud platforms offer a scalable and cost-effective solution to deploy these technologies without the need for extensive on-premise infrastructure. Major cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are investing heavily in AI-optimized server hardware to support the growing demand for machine learning and inference capabilities on the cloud. These platforms allow enterprises of all sizes to access powerful AI tools and infrastructure on a pay-as-you-go basis, democratizing the use of advanced AI. This trend is accelerating the deployment of AI servers in hyperscale data centres globally, fueling the growth of the overall market. Moreover, with the increasing integration of AI into cloud-native applications and services, the need for more efficient and high-performance AI servers is only expected to rise.

Restraint Factor for the AI Server Market

High Costs of Investment and Maintenance for AI Server Infrastructure

A key restraint in the AI server market is the high cost of both initial investment and ongoing maintenance of AI-optimized server infrastructure. Deploying AI solutions, including deep learning and large-scale data processing, demands specialized hardware, such as powerful GPUs and high-performance CPUs, which are often costly. Beyond the upfront capital expenditure, there are continuous operational costs related to energy consumption, cooling systems, and software licenses. While large organizations with substantial budgets can manage these expenses, smaller businesses or those with limited resources may find it difficult to justify the investment, hindering the broader adoption of AI server technologies across various sectors. Introduction of the AI Server Market

The AI server market refers to the specialized computing infr...

As per Cognitive Market Research's latest published report, the Global AI in Mental Health market size was USD 910.6 Million in 2022 and it is forecasted to reach USD 11,371.0 Million by 2030. AI in Mental Health Industry's Compound Annual Growth Rate will be 37.2% from 2023 to 2030. Market Dynamics of AI in Mental Health Market

Growing adoption of AI to detect mental illness and symptoms:

AI plays a cog role in assessing and diagnosing mental illness symptoms. It is observed that detecting the sign of mental illness was challenging for clinicians and researchers. Mental disorders diagnosis often depends on self-reporting or direct observation of abnormal behaviors and actions. Direct observation is a costly procedure and time-consuming. Nonetheless, AI plays an important role to analyse and diagnose several mental health issues. The emergence of deep learning helps to monitor the probability of improving mental health conditions. With the help of deep learning, health practitioners can study and identify behaviors patterns, and potential warning signs which help physicians to make quick decisions. Stressful or traumatic events and generic mental disorders history are some of the major factors that may lead to mental illness. The data assembly and recognition allow clinical institutions and physicians to analyze the prediction for mental health issues in the patient. More specifically, AI helps to diagnose mental illness symptoms more accurately and quickly so that physicians can provide the right treatment with the right diagnosis. Thus, the emergence of AI and its several benefits in the healthcare industry has augmented market growth.

Restraining Factors of AI in Mental Health Market:

Data privacy and regulatory issues: The advent of new technologies such as Artificial Intelligence and IoT is reshaping the healthcare industry. Technology helps to keep records, monitor and track patient health. It also keeps a record of patient’s personal information and their treatment plans. However, the increasing prevalence of data theft and cyber-attacks is expected to hinder market growth to a certain extent. For instance, in 2020, around 58% of data theft increased in the healthcare industry as compared to the previous year. The governments of several countries have imposed different rules and regulations for adopting new technologies. For instance, in the U.S. companies must comply with HIPAA, GDPR, and other guidelines to launch their products and services. These factors can obstruct market growth.

Current Trends on AI in mental healthcare:

Several advantages associated with AI technology has imposing health specialist to adopt high-tech solutions to treat their patent more efficiently. On the other hand, to emphasize the usage of AI in the healthcare industry, giant players operating in this industry are focusing on product development and innovation followed by technological innovation. Conventional treatment for mental illness comprises medications and patient counseling. However, with the help of AI, health practitioners can monitor patients’ treatment and their medications. In addition. In the depression phase, patients loss their interest and mood in day-to-day activities where AI plays an important role in the treatment process. Thus, the rising need for AI solutions to treat mental illness is projected to propel the market growth over the forecast period, from 2023 to 2030. Introduction of AI in Mental Health

A mental disorder is a medical condition that disturbs a person’s thinking, fexeling, interest, mood, and ability for performing daily activities. Several factors such as trauma or a history of abuse, injury, genetic disorder (biological factors), physical illness, use of alcohol or drugs, generic disorder, and chemical imbalances in the brain are some of the major factors contributing to the development of mental illness. In addition, common signs of mental illness are changes in eating habits, mood swings, excessive worrying or fear, avoiding friends and social activities, and problems concentrating.

