PLEASE NOTE: This dataset has been retired. It has been superseded by https://environment.data.gov.uk/dataset/04532375-a198-476e-985e-0579a0a11b47. Links to this data will be removed after April 2025. We encourage users to download this Flood Zones dataset if you would like to retain a comparison ability beyond this date.

The Flood Map for Planning (Rivers and Sea) includes several layers of information. This dataset covers Flood Zone 2 and should not be used without Flood Zone 3. It is our best estimate of the areas of land at risk of flooding, when the presence of flood defences are ignored and covers land between Zone 3 and the extent of the flooding from rivers or the sea with a 1 in 1000 (0.1%) chance of flooding each year. This dataset also includes those areas defined in Flood Zone 3.

This dataset is designed to support flood risk assessments in line with Planning Practice Guidance ; and raise awareness of the likelihood of flooding to encourage people living and working in areas prone to flooding to find out more and take appropriate action.

The information provided is largely based on modelled data and is therefore indicative rather than specific. Locations may also be at risk from other sources of flooding, such as high groundwater levels, overland run off from heavy rain, or failure of infrastructure such as sewers and storm drains.

The information indicates the flood risk to areas of land and is not sufficiently detailed to show whether an individual property is at risk of flooding, therefore properties may not always face the same chance of flooding as the areas that surround them. This is because we do not hold details about properties and their floor levels. Information on flood depth, speed or volume of flow is not included.

The Environment Agency National LIDAR Programme provides accurate elevation data at 1m spatial resolution for all of England.

In 2017 we divided the country into 302 survey blocks covering all of England which were subsequently captured during the winter months (approximately November to April each year) between January 2017 and February 2023. These are known as our 'Phase 1' national lidar programme surveys.

Subsequently we have undertaken repeat surveys of specific blocks based on the on-going requirements for upto date elevation data. Each repeat survey block is given a new incrementing phase number, for example the second time we capture a block this is that blocks 'phase 2' whilst the 3rd time will be 'phase 3'. There is not curretly a plan to capture all the origianl phase 1 survey blocks over a rolling programme with repeat surveys be based on the requirements for upto date elevation data for an area.

All data is published through the DEFRA Data Services survey portal on a quartely on-going bases and a number of different products area available for each survey block. All products are available in 5km tiles aligned to the ordnance survey grid. The tiles are named by the unique survey id, OS grid reference and the first and last survey date of the survey id (P_XXXXX_OSOSOS_SDFLOWN_EDFLOWN.*). The surface models are available in GeoTiff raster format whilst the point cloud is available in *.laz. An index catalogue is also available with provides survey specific information about each tile.

Outlined below is a description of each product that is available for each survey block:

LIDAR Point Cloud: is the discrete LIDAR returns that are used in the creation of the surface models. Supplied in *.laz format they the discrete LIDAR returns have been classified into ground, low, medium and high vegetation classes using an automated classification process.

Digital Surface Model(s) (DSM) are created from the last or only LIDAR pulse returned to the sensor and contains all ground and surface objects.

Digital Terrain Model(s) (DTM) is created from the last return LIDAR pulse classified as ground, filtering out surface objects. Manual filtering is undertaken on the DTM to improve the automated classification routines to produce a most likely ground surface model. Areas of no data, such as water bodies, are also filled to ensure there are no gaps in the model.

First Return Digital Surface Model(s) (FZ DSM) is created from the either the first or only LIDAR pulse returned to the sensor and contains all ground and surface objects. It is more likely to return elevations from the top or near top of trees and the edges of buildings. It can often be used in canopy height modelling and production of building outlines.

Intensity Surface Model(s) (Int DSM) is a measure of the amount of laser light from each laser pulse reflecting from an object. This reflectivity is a function of the near infrared wavelength used and varies with the composition of the surface object reflecting the return and angle of incidence.The intensity surface model produces a grayscale image where darker surfaces such as roads reflect less light than other surfaces such as vegetation.

This dataset is a GIS layer identifying the river waterbodies managed under the Water Framework Directive and any related programmes. This includes canals and surface water transfers which are reported to Europe as artificial rivers. These can be identified using the 'WB_CAT' field. These waterbodies are represented by their centreline, and can be linked to other WFD data using the unique water body ID (WB_ID). ‘WFD River, canal and SWT Waterbodies Cycle 2’ is a subset extracted from the Environment Agency’s Detailed River Network, with attributes removed and merged on the WB_ID.

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.

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.

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.

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.

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

Distribution of Larch in private ownership, identified by visual analysis of aerial photography. Compiled by tree health and research workers. The visual nature of the analysis used to create this data, means that there are inaccuracies Area - in hectares Altitude - in metres Attribution statement: Contains OS data © Crown copyright [and database right] [year]. Data and Resources External version on Defra Data Services Platform External version on Defra Data Services Platform

This dataset is retired and no longer available on the Data Services Platform. The Environment Agency Geomatics team no longer produce a 25cm resolution composite product. This has been replaced by a 1m resolution version. The entire archive of lidar data, including the 1m composite and 25cm time-stamped data, is available to download from the following page: https://environment.data.gov.uk/survey.

