The England species map was funded by DEFRA’s Natural Capital and Ecosystem Assessment (NCEA) programme. The map was created using satellite remote sensing data (Sentinel-2) and machine learning to classify common tree species in England. The model was trained to distinguish 35 common tree species, with minority species grouped into “Other broadleaf” or “Other conifer” classes for better classification performance. The final product comprises a species classification and confidence raster output.

The species map represents a predicted distribution of common tree species in England, produced using a time series of multispectral satellite remote sensing data (Sentinel-2) and machine learning. A classifier based on the XGBoost algorithm was trained to distinguish tree species, utilising a time-series of surface reflectance data and labelled training samples from the sub-compartment database (SCDB). To enhance classification performance, minority species with fewer than 1,000 training samples were grouped into broader categories, resulting in a total of 35 classes. Given the significant class imbalances, a sample weighting strategy was employed to guard against significant underfitting of the minority classes. Model evaluation demonstrated strong classification performance, with an overall accuracy of 89% and balanced class accuracy of 90%. Predictions were made at the pixel level and used to generate a species classification and confidence raster output. Field validation for Norway spruce within the Ips typographus demarcated area, confirmed a precision of 69%, aligning with test data results for this class. Additional validation using National Forest Inventory (NFI) data further reinforced model reliability, though accuracy was observed to be worse for underrepresented species.

The species map represents a predicted distribution of common tree species in England, produced using a time series of multispectral satellite remote sensing data (Sentinel-2) and machine learning. A classifier based on the XGBoost algorithm was trained to distinguish tree species, utilising a time-series of surface reflectance data and labelled training samples from the sub-compartment database (SCDB). To enhance classification performance, minority species with fewer than 1,000 training samples were grouped into broader categories, resulting in a total of 35 classes. Given the significant class imbalances, a sample weighting strategy was employed to guard against significant underfitting of the minority classes.

Model evaluation demonstrated strong classification performance, with an overall accuracy of 89% and balanced class accuracy of 90%. Predictions were made at the pixel level and used to generate a species classification and confidence raster output. Field validation for Norway spruce within the Ips typographus demarcated area, confirmed a precision of 69%, aligning with test data results for this class. Additional validation using National Forest Inventory (NFI) data further reinforced model reliability, though accuracy was observed to be worse for underrepresented species.

🇬🇧 United Kingdom English The England species map was created using satellite remote sensing data (Sentinel-2) and machine learning to classify common tree species in England. The model was trained to distinguish 35 common tree species, with minority species grouped into “Other broadleaf” or “Other conifer” classes for better classification performance. The final product comprises a species classification and confidence raster output. The species map represents a predicted distribution of common tree species in England, produced using a time series of multispectral satellite remote sensing data (Sentinel-2) and machine learning. A classifier based on the XGBoost algorithm was trained to distinguish tree species, utilising a time-series of surface reflectance data and labelled training samples from the sub-compartment database (SCDB). To enhance classification performance, minority species with fewer than 1,000 training samples were grouped into broader categories, resulting in a total of 35 classes. Given the significant class imbalances, a sample weighting strategy was employed to guard against significant underfitting of the minority classes. Model evaluation demonstrated strong classification performance, with an overall accuracy of 89% and balanced class accuracy of 90%. Predictions were made at the pixel level and used to generate a species classification and confidence raster output. Field validation for Norway spruce within the Ips typographus demarcated area, confirmed a precision of 69%, aligning with test data results for this class. Additional validation using National Forest Inventory (NFI) data further reinforced model reliability, though accuracy was observed to be worse for underrepresented species. Attribution statement: © Forestry Commission copyright and/or database right 2024. All rights reserved.

PLEASE NOTE: This dataset has been retired. It has been superseded by https://environment.data.gov.uk/dataset/04532375-a198-476e-985e-0579a0a11b47.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.NOTE: We have paused quarterly updates of this dataset. Please visit the “Pause to Updates of Flood Risk Maps” announcement on our support pages for further information. We will provide notifications on the Flood Map for Planning website to indicate where we have new flood risk information. Other data related to the Flood Map for Planning will continue to be updated, including data relating to flood history, flood defences, and water storage areas.