Gantt Chart Software Market Outlook

The global Gantt chart software market size was valued at approximately USD 300 million in 2023 and is expected to reach around USD 600 million by 2032, expanding at a compound annual growth rate (CAGR) of 7.5%. This growth is driven by the increasing demand for efficient project management tools that streamline planning, scheduling, and collaboration across various industries. The increasing complexity and scale of projects, coupled with the need for real-time progress tracking, are key growth factors propelling the adoption of Gantt chart software. With businesses striving for improved productivity and efficiency, the market is poised for significant growth over the forecast period.

The growing emphasis on the digitization of project management processes is a major factor fueling the growth of the Gantt chart software market. As organizations become more complex and geographically dispersed, the need for tools that facilitate seamless communication and coordination among team members and stakeholders has become crucial. Gantt chart software offers a visual representation of project timelines, resources, and dependencies, enabling project managers to efficiently allocate resources and foresee potential bottlenecks. This efficiency in project execution helps businesses meet deadlines and optimize resource utilization, thereby driving the demand for such software solutions globally.

Another significant growth driver for the Gantt chart software market is the increasing adoption of cloud-based solutions. Cloud technology offers multiple benefits, including accessibility, scalability, and reduced infrastructure costs, which make it attractive for enterprises of all sizes. Cloud-based Gantt chart software allows teams to collaborate in real-time, regardless of their geographical location, providing flexibility and fostering innovation. Moreover, the integration of advanced technologies such as artificial intelligence and machine learning into Gantt chart software aids in predictive analytics, offering insights into project timelines and resource management, further enhancing the software's value proposition.

The rising trend of remote work and the need for virtual collaboration tools also significantly contribute to market growth. As more organizations embrace remote work policies, the demand for software that supports distributed teams and facilitates seamless project management has surged. Gantt chart software caters to this need by providing a centralized platform for tracking project progress, assigning tasks, and ensuring accountability among team members. This shift towards remote work environments has accelerated the adoption rate of Gantt chart software, as it addresses the challenges posed by geographic dispersion and fosters efficient project execution.

Regionally, North America remains a dominant player in the Gantt chart software market, driven by the presence of a large number of technology companies and a well-established IT infrastructure. The region's focus on innovation and the adoption of advanced project management tools contribute significantly to market growth. Meanwhile, the Asia Pacific region is expected to experience the highest growth rate due to rapid industrialization, increased IT investments, and the burgeoning startup ecosystem in countries like China and India. As organizations in these regions seek to enhance their project management capabilities, the demand for Gantt chart software is anticipated to rise substantially.

Deployment Type Analysis

The deployment type segment of the Gantt chart software market is categorized into on-premises and cloud-based solutions. On-premises deployment involves the installation of software on local servers within the organization. This type of deployment offers increased control over data security and customization, making it a preferred choice for large enterprises with strict compliance requirements. However, it often comes with higher upfront costs and maintenance responsibilities, which can be a deterrent for smaller businesses. Despite these challenges, on-premises solutions remain relevant for industries requiring stringent data privacy and security measures, such as banking and healthcare.

Cloud-based deployment, on the other hand, has gained significant traction due to its flexibility, scalability, and cost-effectiveness. It offers businesses the advantage of accessing project management tools from anywhere, at any time, facilitating seamless collaboration among team members. Cloud-based solutions eliminate the need for extensive infrastructure inv

