The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 1m spatial resolution.The DTM (Digital Terrain Model) is produced from the last or only laser pulse returned to the sensor. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface. Produced by the Environment Agency in 2022, the DTM is derived from a combination of our Time Stamped archive and National LIDAR Programme surveys, which have been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged. The 2022 LIDAR Composite contains surveys undertaken between 6th June 2000 and 2nd April 2022. Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the LIDAR composite.DEFRA Data Services Platform Metadata URLDefra Network WMS server provided by the Environment Agency

PLEASE NOTE: This record has been retired. It has been superseded by: https://environment.data.gov.uk/dataset/b5aaa28d-6eb9-460e-8d6f-43caa71fbe0e

This dataset is not suitable for identifying whether an individual property will flood. GIS layer showing the extent of flooding from surface water that could result from a flood with a 1% chance of happening in any given year. The flood depth is grouped into 6 bands. This dataset is one output of our Risk of Flooding from Surface Water (RoFSW) mapping, previously known as the updated Flood Map for Surface Water (uFMfSW). It is one of a group of datasets previously available as the uFMfSW Complex Package. Further information on using these datasets can be found at the Resource Locator link below. Information Warnings: Risk of Flooding from Surface Water is not to be used at property level. If the Content is displayed in map form to others we recommend it should not be used with basemapping more detailed than 1:10,000 as the data is open to misinterpretation if used as a more detailed scale. Because of the way they have been produced and the fact that they are indicative, the maps are not appropriate to act as the sole evidence for any specific planning or regulatory decision or assessment of risk in relation to flooding at any scale without further supporting studies or evidence.

This dataset is no longer available on the Data Services Platform. New version of this dataset, published in June 2020 is available here: https://environment.data.gov.uk/dataset/73c25700-052a-4d3e-87cf-71326fe2d73a and on Survey Data Catalogue.

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering >85% of England at 2m spatial resolution. Produced by the Environment Agency in 2019, this dataset is derived from a combination of our Time Stamped archive and National LIDAR Programme, which has been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged.

The 2019 LIDAR Composite contains surveys undertaken between 12th March 1998 and 1st September 2019. Please refer to the survey index files which shows, for any location, what Time Stamped survey or National LIDAR Programme block went into the production of the LIDAR composite for a specific location.

The DTM (Digital Terrain Model) is produced from the last return LIDAR signal. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface. Available to download as GeoTiff files in 5km grids, data is presented in metres, referenced to Ordinance Survey Newlyn, using the OSTN’15 transformation. All LIDAR data has a vertical accuracy of +/-15cm RMSE.

Light Detection and Ranging (LIDAR) is an airborne mapping technique, which uses a laser to measure the distance between the aircraft and the ground. Up to 500,000 measurements per second are made of the ground, allowing highly detailed terrain models to be generated at spatial resolutions of between 25cm and 2 metres. The Environment Agency’s open data LIDAR archives includes the Point Cloud data, and derived raster surface models of survey specific areas dating back to 1998 and composites of the best data available in any location.

This metadata record is for Approval for Access product AfA458.

Attribution statement: (c) Environment Agency copyright and/or database right 2020. All rights reserved. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

Autonomous Data Platform Market size was valued at USD 1.95 Billion in 2024 and is projected to reach USD 9.63 Billion by 2032, growing at a CAGR of 22.10% from 2026 to 2032.

Global Autonomous Data Platform Market Drivers

Increasing Volume and Complexity of Data: The exponential increase in data volume and complexity is one of the main factors propelling the market for autonomous data platforms. Traditional data management systems find it difficult to handle the data explosion caused by the spread of digital devices, Internet of Things sensors, social media, and other data-generating sources. Large, complex datasets can be handled with extreme efficiency by autonomous data platforms because they use artificial intelligence (AI) and machine learning (ML) to automate data management processes including data integration, cleansing, and transformation. These platforms are being used by organizations more and more to process and analyze data in real-time, giving them the ability to gain actionable insights and stay ahead of the competition.

Need for Real-Time Analytical Data: The market for autonomous data platforms is also being driven by the increased need for real-time analytics. Making judgments based on data rapidly is essential in the fast-paced corporate world of today. Organizations may process and analyze data as it is generated with the help of autonomous data platforms, which offer real-time insights that can be utilized to improve customer experiences, streamline operations, and spur corporate expansion. Real-time analytics is especially important for sectors like banking, healthcare, retail, and telecommunications since it allows these businesses to quickly identify abnormalities, track trends, and make well-informed decisions. One of the main factors influencing autonomous data platforms' adoption across a variety of industries is their capacity to facilitate real-time data processing and analytics.

