This dataset shows captured water courses within the Nations Forests. These will be natural water courses which will usually be permanent. Last updated from Forester Web on 13/12/2024

This is a web map service (WMS) for the 10-metre Land Cover Map 2023. The map presents the and surface classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats.UKCEH’s automated land cover algorithms classify 10 m pixels across the whole of UK. Training data were automatically selected from stable land covers over the interval of 2020 to 2022. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.Land cover was validated by organising the 10 m pixel classification into a land parcel framework (the LCM2023 classified land parcels product). The classified land parcels were compared to known land cover producing a confusion matrix to determine overall and per class accuracy.

Statutory Main Rivers Map is a spatial (polyline) dataset that defines statutory watercourses in England designated as Main Rivers by the Environment Agency.

Watercourses designated as ‘main river’ are generally the larger arterial watercourses. The Environment Agency has permissive powers, but not a duty, to carry out maintenance, improvement or construction work on designated main rivers.

All other open water courses in England are determined by statute as an ‘ordinary watercourse’. On these watercourses the Lead Local flood Authority or, if within an Internal Drainage District, the Internal Drainage Board have similar permissive powers to maintain and improve.

Didemnum vexillum is an invasive sea squirt that is not native to UK shores. It was first detected in Europe in 1991 and has since spread to several countries (including France, Ireland and the UK). The species has been located in Wales, Scotland and England and there is concern D. vexillum may have negative impacts on biodiversity and shellfish interests.

Predicting the spread of an invasive species is crucial when assessing possible management actions. The potential impacts of the species on both biodiversity and commercial interests need to be studied and a cost-benefit approach taken to decide on the best course of management for that species.

Geographic Information System (GIS) offers a fast, efficient way to map this predicted spread. The results of this mapping can then be used to focus on areas where D. vexillum may conflict with conservation and commercial interests.

Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows. Trees have a complex geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.Using the modelFollow the guide to use the model. The model can be used with the 3D Basemaps solution and ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with the attributes: X, Y, Z, and Number of Returns.Note: This model is trained to work on unclassified point clouds that are in a projected coordinate system, where the units of X, Y, and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The provided deep learning model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification.This model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time and compute resources while improving accuracy. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block, and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following 2 classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThis model is expected to work well in all regions globally, with an exception of mountainous regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. Class Precision Recall F1-score Trees / High-vegetation (5) 0.975374 0.965929 0.970628Training dataThis model is trained on a subset of UK Environment Agency's open dataset. The training data used has the following characteristics: X, Y and Z linear unit meter Z range -19.29 m to 314.23 m Number of Returns 1 to 5 Intensity 1 to 4092 Point spacing 0.6 ± 0.3 Scan angle -23 to +23 Maximum points per block 8192 Extra attributes Number of Returns Class structure [0, 5]Sample resultsHere are a few results from the model.

The indicative soils map classifies the soils of Ireland on a categorically simplified but cartographically detailed basis into 25 classes, using an expert rule based methodology. Produced by Teagasc (Kinsealy), EPA and GSI.

Land Cover Map 2021 (LCM2021) is a suite of geospatial land cover datasets (raster and polygon) describing the UK land surface in 2021. These were produced at the UK Centre for Ecology & Hydrology by classifying satellite images from 2021. Land cover maps describe the physical material on the surface of the country. For example grassland, woodland, rivers & lakes or man-made structures such as roads and buildingsThis is a 10 m Classified Pixel dataset, classified to create a single mosaic of national cover. Provenance and quality:UKCEH’s automated land cover classification algorithms generated the 10m classified pixels. Training data were automatically selected from stable land covers over the interval of 2017 to 2019. A Random Forest classifier used these to classify four composite images representing per season median surface reflectance. Seasonal images were integrated with context layers (e.g., height, aspect, slope, coastal proximity, urban proximity and so forth) to reduce confusion among classes with similar spectra.Land cover was validated by organising the pixel classification into a land parcel framework (the LCM2021 Classified Land Parcels product). The classified land parcels were compared to known land cover producing confusion matrix to determine overall and per class accuracy.View full metadata information and download the data at catalogue.ceh.ac.uk

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.

Audience: PublicExtent: UKUpdate Frequency: Every SundaySustrans is the custodian of the National Cycle Network (NCN). We work with our many partners and stakeholders to develop the Network across the UK. This view layer depicts more than 12,000 miles of signed paths and routes for walking, cycling, wheeling and exploring outdoors.While we receive updates from regional staff and volunteers on a continuous basis, we can't guarantee the data to be free of error. If you discover an error please inform us by emailing our Sustrans GIS team so that it can be corrected. About SustransSustrans is the charity making it easier for people to walk and cycle.Join us on our journey, Sustrans Website Layer VisibilityThis layer contains over 35,000 polyline features. To optimise drawing performance the view layer has been limited to Cities (1:160000) level. Known IssuesThe NCN view layer's native projection is British National Grid (EPSG: 27700). The Network in Northern Ireland was captured in WGS 84 and reprojected to British National Grid. This is essential for maintaining a complete dataset and for producing overall statistics about the network. For this reason, the public version is projected in WGS 84 / Pseudo-Mercator (EPSG: 3857). Attribute Information1. DescriptionTrafficFree: Cycle route closed to public motor vehicles such as a footway, cycle path or bridleway.Onroad : Cycle route open to and used by public motor vehicles2. Route type

NCN (National Cycle Network): Cycle route is signed by a number in a red box. RCN (Regional Cycle Network – network not maintained/updated by Sustrans): Cycle route is signed by a number in a blue boxLink (connects to NCN, but not part of a route): Cycle route is signed by a number enclosed in brackets (blue or red box).PROM: promoted routes, not part of the NCN, but links to NCN and forms part of national or regional routes e.g. John O’Groats to Lands’ End3. Route category (RouteCat)

Main routeAlternative routeTemporary diversion (where a route has been closed for works etc and an temporary alternative route has been designed)5. Quality

Smooth: Top quality asphalt, newly laid path, motorway standard. Standard: Average quality asphalt.Acceptable: Rough British country road or good quality unsealed surface.Rough: Would not normally be ridden on a road bike.MTB Only: A road bike definitely would not be a sensible vehicle for using this section (whether or not it is theoretically possible to cycle on this with enough skill).6. LightingFullLit: Route link is fully lit, no dark patches.PartLit: Route is part lit, a few dark patches.NotLit: Route is not lit.Additional information on surface type is available on request. Please email GISSupport@sustrans.org.uk if you require this.Access the data on our open data portal here.