Worldwide spending on data center systems is projected to reach over, *** billion U.S. dollars in 2025, marking a significant ** percent increase from 2024. This growth reflects the ongoing digital transformation across industries and the increasing demand for advanced computing capabilities. The surge in data center investments is closely tied to the rapid expansion of artificial intelligence technologies, particularly with the wake of generative AI. AI chips fuel market growth The rise in data center spending aligns with the booming AI chip market, which is expected to reach ** billion U.S. dollars by 2025. Nvidia has emerged as a leader in this space, with its data center revenue skyrocketing due to the crucial role its GPUs play in training and running large language models like ChatGPT. The global GPU market, valued at ** billion U.S. dollars in 2024, is a key driver of this growth, powering advancements in machine learning and deep learning applications. Semiconductor industry adapts to AI demands The broader semiconductor industry is also evolving to meet the demands of AI technologies. With global semiconductor revenues surpassing *** billion U.S. dollars in 2023, the market is expected to approach *** billion U.S. dollars in 2024. AI chips are becoming increasingly prevalent in servers, data centers and storage infrastructures. This trend is reflected in the data centers and storage semiconductor market, which is projected to grow from ** billion U.S. dollars in 2023 to *** billion U.S. dollars by 2025, driven by the development of image sensors and edge AI processors.

The resolution test target market, encompassing a range of products used to assess the resolving power of imaging systems, is experiencing robust growth. While precise market sizing data for 2025 is unavailable, a logical estimation based on typical growth trajectories in the optics and imaging sectors suggests a market value of approximately $150 million in 2025. This market is driven by several key factors, including the increasing demand for high-resolution imaging in various applications such as medical imaging, industrial inspection, and semiconductor manufacturing. Technological advancements in imaging systems, leading to higher resolutions and increased need for accurate testing, further fuel market expansion. The rising adoption of automation and sophisticated testing procedures across diverse industries also contributes significantly to market growth.
Furthermore, emerging trends such as the growth of artificial intelligence (AI) and machine learning (ML) in image analysis are expected to further boost demand for precise resolution test targets. These technologies rely heavily on accurate image resolution for efficient analysis, necessitating high-quality test targets. However, the market faces some restraints, including high initial investment costs associated with advanced testing equipment and potential supply chain disruptions affecting the availability of specialized materials. Despite these challenges, the overall market outlook remains positive, driven by the continuous innovation in imaging technologies and the expanding application areas for high-resolution imaging. The market is segmented by type (e.g., resolution charts, USAF 1951 targets, Siemens stars), application (e.g., microscopy, machine vision, aerial photography), and end-user industry. Key players like Edmund Optics, Thorlabs, and others are constantly innovating and expanding their product lines to meet the growing demand.

The global artificial intelligence (AI) software market is forecast to grow rapidly in the coming years, reaching around *** billion U.S. dollars by 2025. The overall AI market includes a wide array of applications such as natural language processing, robotic process automation, and machine learning. What is artificial intelligence? Artificial intelligence refers to the capability of a machine that is able to replicate or simulate intelligent human behaviours such as analysing and making judgments and decisions. Originated in the computer sciences and a contested area in philosophy, artificial intelligence has evolved and developed rapidly in the past decades and AI use cases can now be found in all corners of our society: the digital voice assistants that reside in our smartphones or smart speakers, customer support chatbots, as well as industrial robots. Investments in AI Many of the biggest names in the tech industry have invested heavily into both AI acquisitions and AI related research and development. When it comes to AI patent applications by company, Microsoft, IBM, Google, and Samsung have each submitted thousands of such applications, and funding for AI related start-ups are raking in dozens of billions of dollars each year.

Online Diagram Editor Market Outlook

The global online diagram editor market is experiencing substantial growth, with the market size reaching approximately USD 850 million in 2023 and projected to escalate to USD 1.49 billion by 2032, reflecting a robust CAGR of 6.5% during the forecast period. This growth can be attributed to the increasing demand for collaborative tools in the professional environment, driven by the need for efficient visualization of data and processes across various sectors. The adoption of advanced technologies, such as artificial intelligence and machine learning, is further propelling the development of more sophisticated diagramming solutions, thereby enhancing their utility in complex organizational settings.

One of the primary growth factors for the online diagram editor market is the widespread acceptance of remote working models. As organizations worldwide continue to embrace remote and hybrid work environments, the necessity for interactive, real-time collaboration tools has surged. Online diagram editors offer an ideal solution, allowing teams to create, share, and edit diagrams collaboratively, irrespective of geographical constraints. This has become crucial for maintaining productivity and ensuring seamless communication in distributed teams, thus significantly contributing to the market's expansion.