Developments in Machine Learning and Artificial Intelligence: Technological developments in AI and ML are essential to the market expansion for autonomous data platforms. Autonomous data platforms rely on these technologies to automate labor-intensive data management processes that were previously labor-intensive and required human interaction. Over time, as AI and ML algorithms continue to learn from data, the platform's accuracy and efficiency will increase. Because of this, there is less need for manual intervention, which lowers operating expenses and lowers the possibility of human error. Predictive analytics is made possible by the integration of AI and ML into data systems, which enables businesses to foresee patterns, project results, and take proactive measures in decision-making. In the upcoming years, the adoption of autonomous data platforms is anticipated to increase due to the continued development of these technologies, which will further improve their capabilities.

The Automotive Data Service Platform market is experiencing robust growth, driven by the increasing adoption of connected vehicles, the proliferation of data-driven services, and the rising demand for enhanced vehicle safety and efficiency. The market's expansion is fueled by several key factors, including the development of advanced driver-assistance systems (ADAS), the need for real-time vehicle diagnostics, and the emergence of new business models centered around data monetization. Key players like Solifi, Otonomo, and Wejo are actively shaping this landscape, investing in innovative solutions to collect, process, and analyze automotive data. Furthermore, the increasing integration of artificial intelligence (AI) and machine learning (ML) algorithms within these platforms promises to unlock even greater opportunities for predictive maintenance, personalized driving experiences, and improved fleet management. The market is segmented by service type (e.g., diagnostics, telematics, infotainment), vehicle type (passenger cars, commercial vehicles), and region, each exhibiting unique growth trajectories. While data privacy and security concerns pose challenges, the overall market outlook remains positive, anticipating significant expansion over the next decade. The forecast period from 2025 to 2033 suggests substantial growth potential for the Automotive Data Service Platform market. A conservative estimate, assuming a moderate CAGR of 15% (a figure adjusted for the various factors driving the market), would see a substantial increase in market value. This growth is underpinned by ongoing technological advancements, regulatory changes favoring data-driven services, and the increasing sophistication of connected car ecosystems. The competitive landscape is dynamic, with established players and emerging startups vying for market share. Strategic partnerships, acquisitions, and technological innovations will play a crucial role in shaping the market's future. The market's geographic segmentation reveals varying levels of adoption across different regions, influenced by factors such as infrastructure development, consumer adoption rates, and government regulations. North America and Europe are expected to maintain a strong market presence, but Asia-Pacific is poised for significant growth in the coming years.

This record is for Approval for Access product AfA345.

The Environment Agency's (EA) Spatial Flood defences layer is the only comprehensive and up-to-date dataset in England that shows flood defences currently owned, managed or inspected by the EA.

Flood defences can be structures, buildings or parts of buildings. Typically these are earth banks, stone and concrete walls, or sheet-piling that is used to prevent or control the extent of flooding.

A defence is any asset that provides flood defence or coastal protection functions. This includes both man-made and natural defences. Natural defences may include man-made elements to make them more effective or protect them from erosion. Normally a number of assets will be used together to manage the risk in a particular area, working in combination within a risk management system.

PLEASE NOTE: This data is updated daily. This is a large dataset and depending on the chosen download format, it may take 7-8 minutes to download the full national dataset.

PLEASE NOTE: This record has been Retired and is no longer updated. This data service has been superseded by other flood forecasting data services which are available from the DEFRA Data Services Platform: https://environment.data.gov.uk/ The flood risk forecast is produced by the Flood Forecasting Centre (FFC) for publication on the Environment Agency’s website on a daily basis www.environment-agency.gov.uk/homeandleisure/floods/125305.aspx). It is issued more frequently when serious flooding is forecast. It provides the indication of the potential for flooding for three days: the day on which it is issued and the subsequent two days ahead. The forecast highlights flood risk on a county by county basis and includes a short commentary on the situation. It covers flooding from rivers, the sea, surface water and groundwater for England and Wales. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