The Flood Map for Planning Service includes several layers of information. This includes the Flood Zones data which shows the extent of land at present day risk of flooding from rivers and the sea, ignoring the benefits of defences, for the following scenarios:

• Flood Zone 1 – Land having a less than 0.1% (1 in 1000) annual probability of flooding. • Flood Zone 2 – Land having between 0.1% - 1% (1 in 100 to 1 in 1000) annual probability of flooding from rivers or between 0.1% - 0.5% (1 in 200 to 1 in 1000) annual probability of flooding from the sea, and accepted recorded flood outlines . • Flood Zone 3 – Areas shown to be at a 1% (1 in 100) or greater annual probability of flooding from rivers or 0.5% (1 in 200) or greater annual probability of flooding from the sea.

Flood Zone 1 is not shown in this dataset, but covers all areas not contained within Flood Zones 2 and 3. Local Planning Authorities (LPAs) use the Flood Zones to determine if they must consult the Environment Agency on planning applications. They are also used to determine if development is incompatible and whether development is subject to the exception test. The Flood Zones are one of several flood risk datasets used to determine the need for planning applications to be supported by a Flood Risk Assessment (FRA) and subject to the sequential test.

The Flood Zones are a composite dataset including national and local modelled data, and information from past floods.

The Flood Zones are designed to only give an indication of flood risk to an area of land and are not suitable for showing whether an individual property is at risk of flooding. This is because we cannot know all the details about each property.

Set of shapefiles defining Indicative Flood Risk Areas for local risk. Indicative Flood Risk Areas are provided by the Environment Agency for use by Lead Local Flood Authorities in England in their review during 2017 of Preliminary Flood Risk Assessments and Flood Risk Areas under the Flood Risk Regulations.

The Indicative Flood Risk Areas are primarily based on an aggregated 1km square grid Updated Flood Map for Surface Water (1 in 100 and 1000 annual probability rainfall), informally referred to as the “blue square map”. These are 1km grids across England and consist of the following data layers:

• Surface Water Flood Risk Exposure Grid – 1km square grid that shows places above the flood risk threshold defined, using the 1 in 100 and 1000 annual probability (deep) Flood Map for Surface Water. • Flood risk thresholds used to generate the “blue Squares”: - Number of people > 200 - Number of critical services, including electricity and water > 1 - Number of non-residential properties > 20 • Cluster Maps – are aggregations of 3km by 3km squares that each contain at least 5 touching "blue squares" (i.e. 1km grid squares where one of the thresholds above is exceeded) • Communities at Risk by Lead Local Flooding Authority • People Sensitivity Map by Lead Local Flood Authority. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Contains public sector information licensed under the Open Government Licence

PLEASE NOTE: this dataset has been retired. It has been superseded by data for Flood Risk Areas: https://environment.data.gov.uk/dataset/f3d63ec5-a21a-49fb-803a-0fa0fb7238b6

Shapefile for Indicative Flood Risk Areas generated using the Environment Agency's Communities at Risk Approach. This information is provided by the Environment Agency for use by LLFAs in their review during 2017 of Preliminary Flood Risk Assessments and Flood Risk Areas under the Flood Risk Areas. it must be used in conjunction with data for Indicative Flood Risk Areas generated by the Environment Agency using the cluster method as well.

The Indicative Flood Risk Areas are primarily based on an aggregated 1km square grid Updated Flood Map for Surface Water (1 in 100 and 1000 annual probability rainfall), informally referred to as the “blue square map”.

This dataset is a component of Indicative Flood Risk Areas (shapefiles)

A bundle download of all Indicative Flood Risk Areas spatial datasets is also available from this record. Please see individual records for full details and metadata on each product. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.

Are you looking to identify B2B leads to promote your business, product, or service? Outscraper Google Maps Scraper might just be the tool you've been searching for. This powerful software enables you to extract business data directly from Google's extensive database, which spans millions of businesses across countless industries worldwide.

Outscraper Google Maps Scraper is a tool built with advanced technology that lets you scrape a myriad of valuable information about businesses from Google's database. This information includes but is not limited to, business names, addresses, contact information, website URLs, reviews, ratings, and operational hours.

Whether you are a small business trying to make a mark or a large enterprise exploring new territories, the data obtained from the Outscraper Google Maps Scraper can be a treasure trove. This tool provides a cost-effective, efficient, and accurate method to generate leads and gather market insights.