Another significant driver of market growth is the increasing integration of diagram editors into broader software ecosystems. Many enterprises are opting for solutions that can seamlessly integrate with project management tools, customer relationship management (CRM) systems, and other enterprise software to streamline workflows and enhance operational efficiency. This interoperability not only simplifies the user experience but also optimizes the value derived from existing technologies. Consequently, the demand for versatile, integrated diagramming tools is rising, further bolstering market growth.

The educational sector is also playing a vital role in driving the demand for online diagram editors. As educational institutions continue to incorporate digital tools into their teaching methodologies, the need for intuitive, accessible diagramming applications has grown. These tools are being used to facilitate interactive learning experiences, enabling students and educators to visualize complex concepts and foster a deeper understanding of the subject matter. This trend is expected to continue, with educational institutions increasingly adopting technology to enhance learning outcomes.

In the realm of organizational management, Org Chart Software has emerged as a pivotal tool for visualizing company hierarchies and facilitating efficient communication. These software solutions allow businesses to create detailed organizational charts that clearly depict the structure of a company, including departments, teams, and individual roles. By providing a visual representation of the workforce, Org Chart Software helps managers and HR professionals to streamline onboarding processes, identify talent gaps, and plan for future growth. As organizations continue to evolve, the ability to quickly adapt and restructure is crucial, making Org Chart Software an indispensable asset in strategic planning and human resource management.

Regionally, North America is leading the market, owing to the high adoption rate of digital tools and technologies across various industries. The presence of key market players and advanced technological infrastructure further supports this dominance. The Asia Pacific region is anticipated to witness significant growth during the forecast period, driven by the rapid digital transformation initiatives across countries like China and India. The increasing penetration of internet services and the growing emphasis on enhancing business productivity are expected to drive the demand for online diagram editors in this region.

Component Analysis

The component segment of the online diagram editor market is primarily bifurcated into software and services. The software component constitutes the largest share, owing to the wide array of functionalities and capabilities that modern diagramming software provides. These software solutions are increasingly being designed with user-friendly interfaces and enhanced features such as drag-and-drop capabilities, customizable templates, and real-time collaboration options. The growing demand for these advanced features continues to drive the expansion of the softwa

Snapshot img

Artificial Intelligence (AI) In Games Market Size 2025-2029

The artificial intelligence (ai) in games market size is forecast to increase by USD 27.47 billion, at a CAGR of 42.3% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Augmented Reality (AR) and Virtual Reality (VR) games. These immersive technologies are revolutionizing the gaming industry by providing more realistic and interactive experiences, thereby fueling the demand for advanced AI capabilities. AI algorithms enable more intelligent and responsive non-player characters, dynamic game environments, and personalized user experiences. However, the market faces challenges, primarily due to the latency issues in between games. As AI-driven games become more complex and data-intensive, ensuring seamless and low-latency interactions between players and the game environment becomes crucial. Addressing these latency issues will require continuous advancements in AI technologies, network infrastructure, and cloud gaming solutions.
Companies seeking to capitalize on the market opportunities must focus on developing AI solutions that deliver high-performance, low-latency experiences while ensuring data security and privacy. Effective collaboration between game developers, technology providers, and network infrastructure companies will be essential to address these challenges and drive the growth of the AI in Games market.

What will be the Size of the Artificial Intelligence (AI) In Games Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market continues to evolve, integrating advanced technologies such as e-sports integration, player behavior analysis, game analytics, game engine optimization, computer vision, UI, QA, game balance, game AI, character AI, social features, gameplay mechanics, cloud gaming, game physics engines, in-app purchases, game localization, multiplayer networking, performance benchmarking, streaming integration, pathfinding algorithms, procedural generation, UX, subscription models, competitive gaming, machine learning models, neural networks, advertising integration, and audio design. These technologies are not static entities but rather dynamic components that unfold and intertwine, shaping the market's intricate landscape. E-sports integration and player behavior analysis enable game developers to create more engaging experiences, while game analytics offers valuable insights into player preferences and trends.