다운로드 InventoryOfClosedMiningWasteFacilities-SHP.zip download on Defra Data Services Platform ZIP InventoryOfClosedMiningWasteFacilities-MID_MIF.zip 다운로드 InventoryOfClosedMiningWasteFacilities-MID_MIF.zip download on Defra Data Services Platform ZIP InventoryOfClosedMiningWasteFacilities-KML.zip 다운로드 InventoryOfClosedMiningWasteFacilities-KML.zip download on Defra Data Services Platform ZIP InventoryOfClosedMiningWasteFacilities-GeoJSON.zip 다운로드 InventoryOfClosedMiningWasteFacilities-GeoJSON.zip download on Defra Data Services Platform ZIP InventoryOfClosedMiningWasteFacilities-GML.zip 다운로드 InventoryOfClosedMiningWasteFacilities-GML.zip download on Defra Data Services Platform ZIP InventoryOfClosedMiningWasteFacilities-FGDB.zip 다운로드

Big Data Platform And Tools Market Outlook

The global Big Data Platform and Tools market size was valued at approximately $72 billion in 2023 and is projected to reach around $210 billion by 2032, growing at a CAGR of 12.5% during the forecast period. The market growth is primarily driven by the increasing adoption of data-driven decision-making processes across various industries. Organizations are leveraging big data analytics to gain insights, improve operational efficiency, and create new business models, thereby fostering market expansion.

One of the major growth factors for the Big Data Platform and Tools market is the exponential increase in data generation across different sectors. With the proliferation of IoT devices, social media platforms, and various digital channels, the volume, velocity, and variety of data have surged significantly. Companies are increasingly investing in big data platforms to manage and analyze this data, which helps in making informed decisions and gaining a competitive edge. Additionally, advancements in artificial intelligence and machine learning are enhancing the capabilities of big data tools, further driving market growth.

Another crucial factor contributing to the market growth is the rising need for compliance and regulatory requirements. Industries such as BFSI, healthcare, and government are subject to stringent data regulations and privacy laws. Big data platforms provide the necessary tools to ensure data security, governance, and compliance, thereby minimizing the risk of data breaches and regulatory penalties. This has led to a higher adoption rate of big data solutions in these sectors, significantly contributing to the market's expansion.

The increasing focus on enhancing customer experience is also propelling the growth of the Big Data Platform and Tools market. Businesses are utilizing big data analytics to understand customer behavior, preferences, and trends. This information helps in personalizing marketing strategies, improving customer service, and developing new products tailored to customer needs. The ability to provide a superior customer experience is becoming a critical differentiator in highly competitive markets, thereby driving the demand for big data solutions.

Regionally, North America holds the largest share of the Big Data Platform and Tools market, driven by the presence of major technology companies, high adoption of advanced technologies, and substantial investments in R&D. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors such as rapid digitalization, the proliferation of smart devices, and the increasing need for data-driven decision-making in emerging economies like China and India are fueling the market growth in this region. Additionally, government initiatives promoting digital transformation are further boosting the adoption of big data solutions in the Asia Pacific.

Component Analysis

When analyzing the Big Data Platform and Tools market by component, it is evident that both software and services play a crucial role in the market dynamics. The software segment encompasses various big data tools and platforms designed for data processing, analytics, and visualization. These tools are essential for extracting meaningful insights from vast amounts of data, enabling organizations to make data-driven decisions. Key software solutions include Hadoop, Spark, NoSQL databases, and data integration tools. The continuous advancements in these software solutions, such as enhanced processing speed and real-time analytics capabilities, are driving their adoption across various industries.

The services segment, which includes consulting, implementation, and support services, is equally vital for the successful deployment and utilization of big data platforms. Consulting services help organizations identify the right big data strategy and select appropriate tools based on their specific needs. Implementation services ensure the seamless integration of big data solutions into existing IT infrastructure, while support services provide ongoing maintenance and troubleshooting. The growing complexity of big data environments necessitates specialized expertise, making the services segment a critical component of the market.

Furthermore, the rising trend of outsourcing big data services to third-party providers is contributing to the growth of the services segment. Many organizations prefer to rely on external experts for managing their big data initiatives, allowing them to focus on core bus

This dataset is not suitable for identifying whether an individual property will flood.This dataset is one output of our Risk of Flooding from Surface Water (RoFSW) mapping, previously known as the updated Flood Map for Surface Water (uFMfSW). It is one of a group of datasets previously available as the uFMfSW Complex Package. Further information on using these datasets can be found through the DEFRA Data Services Platform link below. Information Warnings: Risk of Flooding from Surface Water is not to be used at property level. If the Content is displayed in map form to others we recommend it should not be used with basemapping more detailed than 1:10,000 as the data is open to misinterpretation if used as a more detailed scale. Because of the way they have been produced and the fact that they are indicative, the maps are not appropriate to act as the sole evidence for any specific planning or regulatory decision or assessment of risk in relation to flooding at any scale without further supporting studies or evidence. Additional Links:DEFRA Data Services Platform- containing download linksRisk of flooding from Surface Water Extent 3.3% WMS

