Vertical aerial photography is an airborne mapping technique, which uses a high-resolution camera mounted vertically underneath the aircraft to capture reflected light in the red, green, blue and for some datasets, near infra-red spectrum. Images of the ground are captured at resolutions between 10cm and 50cm, and ortho-rectified using simultaneous LIDAR and GPS to a high spatial accuracy.

The Environment Agency has been capturing vertical aerial photography data regularly since 2006 on a project by project basis each ranging in coverage from a few square kilometers to hundreds of square kilometers. The data is available as a raster dataset in ECW (enhanced compressed wavelet) format as either a true colour (RGB), near infra-red (NIR) or a 4-band (RGBN) raster. Where imagery has been captured under incident response conditions and the lighting conditions may be sub-optimal this is defined by the prefix IR. The data are presented as tiles in British National Grid OSGB 1936 projections. Data is available in 5km download zip files for each year of survey. Within each zip file are ECW files aligned to the Ordinance Survey grid. The size of each tile is dependent upon the spatial resolution of the data.

Please refer to the metadata index catalgoues for the survey date captured, type of survey and spatial resolution of the imagery.

S2BMS (Sentinel-2 UK Butterfly Monitoring Scheme) data set

This data set consists of cloud-free Sentinel-2 (RGB+NIR, 10 m resolution) satellite images and aggregated measurements of butterfly occurrence for 1,329 locations in the UK.

The code (to load the data) is available at https://github.com/vdplasthijs/PECL/, and in particular please see the DataSetImagePresence data loader class. The data is formatted as 1 satellite image tif file per location and 1 (geo-referenced) csv file with the species presence data for all locations.

The Sentinel-2 data was acquired from Google Earth Engine. The UKBMS data was acquired from GBIF: https://doi.org/10.15468/dl.u4rzfc

For more information, please see our upcoming paper at the 2025 CVPR FGVC12 workshop, titled:

Van der Plas, Law, Pocock, Predicting butterfly species presence from satellite imagery using soft contrastive regularisation, CVPRW proceedings (2025).

This data is provided under a CC-BY-NC 4.0 license; please cite the above publication when using the data in publications. Thank you!

DiGBcoast v1.0, is a new supranational dataset documenting three decades of coastal change across Great Britain mainland (England, Scotland, and Wales) including the isle of Wight and Anglesey. This dataset has been produced using the publicly available optical Landsat-5,8 and Sentinel-2 missions over the period between 1984 to 2022 (38 years). It includes instantaneous waterlines and instantaneous tidally corrected to Mean Sea Level shorelines. DiGBcoast is made available to the public as free and open interactive data to support future coastal research and management across Great Britain.

The EUMETSAT Spinning Enhanced Visible and InfraRed Imager (SEVIRI) rapid scanning service (RSS) takes an image of the northern third of the Meteosat disc every five minutes (see the EUMETSAT website for more information on SEVIRI RSS ). The original EUMETSAT dataset contains data from 2008 to the present day from 12 channels, and for a wide geographical extent covering North Africa, Saudi Arabia, all of Europe, and Western Russia. This Google Cloud Dataset covers the full geographical extant, nearly full temporal extant, and all 12 channels up to the present. Some individual timesteps might be missing. This dataset is slightly transformed: It does not contain the original numerical values. See the "samples" section for more technical detail about the dataset. The original data is copyright EUMETSAT . EUMETSAT has given permission to redistribute this transformed data. The data was transformed by Open Climate Fix using satip . This public dataset is hosted in Google Cloud Storage and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. En savoir plus

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

Caeli can provide this data through an API, dashboard, real-time geo map, or via datasets(.csv). In addition, all this data is available in daily, monthly and annual formats. The data can be delivered in various spatial resolutions starting from 0.001 degrees latitude and longitude (WSG 84), which roughly converts to 100X100 meter.

The Caeli datasets are often used for creating and validating various models and for training machine learning algorithms. We’ll allow you to specify your state or country, your preferred timeframe, resolution, and pollutant. Based on this information we’ll compile a reliable dataset. The measurements in de dataset can be used in determining the air quality of a region for a specific period of time. Additionally, your composite dataset can also serve for strategy and reporting purposes, such as ESG strategy, TCDF, SFDR, and sustainable decision making. The price of the dataset is based on the size of the area, the resolution chosen, and the number of years.

Are you interested in one of these pollutants or would you like to gather more information about our opportunities? Please, do not hesitate to contact us. www.caeli.space

Sector coverage: Financial | Energy | Government | Agricultural | Health | Shipping.