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering areas of England at 25cm spatial resolution. Produced by the Environment Agency in 2017, this dataset is derived from a combination of our full time stamped archive, 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. The composite is updated on an annual basis to include the latest surveys.

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 ASCII 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. A tinted shaded relief, which is an image showing what LIDAR looks like when loaded into specialist software, is also available as a WMS feed. You can also download survey index files which shows, for any location, what Time Stamped survey went into the production of the LIDAR composite.

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 and composites of the best data available in any location.

To find out more about LIDAR and the various surface models we produce please read our story map

This metadata record is for Approval for Access product AfA458. Attribution statement: (c) Environment Agency copyright and/or database right 2019. All rights reserved.

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

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.

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

Synthetic Data Platform Service Liability Market Outlook

According to our latest research, the synthetic data platform service liability market size reached USD 1.84 billion globally in 2024. The market is exhibiting robust momentum, registering a CAGR of 29.7% during the forecast period. By 2033, the synthetic data platform service liability market is projected to attain USD 17.2 billion, driven by the increasing demand for privacy-preserving data solutions across industries. The remarkable growth is primarily attributed to the expanding adoption of artificial intelligence (AI) and machine learning (ML) technologies, which require vast, high-quality datasets while ensuring compliance with stringent data privacy regulations.

One of the primary growth factors propelling the synthetic data platform service liability market is the escalating focus on data privacy and security. In recent years, regulatory frameworks such as GDPR in Europe, CCPA in California, and other global data protection laws have heightened enterprises’ concerns regarding the use of real-world data for AI and analytics. Synthetic data platforms offer a viable solution, enabling organizations to generate realistic yet anonymized datasets that effectively mitigate privacy risks. This capability not only facilitates compliance but also unlocks new opportunities for data-driven innovation by allowing organizations to share and analyze data without exposing sensitive information. As a result, enterprises across sectors such as healthcare, finance, and retail are increasingly investing in synthetic data solutions to address liability concerns associated with data breaches and misuse.

Another significant driver of the synthetic data platform service liability market is the rapid advancement and integration of AI and ML technologies across diverse industries. These technologies depend heavily on large volumes of high-quality data for training and validation, which is often challenging to obtain due to privacy, ethical, and logistical constraints. Synthetic data platforms bridge this gap by generating customizable, bias-free, and scalable datasets that enhance model performance and generalizability. Furthermore, the liability aspect becomes critical as organizations must ensure that synthetic data does not inadvertently introduce biases or inaccuracies, which could lead to flawed decision-making or regulatory scrutiny. The growing awareness of these liability issues is prompting companies to seek robust synthetic data services that provide transparency, traceability, and risk mitigation features, fueling market expansion.

The proliferation of digital transformation initiatives, particularly in sectors such as BFSI, healthcare, and automotive, is further accelerating the adoption of synthetic data platform services. As organizations digitize their operations and leverage advanced analytics, the need for secure and compliant data handling becomes paramount. Synthetic data platforms not only support these transformation efforts by providing safe and versatile datasets but also address liability concerns related to data ownership, consent, and usage rights. The increasing complexity of data ecosystems and the rising incidence of cyber threats underscore the importance of liability management in synthetic data services. Consequently, solution providers are enhancing their offerings with advanced governance, auditing, and compliance tools to help clients navigate the evolving regulatory landscape and minimize legal exposure.

From a regional perspective, North America continues to dominate the synthetic data platform service liability market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of leading technology companies, early adoption of AI and data privacy regulations, and significant investments in digital infrastructure. Europe follows closely, driven by stringent data protection laws and a strong emphasis on ethical AI development. The Asia Pacific region is witnessing the fastest growth, supported by rapid digitalization, expanding AI initiatives, and increasing awareness of data privacy issues. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a comparatively nascent stage, as organizations in these regions begin to recognize the value of synthetic data in managing liability and compliance challenges.

The Content Services Platforms Market Report is Segmented by Component (Solutions, and Services), Deployment Model (On-Premises, and Cloud), End-User Enterprise Size (Small and Medium-Sized Enterprises, and Large Enterprises), End-User Industry Vertical (BFSI, Government and Public Sector, Healthcare and Life Sciences, IT and Telecom, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