This dataset contains information on prosecutions by the Environment Agency under its regulatory powers. Cases completed from January 2000 that resulted in a conviction are included. Records related to prosecution of individuals have been anonymised in accordance with government procedures. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Data and Resources Environment_Agency_Prosecutions.xlsxXLSX Environment_Agency_Prosecutions.xlsx download on Defra Data Services Platform

The Content Services Platforms Market report segments the industry into By Component (Solutions/Software, Services), By Deployment Type (On-Premises, Cloud), By Organization Size (Small and Medium-Sized Enterprises, Large Enterprises), By End-User Industry Vertical (BFSI, Government and Public Sector, and more), and By Geography (North America, Europe, Asia-Pacific, and more).

Drinking Water Groundwater Safeguard Zones (SgZs) are established around public water supplies where additional pollution control measures are needed. The Water Framework Directive requires that Drinking Water Protected Areas are identified (WFD Article 7.1) and that they are given the necessary protection (WFD Article 7.3) with the aim of avoiding deterioration in their quality in order to reduce the level of purification treatment required in the production of drinking water.

The geometry of Groundwater Safeguard Zones are based on Groundwater Source Protection Zones (SPZs)*, usually the SPZ 2, and use additional assessment to identify areas, which may or may not coincide with the SPZ, where additional measures are required to ensure that abstraction waters meet Article 7.3 of the WFD. SgZs can be large or small depending on the pollution problem and may not cover the whole of a catchment or sub-catchment.

*AfA029 Source Protection Zones [Merged] is available.

Global Public Cloud Platform as a Service (PaaS) Market size worth reach USD 9.12 billion by 2021, growing at a CAGR of 30% during 2016 and 2021.

Market Size and Growth Potential: The global library services platform market is estimated to be valued at over USD X million in 2025, with a CAGR of around XX% during the forecast period from 2025 to 2033. The increasing adoption of digital technologies in libraries, rising demand for personalized library experiences, and growing government initiatives to promote literacy are key drivers of this market growth. Segmentation and Competitive Landscape: By application, the market is segmented into school libraries, public libraries, and others. Cloud-based and local deployment are the two main types of library services platforms. Key players in the market include Ex Libris, SirsiDynix, Innovative Interfaces, Capita, Infor, PTFS, OCLC, EOS, Auto Graphics, LAT, TLC, PrimaSoft, Equinox Software, Libsys, and Book Systems. North America and Europe are the dominant regional markets, with Asia Pacific expected to witness significant growth due to the expansion of library infrastructure and digital literacy initiatives.

Content Services Platforms Market Outlook

The global content services platforms market has experienced significant growth, with the market size reaching approximately $45 billion in 2023, and it is projected to expand further at a robust CAGR of 15% from 2024 to 2032, anticipating a market size of nearly $121 billion by the end of 2032. This growth is fueled by the increasing digital transformation initiatives across industries, the rising need for efficient content management solutions, and the growing demand for enhanced collaboration tools among enterprises. As organizations continue to digitize their operations and workflows, the adoption of content services platforms is anticipated to accelerate, further driving market expansion.

One of the primary growth factors for the content services platforms market is the escalating volume of digital content being generated across various industries. The proliferation of digital communication channels, social media platforms, and mobile applications has resulted in an exponential increase in data and content production. This surge in content creation necessitates robust platforms that can efficiently manage, store, and disseminate information, thereby driving the demand for content services platforms. Furthermore, the shift towards remote work environments and hybrid work models has highlighted the need for seamless access to content and collaboration tools, further boosting market growth. Organizations are seeking solutions that offer secure access to content from any location, ensuring business continuity and productivity.

Another significant growth driver is the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into content services platforms. AI and ML capabilities enhance the functionality of these platforms by automating content categorization, improving search capabilities, and providing insights through data analytics. This technological evolution enables organizations to extract more value from their content, optimize workflows, and make data-driven decisions. As businesses increasingly recognize the benefits of AI-powered content services platforms, their adoption is expected to surge, propelling market growth over the forecast period. Additionally, the rising focus on compliance and data security regulations is compelling organizations to adopt content services platforms that offer robust security features and ensure data governance.