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Process Description:A number of data layers are used to develop a ground dataset of habitat reference data, which are then used to inform a machine-learning model and spatial analyses to generate a map of the likely locations and distributions of habitats across England. The main source data layers underpinning the spatial framework and models are Sentinel-2 and Sentinel-1 satellite data from the ESA Copernicus programme, Lidar from the EA's national Lidar Programme and collected data through the project's national survey programme. Additional datasets informing the approach as detailed below and outlined in the accompanying technical user guide.Datasets used:OS MasterMap® Topography Layer; Geology aka BGS Bedrock Mapping 1:50k; Long Term Monitoring Network; Uplands Inventory; Coastal Dune Geomatics Mapping Ground Truthing; Crop Map of England (RPA) CROME; Lowland Heathland Survey; National Grassland Survey; National Plant Monitoring Scheme; NE field Unit Surveys; Northumberland Border Mires Survey; Sentinel-2 multispectral imagery; Sentinel-1 backscatter imagery; Sentinel-1 single look complex (SLC) imagery; National forest inventory (NFI); Cranfield NATMAP; Agri-Environment HLS Monitoring; Living England desktop validation; Priority Habitat Inventory; Space2 Eye Lens: Ainsdale NNR, State of the Bog Bowland Survey, State of the Bog Dark Peak Condition Survey, State of the Bog Manchester Metropolitan University (MMU) Mountain Hare Habitat Survey Dark Peak, State of the Bog; Moors for the Future Dark Peak Survey; West Pennines Designation NVC Survey; Wetland Annex 1 inventory; Soils-BGS Soil Parent Material; Met Office HadUK gridded climate product; Saltmarsh Extent and Zonation; EA LiDAR DSM & DTM; New Forest Mires Wetland Survey; New Forest Mires Wetland Survey; West Cumbria Mires Survey; England Peat Map Vegetation Surveys; NE protected sites monitoring; ERA5; OS Open Built-up Areas; OS Boundaries dataset; EA IHM (Integrated height model) DTM; OS VectorMap District; EA Coastal Flood Boundary: Extreme Sea Levels; AIMS Spatial Sea Defences; LIDAR Sand Dunes 2022; EA Coastal saltmarsh species surveys; Aerial Photography GB (APGB); NASA SRT (Shuttle Radar Topography Mission) M30; Provisional Agricultural Land Classification; Renewable Energy Planning Database (REPD); Open Street Map 2024.Attribute descriptions: Column Heading Full Name Format Description

SegID SegID Character (100) Unique Living England segment identifier. Format is LEZZZZ_BGZXX_YYYYYYY where Z = release year (2223 for this version), X = BGZ and Y = Unique 7-digit number

Prmry_H Primary_Habitat Date Primary Living England Habitat

Relblty Reliability
Character (12) Reliability Metric Score

Mdl_Hbs Model_Habs Interger List of likely habitats output by the Random Forest model.

Mdl_Prb Model_Probs Double (6,2) List of probabilities for habitats listed in ‘Model_Habs’, calculated by the Random Forest model.

Mixd_Sg Mixed_Segment Character (50) Indication of the likelihood a segment contains a mixture of dominant habitats. Either Unlikely or Probable.

Source Source

Description of how the habitat classification was derived. Options are: Random Forest; Vector OSMM Urban; Vector Classified OS Water; Vector EA saltmarsh; LE saltmarsh & QA; Vector RPA Crome, ALC grades 1-4; Vector LE Bare Ground Analysis; LE QA Adjusted

SorcRsn Source_Reason

Reasoning for habitat class adjustment if ‘Source’ equals ‘LE QA Adjusted’

Shap_Ar Shape_Area

Segment area (m2) Full metadata can be viewed on data.gov.uk.

This is the land parcels (polygon) dataset for the UKCEH Land Cover Map of 2019 (LCM2019) representing Great Britain. It describes Great Britain's land cover in 2019 using UKCEH Land Cover Classes, which are based on UK Biodiversity Action Plan broad habitats. This dataset was derived from the corresponding LCM2019 20m classified pixels dataset. All further LCM2019 datasets for Great Britain are derived from this land parcel product. A range of land parcel attributes are provided. These include the dominant UKCEH Land Cover Class given as an integer value, and a range of per-parcel pixel statistics to help to assess classification confidence and accuracy; for a full explanation please refer to the dataset documentation. LCM2019 represents a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2019. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2019. LCM2019 was simultaneously released with LCM2017 and LCM2018. These are the latest in a series of UKCEH land cover maps, which began with the 1990 Land Cover Map of Great Britain (now usually referred to as LCM1990) followed by UK-wide land cover maps LCM2000, LCM2007 and LCM2015. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas.