Game engine optimization and computer vision enhance game performance and visual quality, respectively. UI and QA ensure seamless user experiences and bug-free gameplay, respectively. Game balance and character AI add depth and complexity to game mechanics. Machine learning models and neural networks facilitate intelligent decision-making, while social features and gameplay mechanics foster community engagement. Cloud gaming and streaming integration expand accessibility, and game physics engines and in-app purchases generate revenue. Game localization and multiplayer networking cater to diverse player bases, and performance benchmarking ensures optimal game performance. The ongoing interplay of these technologies shapes the market's dynamics, with new applications and innovations continually emerging.

How is this Artificial Intelligence (AI) In Games Industry segmented?

The artificial intelligence (ai) in games industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  AI enabled platforms
  AI enabled games


Technology

  Machine learning
  Natural language processing
  Computer vision
  Robotics


Game

  Action
  Adventure
  Casual
  Racing
  Simulation
  Sports
  Strategy


Application

  Gameplay Optimization
  Character Behavior Generation
  Level Design
  Player Engagement


End-User

  Developers
  Publishers
  Players


Platform Type

  Console
  PC
  Mobile
  Cloud


Geography

  North America

    US
    Mexico


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    Australia
    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The ai enabled platforms segment is estimated to witness significant growth during the forecast period.

In the dynamic gaming industry, Artificial Intelligence (AI) is revolutionizing game development and player experience. AI technologies, including deep learning, reinforcement learning, and machine learning models, are integrated into various aspects of game creation. These tools enhance

Graph Analytics Market Outlook

According to our latest research, the global graph analytics market size reached USD 2.9 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to grow at a CAGR of 31.2% during the forecast period, reaching USD 26.5 billion by 2033. This accelerated growth is driven by the increasing need for advanced analytics to uncover complex relationships within large and interconnected datasets, as organizations seek to enhance decision-making, detect fraud, and improve customer experiences with actionable insights.

A primary growth factor in the graph analytics market is the exponential rise in data complexity and volume generated by digital transformation initiatives. Modern enterprises are leveraging IoT devices, social media, and transactional systems that produce vast amounts of structured and unstructured data. Traditional analytics tools often fall short in identifying intricate relationships and patterns within these datasets. Graph analytics, with its ability to map and analyze complex networks, offers a powerful solution for extracting valuable insights from interconnected data points. As organizations recognize the limitations of conventional analytics and the advantages of graph-based approaches, demand for graph analytics platforms continues to surge across sectors such as BFSI, healthcare, and retail.

Another significant driver for the graph analytics market is the increasing adoption of AI and machine learning technologies. Graph analytics enhances the performance of machine learning models by providing contextual information and relationship mapping, which are crucial for applications like fraud detection, recommendation engines, and risk management. The integration of graph analytics with AI workflows enables more accurate predictions and real-time decision-making, especially in industries where timely insights are critical. This synergy between AI and graph analytics is encouraging enterprises to invest in advanced analytics solutions, further fueling market expansion.

The proliferation of cloud computing is also playing a pivotal role in the growth of the graph analytics market. Cloud-based deployment offers scalability, flexibility, and cost-effectiveness, making advanced analytics accessible to organizations of all sizes. As businesses increasingly migrate their data and analytics workloads to the cloud, the adoption of cloud-native graph analytics solutions is accelerating. These solutions facilitate seamless integration with existing IT infrastructure and support real-time analytics at scale, enabling organizations to respond swiftly to evolving business needs and market dynamics.

Regionally, North America continues to lead the graph analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of major technology players, early adoption of advanced analytics, and high investment in digital transformation initiatives are key factors driving market growth in these regions. Meanwhile, the Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, expanding e-commerce, and increasing focus on data-driven decision-making in emerging economies. As organizations worldwide recognize the strategic value of graph analytics, the market is poised for significant expansion across all major regions during the forecast period.

Component Analysis

The graph analytics market by component is segmented into software and services. The software segment currently dominates, accounting for the majority of market revenue in 2024. This dominance is attributed to the growing adoption of advanced graph analytics platforms that enable organizations to visualize, query, and analyze complex relationships within their data. These software solutions are becoming increasingly sophisticated, offering features such as real-time analytics, integration with AI and machine learning, and support for large-scale datasets. As

AI Occlusion Pressure Map Scanner Market Outlook

According to our latest research, the global AI Occlusion Pressure Map Scanner market size reached USD 1.14 billion in 2024, with a robust CAGR of 13.7% anticipated during the forecast period from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a valuation of USD 3.68 billion, fueled by rising demand for advanced medical diagnostics and rehabilitation solutions. This growth is primarily driven by technological advancements in artificial intelligence, the increasing prevalence of chronic diseases, and a growing focus on patient-centric healthcare.