The integration capabilities of content services platforms with existing enterprise systems also play a crucial role in their market expansion. Businesses are increasingly prioritizing solutions that can seamlessly integrate with their existing IT infrastructure, including CRM, ERP, and other business applications. This integration allows for better data flow, improved operational efficiency, and enhanced user experience, thereby driving the adoption of content services platforms. Moreover, the customization and scalability offered by these platforms make them suitable for organizations of all sizes and across different industry verticals, further contributing to market growth. As companies continue to prioritize digital transformation and improve their content management strategies, the demand for content services platforms is expected to witness sustained growth.

Component Analysis

The content services platforms market is bifurcated into two primary components: solutions and services. Solutions encompass the software platforms themselves, which include a range of functionalities from content management and storage to search and retrieval capabilities. These solutions are integral to organizations as they provide the core infrastructure needed to manage the lifecycle of digital content effectively. The demand for comprehensive solutions is increasing as businesses look for platforms that can handle diverse content types, including documents, images, videos, and more. This has led to the development of feature-rich solutions that offer advanced analytics, collaboration tools, and workflow automation, enhancing overall organizational efficiency.

Services, on the other hand, include consulting, implementation, integration, and support services that help organizations deploy and optimize content services platforms. The services segment is crucial as it ensures that the platforms are tailored to meet the specific needs of each organization. Consulting services guide businesses in selecting the right platform based on their requirements, while implementation services assist in smooth deployment and integration with existing systems. The increasing complexity of content managem

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Market Overview: The global Smart Tourism Big Data Service Platform market is projected to reach a valuation of $9.1 billion by 2033, exhibiting a robust CAGR of 15.4% over the forecast period 2025-2033. This growth is driven by the increasing adoption of data analytics in the tourism industry, growing demand for personalized travel experiences, and the proliferation of mobile devices and IoT sensors. The market is segmented based on type (cloud-based, on-premises) and application (individual, group, others), with the cloud-based segment dominating due to its cost-effectiveness and flexibility. North America currently holds the largest market share, followed by Europe and Asia Pacific. Key Trends and Challenges: Prominent trends shaping the market include the rise of artificial intelligence and machine learning, enabling personalized recommendations and predictive analytics. Additionally, the integration of blockchain technology is enhancing data security and transparency. However, challenges such as data privacy concerns, fragmented data sources, and the need for skilled professionals may hinder market growth. Despite these challenges, advancements in data analytics and the growing importance of data-driven decision-making in the tourism sector are expected to drive sustained market expansion in the coming years.

This dataset is no longer available on the Data Services Platform. New version of this dataset, published in June 2020 is available here: https://environment.data.gov.uk/dataset/668881ad-4f8f-42bd-b835-89acf0269496 and on Survey Data Catalogue.

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering >80% of England at 1m spatial resolution. Produced by the Environment Agency in 2019, this dataset is derived from a combination of our Time Stamped archive and National LIDAR Programme, which has been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged.

The 2019 LIDAR Composite contains surveys undertaken between 6th June 2000 and 1st September 2019. Please refer to the survey index files which shows, for any location, what Time Stamped survey or National LIDAR Programme block went into the production of the LIDAR composite for a specific location.

The DTM (Digital Terrain Model) is produced from the last return LIDAR signal. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface. Available to download as GeoTiff files in 5km grids, data is presented in metres, referenced to Ordinance Survey Newlyn, using the OSTN’15 transformation. All LIDAR data has a vertical accuracy of +/-15cm RMSE.

Light Detection and Ranging (LIDAR) is an airborne mapping technique, which uses a laser to measure the distance between the aircraft and the ground. Up to 500,000 measurements per second are made of the ground, allowing highly detailed terrain models to be generated at spatial resolutions of between 25cm and 2 metres. The Environment Agency’s open data LIDAR archives includes the Point Cloud data, and derived raster surface models of survey specific areas dating back to 1998 and composites of the best data available in any location.

This metadata record is for Approval for Access product AfA458.

Attribution statement: (c) Environment Agency copyright and/or database right 2020. All rights reserved. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

The statistic shows a revenue forecast of the competitive public cloud data services market worldwide, from 2013 to 2019. In 2016, cloud data services for the public Platform as a Service (PaaS) market are forecast to generate *** billion U.S. dollars globally.