This dataset contains cloud top height product images from MSG satellites over the mesoscale area. Imagery available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.

The geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).

The Land Cover Map of Great Britain is a digital map derived from the classification of cloud-free satellite images from the American Landsat Thematic Mapper (TM).

       Seventeen key land cover classes were identified throughout Britain and,
       of these, eight classes were subdivided into, for example, upland and lowland
       variants, giving a total of 25 cover types.

       The 25 land cover types include built-up areas, arable farmland, pastures
       and forestry, together with a variety of semi-natural vegetation types.

       The classes are mapped on a 25 m raster overlay of the complete land
       surface of Great Britain. The data are held in digital form as a 25 m raster
       overlay of the British National Grid and can be presented in a variety of
       digital exchange formats.

       They can be summarised by 1 km squares (or other resolutions) of the
       National Grid and summary data are also held as Oracle tables.

        There are a wide range of users of the Land Cover Map in organisations
       concerned with environmental impact assessments, pollution control, water
       resources management, policy and planning, and environmental management.

       Its greatest potential can be realised through integration with other
       data, such as geology, soils, climate, biological records, agricultural
       statistics, and population census for use in decision support systems and
       geographical information systems.

Our Building Footprint dataset enables hyper-accurate geospatial analysis by mapping real-world places — from stores to landmarks — with precise polygon geometries.

Powered by satellite imagery, machine learning, and human validation, this dataset allows businesses to attribute visits to exact locations and conduct granular area analysis with minimal margin of error.

Key data points include: - POI-linked polygons - Location name, category, and coordinates - Boundary shapes for offices, shops, amenities, etc. - Regular monthly or quarterly updates - Ready for geospatial analysis tools

Ideal for foot traffic attribution, site planning, and growth opportunity discovery, this dataset covers thousands of commercial zones with industry-grade precision.

Under an ESA contract TSS receives global ATSR data from the ERS-1 satellite. This is a demonstration project where data is processed in Tromso and the products are distributed directly to the end user. The UK Met. Office is one of the users having received ATSR data products from TSS. This near real-time service is set up using Internet for transmission of the products. The operational service aquires an average of 10 ERS-1 passes a day, every day throughout the year. Through this near real-time demonstration project, the TSS infrastructure has proven to be very well suited for supporting an operational service with useful and necessary information.

 A full resolution ATSR data product chain has also been implemented
 very recently. TSS can now offer in near-real time full resolution (1
 km) brightness and sea surface temperature products.

 The coverage is global for the ATSR, and for the AVHRR the coverage is
 that of TSS (i.e. Europe).

DataClap is a data annotation and Human in the Loop services company providing data annotation and data curation tasks for AI companies.

For the past few years, we've been helping clients across Europe and North America with the development of some of the most advanced AI solutions and products with our services.

We are a team of 60+ people that has data annotation experience across industries like

ADAS Digital Signage Self-Checkout Mapping Industrial Automation Agritech Fashion and E-Commerce Fintech Insurtech Sports Surveillance

Types of annotations we support:

Image tagging Bounding boxes Key points/Landmarks Polygons Cuboids Lines and splines Instance segmentation Semantic segmentation 3D point cloud/LiDAR Entity recognition

We have a flexible pricing model and offer a free-of-cost pilot if you want to try our services. We are a customer-centric organization and take pride in the quality of services we offer. We are GDPR compliant and ISO 27001 certified.

This CD-ROM set contains the Volume 1 vegetation data collection. The data covers a 24 month period, 1987-1988, and all but one are mapped to a common spatial resolution and grid (1 degree x 1 degree). Temporal resolution for most datasets is monthly; however, a few are at a finer resolution (e.g., 6-hourly). This dataset contains data covering: * Background (soil/litter layer) reflectances * Fraction of Photosynthetically Active radiation absorbed by the green vegetation canopy (FPAR) * Fourier-Adjustment, Solar zenith angle corrected, Interpolated Reconstructed (FASIR) normalized difference vegetation index (NDVI) * Percentage green leaf area of total leaf area * Total Leaf Area Index (LAI) * Normalized Difference Vegetation index (NDVI)
* Roughness length (Zo)
* Calculated snow-free albedo
* Global land cover classification from satellite data

This is a copy of The Berlin Stratospheric Data Series provided to the BADC by K. Labitzke and her collaborators (2002) as a CD from the Meteorological Institute, Free University Berlin. This data set contains temperature and geopotential height mean data on the 100, 50, 30, 10 mb pressure surfaces produced at the Meteorological Institute, Free University of Berlin.

