Scotland Habitat and Land cover map - 2020
Habitat and land cover maps created using AI to classify satellite data to EUNIS level 2 by Space Intelligence in partnership with NatureScot. This work was a response to the Can Do Innovation fund challenge AI for Good -How can we use Artificial Intelligence (AI) techniques to tackle the climate emergency? This dataset contains the EUNIS classification level 2 of Scotland's land cover for the year 2020. It is part of a series of 3 layers (raster datasetsat~20m resolution). The other layer provides the land cover classificationfor the year 2019 and a third layer provides the land cover changes that occured between 2019 and 2020.
dataset
protocol: OGC:WMS-1.3.0-http-get-capabilities
name: 39
description: Scotland Habitat and Land cover map 2020
https://gateway.snh.gov.uk/natural-spaces/dataset.jsp?dsid=HLCM2020
protocol: WWW:LINK-1.0-http--link
name: Scottish Natural Heritage - Natural Spaces Download
description: Data Download page
function: download
https://cagmap.snh.gov.uk/natural-spaces/inspire_download.atom.xml
protocol: INSPIRE-ATOM
name: Scottish Natural Heritage - Inspire Atom feed
description: Inspire compliant atom feed
function: information
HLCM2020
eng
EPSG
27700
environment
GB-SCT
publication
2020-08-31
Habitats and biotopes
publication
2008-06-01
habitat
revision
2021-06-01
Downloadable Data
-9.230504
-0.704615
60.866277
54.513054
publication
2021-06-04
notPlanned
20 m resolution geotifs derived from Ground Data The ground data were collected through using a combination of the following sources, using a broad search that stretched beyond our Areas of Interest: ● Habitat Map of Scotland (ground polygons)1 ● 2018 National Forest Inventory2 ● Ordnance Survey3 ● Global Forest Change v1.64 ● High resolution imagery5 In all cases the ground data were not used naively: we used a careful combination of at least two data sources to create each polygon, and checking against recent high resolution imagery to ensure each polygon was ‘pure’ (i.e. included only one class) and up to date (for example, if it was a forest polygon, the trees had not been cleared since the data were collected). Satellite remote sensing datasets used for mapping Optical Sentinel 2 (S2) (30/03/2019-10/11/2019) Radar Sentinel-1, descending and ascending (01/01/2019-31/12/2019) ALOS-PALSAR 2, 2018 annual composite Topography Shuttle Radar Topography Mission (SRTM, 2000) Process Extensive training datasets, and derived features from remote sensing data, to implement a complex set of tuned machine learning algorithms to produce a Prediction Model, and ultimately a prediction of a class for each pixel. Through the project duration the sophistication of the models used increased, increasing accuracy and efficiency. For commercial reasons the details of the final algorithms used will not be revealed here.
publication
2010-12-08
false
Data Set Not Assessed
creation
2021-06-04
true
The overall accuracy of the maps against our input data was high (95.8%). The User’s Accuracy, a measure of how likely a pixel of a particular class in the map is to actually be that class, ranged from 86.4% for class H3 (inland cliffs), to 99.9% (screes), with most classes well over 90% accuracy. Commonly confused classes using remote sensing data, such as the four woodland classes (Deciduous, Coniferous, ‘Mixed’ and Small’ (patches and lines of disturbed woodland) were all classified with ~95% accuracy or higher. This suggests the classifier would be able, if repeated annually, to reliably detect changes between these classes, which is critical for the determination of the NCAI.
SDE Raster Dataset
10.7.1
Available under the Open Government Licence http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
Maps and data created by Space Intelligence with input and support from NatureScot , © SNH
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2022-08-19T10:39:20