By using Outscraper, you'll gain a significant competitive edge as it allows you to analyze your market and find potential B2B leads with precision. You can use this data to understand your competitors' landscape, discover new markets, or enhance your customer database. The tool offers the flexibility to extract data based on specific parameters like business category or geographic location, helping you to target the most relevant leads for your business.

In a world that's growing increasingly data-driven, utilizing a tool like Outscraper Google Maps Scraper could be instrumental to your business' success. If you're looking to get ahead in your market and find B2B leads in a more efficient and precise manner, Outscraper is worth considering. It streamlines the data collection process, allowing you to focus on what truly matters – using the data to grow your business.

https://outscraper.com/google-maps-scraper/

As a result of the Google Maps scraping, your data file will contain the following details:

Query Name Site Type Subtypes Category Phone Full Address Borough Street City Postal Code State Us State Country Country Code Latitude Longitude Time Zone Plus Code Rating Reviews Reviews Link Reviews Per Scores Photos Count Photo Street View Working Hours Working Hours Old Format Popular Times Business Status About Range Posts Verified Owner ID Owner Title Owner Link Reservation Links Booking Appointment Link Menu Link Order Links Location Link Place ID Google ID Reviews ID

If you want to enrich your datasets with social media accounts and many more details you could combine Google Maps Scraper with Domain Contact Scraper.

Domain Contact Scraper can scrape these details:

Email Facebook Github Instagram Linkedin Phone Twitter Youtube

This dataset consists of the 1km raster, percentage target class version of the Land Cover Map 1990 (LCM1990) for Northern Ireland. The 1km percentage product provides the percentage cover for each of 21 land cover classes for 1km x 1km pixels. This product contains one band per target habitat class (producing a 21 band image). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. This dataset is derived from the vector version of the Land Cover Map, which contains individual parcels of land cover and is the highest available spatial resolution. LCM1990 is a land cover map of the UK which was produced at the UK Centre for Ecology & Hydrology by classifying satellite images (mainly from 1989 and 1990) into 21 Broad Habitat-based classes. It is the first in a series of land cover maps for the UK, which also includes maps for 2000, 2007, 2015, 2017, 2018 and 2019. LCM1990 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the UKCEH web site and the LCM1990 Dataset documentation) to select the product most suited to their needs. 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. Full details about this dataset can be found at https://doi.org/10.5285/4a5e720f-5f1d-4e96-8e34-ae32c668c613

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale.

The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage.

Climate Just Map Tool includes maps on:

• Flooding (river/coastal and surface water)
• Heat
• Fuel poverty.

The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data.

Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls

Indicators include:

Climate Just-Flood disadvantage_2011_Dec2014.xlsx

Fluvial flood disadvantage index
Pluvial flood disadvantage index (1 in 30 years)
Pluvial flood disadvantage index (1 in 100 years)
Pluvial flood disadvantage index (1 in 1000 years)

Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx

Percentage of area at moderate and significant risk of fluvial flooding
Percentage of area at risk of surface water flooding (1 in 30 years)
Percentage of area at risk of surface water flooding (1 in 100 years)
Percentage of area at risk of surface water flooding (1 in 1000 years)

Climate Just-SSVI_indices_2011_Dec2014.xlsx

Sensitivity - flood and heat
Ability to prepare - flood
Ability to respond - flood
Ability to recover - flood
Enhanced exposure - flood
Ability to prepare - heat
Ability to respond - heat
Ability to recover - heat
Enhanced exposure - heat
Socio-spatial vulnerability index - flood
Socio-spatial vulnerability index - heat

Climate Just-SSVI_indicators_2011_Dec2014.xlsx

% children < 5 years old
% people > 75 years old
% people with long term ill-health/disability (activities limited a little or a lot)
% households with at least one person with long term ill-health/disability (activities limited a little or a lot)
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% households rented from social landlords
% households rented from private landlords
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% people with % unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Crime score (IMD)
% area not road
Density of retail units (count /km2)
% change in number of local VAT-based units
% people with % not home workers
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (Pounds)
% all pensioner households
% born outside UK and Ireland
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Travel time to nearest GP by walk/public transport (mins - representative time)
% of at risk pop

The Flood Map for Planning (Rivers and Sea) includes several layers of information. This layer and documentation covers Flood Zone 2. It is the Environment Agency's best estimate of the areas of land at risk of flooding, when he 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. This dataset is republished by the West of England Combined Authority for supplementing information within our Local Nature Recovery Strategy. If you are using it for statutory purposes, you should refer to the Environment Agency's canonical version, linked in the Attributions field below as this is likely to be more current.