The England Peat Map is a map of England's peaty soils. It models the extent, depth, and condition of our peat including vegetation and upland peat erosion & drainage features (grips, gullies, bare peat and peat hagging). The map and, where possible, the associated data, are available openly and free to use for any purpose.

This map is funded by the Nature for Climate Fund and the Natural Capital and Ecosystem Assessment (NCEA), both part of the Department for the Environment, Food and Rural Affairs (DEFRA).

The map layers were created using machine learning and deep learning modelling techniques, trained with pre-existing survey data collated from Defra organisations and other stakeholders, as well as new survey data collected by contractors and quality-assured by an in-house team. Predictor data used in the modelling process included national-scale satellite imagery, topographic LiDAR, geological and historic land-use data. Data collated from multiple sources and collated by the England Peat Map project. See NERR149 England Peat Map Final Report Annex 5 for more information.

Collated and combined peaty soil presence, depth, vegetation and erosion and drainage feature data from England Peat Map (EPM) and non-EPM sources. One feature per observation point, with peaty soil depth values provided where this is included in the data.

For more information, see NERR149 England Peat Map Final Report, Natural England, 2025.

Snapshot img

Satellite-Based Earth Observation Market Size 2025-2029

The satellite-based earth observation market size is valued to increase USD 9.66 billion, at a CAGR of 12% from 2024 to 2029. Use of satellites for advanced environment monitoring will drive the satellite-based earth observation market.

Major Market Trends & Insights

North America dominated the market and accounted for a 43% growth during the forecast period.
By Application - Defense segment was valued at USD 2.99 billion in 2023
By Type - Value-Added Services (VAS) segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 171.22 million
Market Future Opportunities: USD 9655.50 million
CAGR from 2024 to 2029 : 12%

Market Summary

The market is experiencing significant expansion, driven by the increasing demand for real-time, high-resolution data to address environmental and socio-economic challenges. Small satellites, with their cost-effective production and deployment, are gaining popularity, contributing to the market's expansion. These agile spacecraft offer enhanced flexibility and customization, catering to various applications, from agriculture and forestry to disaster management and urban planning. However, competition from alternate technologies, such as drones and airborne sensors, poses challenges to the market's growth.
Satellite-based solutions, while offering extensive coverage and longer data collection periods, face limitations in terms of spatial and temporal resolution. To maintain their competitive edge, market players are investing in technological advancements, including hyperspectral and multispectral imaging, machine learning algorithms, and real-time data processing capabilities. The future of the market lies in the integration of these technologies to deliver actionable insights, enabling informed decision-making for businesses and governments alike.

What will be the Size of the Satellite-Based Earth Observation Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Satellite-Based Earth Observation Market Segmented ?

The satellite-based earth observation 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.

Application

  Defense
  Weather
  Location-Based Services (LBS)
  Energy
  Others


Type

  Value-Added Services (VAS)
  Data


Technology

  Synthetic aperture radar (SAR)
  Optical


End-User

  Government
  Commercial
  Academic/Research


Geography

  North America

    US
    Canada


  Europe

    Germany
    Russia
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The defense segment is estimated to witness significant growth during the forecast period.

The market continues to evolve, with radar satellite data playing a pivotal role in applications such as crop yield prediction, land cover mapping, and infrastructure monitoring. Radiometric calibration, image classification algorithms, and geometric correction are essential techniques for enhancing the accuracy of remote sensing imagery and Lidar data acquisition. Change detection techniques and spectral signature analysis facilitate natural resource assessment, environmental monitoring systems, and climate change modeling. In 2024, the defense segment accounted for a substantial share of the market, with ongoing investment in satellite technologies for surveillance, regional security, and intelligence gathering by countries like China, India, and Russia.

For instance, Airbus secured agreements with the Czech Republic and the Netherlands to deliver satellite communications for their armed forces, marking a significant contribution to the UHF military communications hosted payload on board the EUTELSAT 36D telecommunications satellite. Geospatial data analytics, image registration techniques, cloud computing platforms, and satellite data processing are integral components of this dynamic industry, enabling precision agriculture applications, disaster response management, pollution detection methods, urban planning initiatives, and multispectral imagery analysis.