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The Environment Agency National Water Quality Instrumentation Service provide multi-parameter sondes for continuous sampling of river water quality which are deployed for weeks or months at a time to inform pollution investigations. The data can be used to identify intermittent sources of pollution which infrequent spot sampling will miss. Past locations of EA continuous water quality monitoring sondes deployed up until March 2022. Data from the sondes are available via the Meteor platform on request from the EA. Contact Pippa Tucker to request access. Current deployment locations and live data can be viewed via the Meteor platform.During 2022 we will be working with EA to establish a regular update cycle for this dataset and to develop training webinars to help understand and interpret continous water quality monitoring data. Contact data@theriverstrust.org if you would like more information.Update May 2022 - would you like open access to the historical data? Tell EA and Defra what you would like to see published on the Defra Data Services Platform via this survey: https://forms.office.com/r/QmUV2iDwFJ The following parameters are recorded by the sondes: Temperature - In rivers can range from 0 - 25°C. Effluent discharges may be warmer then the receiving watercourse and discharges of groundwater may be cooler then the receiving watercourse. These discharges may distort the normal temperature profile. Water temperatures change with the seasons and will also change throughout the day due to ambient air temperature. Conductivity - This is affected primarily by the geology of the area. It represents the amount of inorganic dissolved solids (such as sodium, sulphate, chloride, calcium) in the water. Inputs from discharges and runoff can change this background level. A drop in value is a good indicator of rainfall. Dissolved Oxygen - Is one of the most important indicators of the health of a river and it is necessary to sustain aquatic life. The concentration is reduced by the respiration of living organisms and replenished by photosynthesis of aquatic plants and algae. In addition, increases in dissolved oxygen are driven by re-aeration from the atmosphere and turbulent flow. This dynamic change is most clearly observed as diurnal patterns in the dissolved oxygen data. pH - An indicator of the acidity or alkalinity of water. Ammonium - The presence of ammonium in a waterbody can be an indicator of pollution. It is associated with the natural decomposition of organic material, the input of sewage (both treated and untreated) or chemically derived commercial fertilisers entering the watercourse. Ammonium levels can also be increased by the presence of interfering ions found in sodium and potassium salts. The source of these may be natural or manmade. Ammonium / Ammonia levels do not directly relate to bacteriology counts. Turbidity - Is a good indicator of the amount of suspended solids in the waterbody. It is caused by either the resuspension of sediment from the bed of the river or other materials entering the watercourse during storm events. Very short transient spikes may be observed; these are due to aquatic organisms moving across the sensor.

This data is no longer available on the Defra Data Services Platform. Visit Scottish Remote Sensing Portal to access the latest LIDAR data for Scotland: https://remotesensingdata.gov.scot/data#/list

This metadata record is for Approval for Access product AfA458. 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 300,000 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 LIDAR data archive contains digital elevation data derived from surveys carried out by the Environment Agency's specialist remote sensing team. This dataset is derived from a combination of our full dataset which has been merged and re-sampled to give the best possible coverage. Data is available at 2m, 1m, 50cm, and 25cm resolution. The dataset can be supplied as a Digital Surface Model produced from the signal returned to the LIDAR (which includes heights of objects, such as vehicles, buildings and vegetation, as well as the terrain surface) or as a Digital Terrain Model produced by removing objects from the Digital Surface Model. The dataset can be presented as an ESRI Binary Grid which contains height values, or as a georeferenced JPEG which is an image showing what LIDAR looks like when loaded into specialist software. Attribution statement: Visit Scottish Remote Sensing Portal for information.

This dataset is no longer available on the Data Services Platform. New version of the LIDAR Composite DSM data is available here: https://environment.data.gov.uk/searchresults;query=lidar%20composite%202020;searchtype=All;page=1;pagesize=20;orderby=Relevancy The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~75% of England at 2m spatial resolution. Produced by the Environment Agency in 2017, this dataset is derived from a combination of our full time stamped archive, 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. The composite is updated on an annual basis to include the latest surveys. 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 ASCII 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. A tinted shaded relief, which is an image showing what LIDAR looks like when loaded into specialist software, is also available as a WMS feed. You can also download survey index files which shows, for any location, what Time Stamped survey went into the production of the LIDAR composite. 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 and composites of the best data available in any location. To find out more about LIDAR and the various surface models we produce please read our story map This metadata record is for Approval for Access product AfA458. Attribution statement: (c) Environment Agency copyright and/or database right 2019. All rights reserved. Attribution Statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

The RPA CROME GOP is a simple point dataset, that marks the location of observations made by surveyors in a sample of parcels and records the land cover according to set criteria. It has been maintained by RPA Geospatial Services since 2015. RPA currently collects ground observation data from the following source: • Agricultural parcels eligible for the Control with Remote Sensing (CwRS) element of the Basic Payment Scheme (BPS), and that fall within zones selected for monitoring claims. The locations and size of these zones vary from year to year. The field surveys for are carried out on RPA’s behalf by Cyient Europe Ltd. They have been acquired for both Control with Remote Sensing (CwRS) and for Commons Eligibility Mapping programmes that have been completed during the year. The points are attributed with: • Parcel reference ID (as with RPA Parcel Points) • Crop or land cover observed to be / have been growing at that location • Date of observation • Whether observations were made by RPA or an external surveyor • Any additional comments Two versions will be made available: one with photos of the land cover attached (RPA_CROME_GOP_2023_FULL) and the other with them removed (RPA_CROME_GOP_2023_Basic). The data for the CwRS programme is used in the production of the Crop Map of England (CROME) which is publicly available and has historically been used as part of CwRS for the Basic Payment Scheme (BPS). It is intended that releasing the ground observations would benefit research in automation, machine learning, and our national food production. RPA’s GOP use the Open Government Licence v3.0 as used by other publicly accessible data on the Defra Data Services Platform and would be updated approximately every year, subject to the continuation of current policies. Attribution statement: © Rural Payments Agency copyright and/or database right 2023. All rights reserved.