This method returns Crystal Roof’s proprietary crime rate map overlays. These overlays are taken directly from our main Crime Rates map.

The overlays are circular PNG images, available in 1,000, 1,500, or 2,000-meter radii.

You can request overlays showing either total crime rates or crime rates for a specific crime type (controlled by the variant parameter).

About Crystal Roof’s Crime Rates Map

• Crime rates are calculated for small geographic areas known as Lower Layer Super Output Areas (LSOAs).
• Rates are calculated per 1,000 residents, using population data from the 2021 Census.
• Crime levels are grouped into 10 categories using our proprietary algorithm, which balances both the distribution of crime values and the number of areas with similar rates. These categories are not standard deciles.
• All figures represent annual data (covering the most recent 12 months).
• The dataset is updated monthly, with a three-month lag between the current date and the most recent available data.

Integration examples

• Google Maps integration example
• Leaflet integration example

Israel Exports of maps, hydrographic or similar charts (printed) to United Kingdom was US$1000 during 2024, according to the United Nations COMTRADE database on international trade. Israel Exports of maps, hydrographic or similar charts (printed) to United Kingdom - data, historical chart and statistics - was last updated on June of 2025.

PLEASE NOTE: this dataset has been retired. It has been superseded by data for Flood Risk Areas: https://environment.data.gov.uk/dataset/f3d63ec5-a21a-49fb-803a-0fa0fb7238b6

The Indicative Flood Risk Areas are primarily based on an aggregated 1km square grid Updated Flood Map for Surface Water (1 in 100 and 1000 annual probability rainfall), informally referred to as the “blue square map”.

• Cluster Maps – are aggregations of 3km by 3km squares that each contain at least 4 (in Wales) or 5 (in England) touching "blue squares" (i.e. 1km grid squares where one of the thresholds above is exceeded)

This dataset forms part of Indicative Flood Risk Areas (shapefiles)

A bundle download of all Indicative Flood Risk Areas spatial datasets is also available from this record. Please see individual records for full details and metadata on each product.

The surface water flood maps give an indication of the broad areas likely to be at risk of surface water flooding. This includes flooding that takes place from the surface runoff generated by rainwater (including snow and other precipitation) that: (a) is on the surface of the ground (whether or not it is moving), and (b) has not yet entered a watercourse, drainage system or public sewer. The Flood Map for Surface Water pick out natural drainage channels, rivers, low areas in floodplains, and flow paths between buildings. But it does not indicate flooding caused by local rainfall. It does not show flooding that occurs from overflowing watercourses, drainage systems or public sewers caused by catchment-wide rainfall events or river flow. A national model has been run for 1 in 30, 1 in 100 and 1 in 1000 year rainfall events. It has been modelled on a 2 metre square grid. Lead Local Flood Authorities were consulted and where available locally held model outputs have been incorporated into the maps. This dataset has been superseded by the new National Flood Hazard Maps 2019 - NRW_DS124790

This dataset provides the first map and synthesis of the temperature of Britain's coalfields. It was created to support low-temperature heat recovery, cooling and storage schemes using mine water in abandoned workings. This baseline spatial mapping and synthesis of coalfield temperatures offers significant benefit to those planning, designing and regulating heat recovery and storage in Britain's abandoned coalfields. The data has been developed jointly by the Coal Authority and the British Geological Survey. It is delivered as a hexgrid representing mine water blocks, identifying equilibrium mine temperatures at 10 depth intervals (100m > 1000m) and pumped mine temperatures at 6 depth intervals (100m > 600m).