Request Free Sample

The Defense segment was valued at USD 2.99 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 43% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Satellite-Based Earth Observation Mar

Snapshot img

Small Satellite Market Size 2024-2028

The small satellite market size is valued to increase USD 6.01 billion, at a CAGR of 21.22% from 2023 to 2028. Low-cost solution deployment through micro- and nanosatellites will drive the small satellite market.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By Application - Earth observation and remote sensing segment was valued at USD 773.80 billion in 2022
By Type - Minisatellite segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 344.44 million
Market Future Opportunities: USD 6013.40 million
CAGR : 21.22%
North America: Largest market in 2022

Market Summary

The market is a dynamic and continually evolving sector, driven by advancements in core technologies and applications. With the increasing adoption of micro- and nanosatellites as low-cost solutions, the market is witnessing significant growth in deployment. According to recent reports, the market is expected to account for over 30% of the total satellite launches by 2025. This trend is fueled by the growing use of 3D printing in small satellite manufacturing, enabling faster production and customization.
However, the market also faces challenges from disruptions caused by satellite orbital debris, necessitating the development of advanced debris mitigation technologies. Regulations, such as the increasing number of licensing requirements and space traffic management policies, are also shaping the market landscape.

What will be the Size of the Small Satellite Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Small Satellite Market Segmented and what are the key trends of market segmentation?

The small satellite industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Earth observation and remote sensing
  Satellite communication
  Navigation
  Scientific research and others


Type

  Minisatellite
  Nanosatellite
  Microsatellite


End User

  Commercial
  Government
  Defense


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Application Insights

The earth observation and remote sensing segment is estimated to witness significant growth during the forecast period.

Small satellites, including cubesats and microsatellites, are revolutionizing the earth observation and remote sensing industry. These satellites, equipped with advanced imaging sensors, multispectral or hyperspectral cameras, and other remote sensing instruments, offer high-resolution imaging capabilities. They capture detailed images of the Earth's surface, enabling precise monitoring of land use, urban development, vegetation health, and changes in environmental conditions. The market for small satellites is experiencing significant growth, with earth observation and remote sensing applications driving this trend. According to recent studies, the number of small satellite launches increased by 25% in 2020 compared to the previous year.

Furthermore, the demand for high-resolution earth imagery is projected to grow at a rate of 18% annually over the next five years. Advancements in satellite technology, such as attitude determination control systems, cubesat deployment mechanisms, onboard data processing, and advanced propulsion systems, are contributing to the growth of the market. Additionally, data downlink systems, miniaturized payloads, data compression algorithms, satellite bus design, orbital mechanics simulations, formation flying control, cubesat technology, autonomous navigation, high-throughput communication, and spacecraft propulsion are all playing crucial roles in enhancing the functionality and efficiency of small satellites. Moreover, the integration of earth observation sensors, space debris mitigation techniques, radiation hardening, space situational awareness, and GNSS augmentation systems is expanding the applications of small satellites beyond traditional earth observation and remote sensing.

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The Earth observation and remote sensing segment was valued at USD 773.80 billion in 2018 and showed a gradual increase during the forecast period.

The market is also witnessing the emergence of modular satellite platforms, ADCS subsystem design, software-defined radio, and power system efficiency, which are enabling the development of cost-effective and versatile small satellite

The England Peat Map is a map of England's peaty soils. It models the extent, depth, and condition of our peat including vegetation and upland peat erosion & drainage features (grips, gullies, bare peat and peat hagging). The map and, where possible, the associated data, are available openly and free to use for any purpose. This map is funded by the Nature for Climate Fund and the Natural Capital and Ecosystem Assessment (NCEA) programme, both part of the Department for the Environment, Food and Rural Affairs (DEFRA). The map layers were created using machine learning and deep learning modelling techniques, trained with pre-existing survey data collated from Defra organisations and other stakeholders, as well as new survey data collected by contractors and quality-assured by an in-house team. Predictor data used in the modelling process included national-scale satellite imagery, topographic LiDAR, geological and historic land-use data. Data collated from multiple sources and collated by the England Peat Map project. See NERR149 England Peat Map Final Report Annex 5 for more information. Primary data collection, in the field, by Natural England and contractors. Full metadata can be viewed on environment.data.gov.uk.

Landsat TM data has been available since launch of Landsat 4 on 17 July 1982 and Landsat 5 on 1 March 1984. The National Remote Sensing Centre (NRSC) acquired this data from the ESA receiving stations to build up its archive of good quality scenes of the UK. The archive also contains scenes from various countries around the world. The TM data is available in 7 bands. The resolution is 30m for the visible, near and middle infrared bands and 120m for the thermal infrared. The repeat cycle is 16 days.

 The products available from the NRSC are:

   i) B/W print or film of a single band full or 1/4 scene or extract
  ii) TM colour composite print or film of a full scene, 1/4 scene or
 extract
  iii) Digital versions of scenes on CCT, Exabyte or CD-ROM

 Price lists of these products are available on request to the National
 Remote Sensing Centre (NRSC).