The atlas contains the location of UK rivers at two scales, a course scale, suitable for viewing at national and regional scales. As you zoom in to a local scale the river lines will change to show a more accurate representation of watercourses.For all drainage basins the pop-up will show

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.

This dataset is wholly owned by the Royal Yachting Association (RYA). The Royal Yachting Association (RYA) UK Coastal Atlas of Recreational Boating is a GIS dataset of recreational boating activity around the UK, comprising spatial data including indicators of intensity of use, general boating areas, offshore routes, as well as the locations of clubs, training centres and marinas

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.

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.

This dataset shows captured water courses within the national forest estate. These will be natural water courses which will usually be permanent.

About

UKBORDERS provides digitised boundary datasets of the UK, available in many Geographic Information System (GIS) formats (MapInfo MIF/MID, ArcView Shape, Arc/Info Export and several others), for teachers and researchers in the UK Higher and Further Education community to download and use in their work.

Re-use

Available for re-use in UK HE/FE.

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.

OS Open Rivers is a generalised open water network showing the flow and the locations of rivers, streams, lakes and canals across the whole of Great Britain. The new product is part of the OS Open suite and is designed to be used with other OpenData product sets. It’s mapping that can help you question, visualise and share results quickly and clearly. With OS Open Rivers you can: Understand the water network at a ‘high level’ with generalised geometry and network connectivity.View a network of main rivers, identifying the main river course along its full length.Pin information on the connected network for personal or business use. Take an informed overview of a situation along the network to manage it strategically. Compare and monitor stretches of water. Share information, such as flood alerts and flood risk areas.The currency of this data is 04/2025 The coverage of the map service is GB. The map projection is British National Grid.