This is a spatial dataset that defines the non-designated geographic extent and location of Historic Parklands in England, as depicted in the OS Historic Maps (2nd Edition and Hills Edition). Regularly updated aerial imagery has been used to ensure high spatial accuracy. The mapping scale is 1:1,000. This dataset, alongside the designated landscapes within the Historic England Registered Parks and Gardens layers, and HER searches can provide a comprehensive understanding of the maximum historic extent of Parklands.Historic parklands are uniquely placed to deliver integrated multi-objective benefits for the historic and natural environments. They are a finite and non-renewable resource, and they remain working landscapes. Through active management, their countless interests can be secured to great public benefit.This dataset was developed using multiple reference layers, each contributing unique value. The layers are listed below in order of precedence for decision-making:Aerial Photography of Great Britain – High-resolution imagery used for precise boundary verification of historic parklands.OS 2nd Edition Historic Map (1888–1913) – Detailed topographic maps capturing landscape changes during the late Victorian and Edwardian periods, with a - particular focus on Parks and Ornamental Grounds.OS Hills Edition Historic Map (1885–1903) – One-inch-to-the-mile maps with shaded relief, providing insights into terrain and historical land use, with a specific emphasis on Parks and Ornamental Grounds.HE Registered Parks and Gardens (RPG) – Contains designated historic parkland names and boundaries.NE Wood Pasture and Parkland – Represents vegetation structure rather than historic features, used selectively when other sources are unavailable, although it has limited relevance for defining parkland boundaries.NE Ancient Woodland – Depicts ancient woodlands but has limited relevance for defining parkland boundaries.NE Historic Parkland – A pre-existing dataset with low spatial accuracy and no metadata, serving as a reference layer for areas requiring review (targeting only).Attribution Column Heading Full Name Format Description

Name Parkland Name Character (254) Name of Parkland

Part_N Part Number Character (5) Part number in case the parkland is formed by several polygon

Comment Comment Character (254) Open text field to record any unusual or specific cases

HER_Name HER Name Character (55) Name for each of the 83 Historic Environment Records geographic areas in England.

HE_Region HE Region Character (25) Name for each of 6 Historic England Regions

Area_m2 Area m2 Double The size of the parkland features square meters or hectares.

Length_m Length m (perimeter) Double The perimeter length of the parkland feature.

Creator Creator Character (25) The contractor, team or person who created the dataset entry.

C_Date Creation Date Date The date when the dataset entry was created.

Editor Editor Character (25) The contractor, team or person who last edited the dataset entry.

E_Date Edition Date Date The date of the last edit.

Full metadata can be viewed on environment.data.gov.uk

This dataset contains river (fluvial) and surface water (pluvial) flooding maps for the central highlands of Vietnam and surrounding provinces. Flood depth is estimated at 30m horizontal grid spacing for 10 return periods, ranging from the 1 in 5 year to the 1 in 1000 year return period flood. These maps are of relevance to planners and policy makers to estimate which areas of most at risk of flooding and can contribute towards policy such as the sustainable development goals.

The Flood Zones show the probability of river and sea flooding, ignoring the presence of defences.

There are four zones, 1, 2, 3a and 3b, that reflect the annual probability of flooding happening.

This map shows all areas with more than a 1 in 1,000 annual probability of either river or sea flooding by combining zones 2, 3a and 3b.

If you want to submit a planning application within these zones, you will need to do a flood risk assessment. For a full explanation of when a flood risk assessment is required, visit https://www.gov.uk/guidance/flood-risk-assessment-for-planning-applications#when-you-need-an-assessment.

To view more detailed information on the flood zones, visit https://flood-map-for-planning.service.gov.uk/.

These 3 layers show the extent of flooding from surface water that could result from a flood with a 3.3% (1 in 30), 1% (1 in 100), and 0.1% (1 in 1000) chance of happening in any given year.Surface water flooding happens when rainwater does not drain away through the normal drainage systems or soak into the ground, but lies on or flows over the ground instead. Managing the risk of flooding from surface water is the responsibility of lead local flood authorities (LLFA). The LLFA is the unitary authority or if there is no unitary authority, the county council for the area.The Environment Agency (EA) is responsible for publishing surface water flood risk maps however mapping of surface water flood risk areas is responsibility of LLFAs. We, the EA, produced the Risk of Flooding from Surface Water (RoFSW) map on behalf of LLFAs, using their input and information. It assesses flooding scenarios as a result of rainfall with a 3.3% (1 in 30), 1% (1 in 100), or 0.1% (1 in 1000) chance of occurring each year. Although surface water flood risk information is not suitable for identifying whether an individual property will flood it does gives an indication of the broad areas likely to be affected.More information - What is the Risk of Flooding from Surface map