Policy SCLP12.11 - Felixstowe Ferry and Golf Course from the Suffolk Coastal Local Plan, Adopted September 2020. The Suffolk Coastal Local Plan applies to the part of East Suffolk formerly covered by the Suffolk Coastal local planning authority area.This area should only be used as a guide. Please refer to the definitive policies map.

Policy SCLP12.11 - Felixstowe Ferry and Golf Course from the Suffolk Coastal Local Plan, Adopted September 2020. The Suffolk Coastal Local Plan applies to the part of East Suffolk formerly covered by the Suffolk Coastal local planning authority area.This area should only be used as a guide. Please refer to the definitive policies map.

The English Indices of Deprivation 2019 use 39 separate indicators, organised across seven distinct domains of deprivation which can be combined, using appropriate weights, to calculate the Index of Multiple Deprivation 2019 (IMD 2019). This is an overall measure of multiple deprivation experienced by people living in an area and is calculated for every Lower layer Super Output Area (LSOA) in England. The IMD 2019 can be used to rank every LSOA in England according to their relative level of deprivation.

Column

Full Column

LSOA01CD

LSOA code (2011)

LSOA01NM

LSOA name (2011)

LADcd

Local Authority District code (2019)

LADnm

Local Authority District name (2019)

IMDScore

Index of Multiple Deprivation (IMD) Score

IMDRank0

Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived)

IMDDec0

Index of Multiple Deprivation (IMD) Decile (where 1 is most deprived 10% of LSOAs)

IncScore

Income Score (rate)

IncRank

Income Rank (where 1 is most deprived)

IncDec

Income Decile (where 1 is most deprived 10% of LSOAs)

EmpScore

Employment Score (rate)

EmpRank

Employment Rank (where 1 is most deprived)

EmpDec

Employment Decile (where 1 is most deprived 10% of LSOAs)

EduScore

Education, Skills and Training Score

EduRank

Education, Skills and Training Rank (where 1 is most deprived)

EduDec

Education, Skills and Training Decile (where 1 is most deprived 10% of LSOAs)

HDDScore

Health Deprivation and Disability Score

HDDRank

Health Deprivation and Disability Rank (where 1 is most deprived)

HDDDec

Health Deprivation and Disability Decile (where 1 is most deprived 10% of LSOAs)

CriScore

Crime Score

CriRank

Crime Rank (where 1 is most deprived)

CriDec

Crime Decile (where 1 is most deprived 10% of LSOAs)

BHSScore

Barriers to Housing and Services Score

BHSRank

Barriers to Housing and Services Rank (where 1 is most deprived)

BHSDec

Barriers to Housing and Services Decile (where 1 is most deprived 10% of LSOAs)

EnvScore

Living Environment Score

EnvRank

Living Environment Rank (where 1 is most deprived)

EnvDec

Living Environment Decile (where 1 is most deprived 10% of LSOAs)

IDCScore

Income Deprivation Affecting Children Index (IDACI) Score (rate)

IDCRank

Income Deprivation Affecting Children Index (IDACI) Rank (where 1 is most deprived)

IDCDec

Income Deprivation Affecting Children Index (IDACI) Decile (where 1 is most deprived 10% of LSOAs)

IDOScore

Income Deprivation Affecting Older People (IDAOPI) Score (rate)

IDORank

Income Deprivation Affecting Older People (IDAOPI) Rank (where 1 is most deprived)

IDODec

Income Deprivation Affecting Older People (IDAOPI) Decile (where 1 is most deprived 10% of LSOAs)

CYPScore

Children and Young People Sub-domain Score

CYPRank

Children and Young People Sub-domain Rank (where 1 is most deprived)

CYPDec

Children and Young People Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

ASScore

Adult Skills Sub-domain Score

ASRank

Adult Skills Sub-domain Rank (where 1 is most deprived)

ASDec

Adult Skills Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

GBScore

Geographical Barriers Sub-domain Score

GBRank

Geographical Barriers Sub-domain Rank (where 1 is most deprived)

GBDec

Geographical Barriers Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

WBScore

Wider Barriers Sub-domain Score

WBRank

Wider Barriers Sub-domain Rank (where 1 is most deprived)

WBDec

Wider Barriers Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

IndScore

Indoors Sub-domain Score

IndRank

Indoors Sub-domain Rank (where 1 is most deprived)

IndDec

Indoors Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

OutScore

Outdoors Sub-domain Score

OutRank

Outdoors Sub-domain Rank (where 1 is most deprived)

OutDec

Outdoors Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

TotPop

Total population: mid 2015 (excluding prisoners)

DepChi

Dependent Children aged 0-15: mid 2015 (excluding prisoners)

Pop16_59

Population aged 16-59: mid 2015 (excluding prisoners)

Pop60+

Older population aged 60 and over: mid 2015 (excluding prisoners)

WorkPop

Working age population 18-59/64: for use with Employment Deprivation Domain (excluding prisoners)