A key growth factor for the AI Occlusion Pressure Map Scanner market is the rapid integration of AI algorithms into pressure mapping systems, which has significantly enhanced the accuracy and efficiency of detecting occlusions and pressure irregularities. These innovations have enabled healthcare professionals to diagnose conditions such as diabetic foot ulcers, vascular diseases, and musculoskeletal disorders with greater precision and speed. The continuous evolution of AI, including deep learning and machine learning technologies, has further augmented the capabilities of these scanners, allowing for real-time data analysis and predictive analytics. This has led to improved patient outcomes and reduced healthcare costs, making AI-powered pressure map scanners increasingly indispensable in modern medical practice.

Another significant driver contributing to market growth is the rising awareness and adoption of preventive healthcare measures. As populations age and the incidence of chronic diseases increases globally, there is a heightened emphasis on early detection and intervention. AI Occlusion Pressure Map Scanners play a pivotal role in this paradigm shift, offering non-invasive, accurate, and rapid assessments that facilitate timely clinical decisions. The integration of wearable and portable devices has further expanded the accessibility of these technologies beyond hospital settings, enabling home-based monitoring and personalized rehabilitation programs. This democratization of advanced diagnostic tools is expected to propel market expansion across both developed and emerging economies.

Moreover, the expanding application scope of AI Occlusion Pressure Map Scanners in fields such as sports science and rehabilitation is opening new avenues for market growth. Professional athletes and sports organizations are increasingly utilizing these devices to monitor pressure distribution, optimize performance, and prevent injuries. Similarly, rehabilitation centers are leveraging AI-based pressure mapping to design customized therapy plans for patients recovering from surgeries or injuries. The synergy of AI with sensor technologies and data analytics is fostering the development of next-generation solutions that cater to a broad spectrum of end users, including hospitals, clinics, research institutes, and fitness centers. This diversification of applications is expected to sustain the market’s momentum throughout the forecast period.

Regionally, North America is currently the dominant market for AI Occlusion Pressure Map Scanners, accounting for over 38% of global revenue in 2024. The region’s leadership is underpinned by advanced healthcare infrastructure, significant investments in AI research, and a high adoption rate of innovative medical technologies. Europe follows closely, driven by supportive regulatory frameworks and growing healthcare expenditure. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by increasing healthcare modernization, expanding patient populations, and rising awareness of preventive care. These trends highlight a dynamic regional landscape with substantial opportunities for market players.

Product Type Analysis

The Product Type segment of the AI Occlusion Pressure Map Scanner market encompasses portable scanners, fixed scanners, wearable devices, and others, each offeri

Abstract

The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location.

How can I see the data?

A web server to look through predictions is available through idtrees.org

Dataset Organization

The shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: https://data.neonscience.org/data-products/explore?search=camera. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see https://www.neonscience.org/field-sites/field-sites-map.

The predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available:

Height: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001)

Area: The crown area in m² of the rectangular bounding box.

Label: All data in this release are "Tree".

Score: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence)

How were predictions made?

The DeepForest algorithm is available as a python package: https://deepforest.readthedocs.io/. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed.

How were predictions validated?

Please see

Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics, 56, 101061.

Weinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv.

Weinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing 11.11 (2019): 1309.

Were any sites removed?

Several sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request.

# Contact

We welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(GTLS) Chart Industries: Poised for Growth in a Cooling World

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the 'raw' and 'ready-to-train'-versions with corresponding test datasets. The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘ready-to-train’-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the Test data for the Ready-To-Train version. Reference data is not included. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the 'ready-to-train' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg

Version 2 includes the reference sea ice charts (previously absent) as the AutoICE Challenge has been finalised. The ice charts are both included in numerical format in the netCDF files and in quicklook images containing the SIC, SOD and FLOE for each scene in png format.

This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065