Data4Land: How to enrich land-use/land-cover with historical vector(s)

Vitalii Kriukov; R. Rahman; Lucy Bastin

doi:10.1016/j.softx.2025.102226

Data4Land: How to enrich land-use/land-cover with historical vector(s)

Vitalii Kriukov^*
, R. Rahman
, Lucy Bastin

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

4 Downloads (Pure)

Abstract

Land-use/land-cover (LULC) datasets produced from remote sensing may not consistently capture small/linear features of interest, including ecological barriers, eg. roads, railways and water objects. To overcome this challenge, a flexible workflow was developed to enrich the LULC with vector data (OpenStreetMap and World Database on Protected Areas). Users apply the Data4Land tool through a series of Jupyter Notebooks that address different mandatory and optional functions. The tool was used to enrich example LULC datasets and compute habitat connectivity indices for Catalonia (Spain) and Northern England. If vector data represent ecological barriers, connectivity indices tend to drop at all scales once the enriched LULC datasets are implemented.

Original language	English
Article number	102226
Number of pages	9
Journal	SoftwareX
Volume	31
Early online date	17 Jul 2025
DOIs	https://doi.org/10.1016/j.softx.2025.102226
Publication status	Published - Sept 2025

Bibliographical note

Data Access Statement

This software is the part of the AD4GD project, biodiversity pilot. Zenodo DOI: https://doi.org/10.5281/zenodo.13991724 (Software URL). The output data produced by Data4Land with the sample input data is accessible on Zenodo: https://zenodo.org/records/15320266 (Dataset URL).

Funding

This software is the part of the AD4GD project, biodiversity pilot. The AD4GD project is supported by Innovate UK, United Kingdom (grant number 10041654), the European Union, and Switzerland.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

Spatial data
OpenStreetMap
VGI
Habitat connectivity
Land use
REST API

Access to Document

10.1016/j.softx.2025.102226Licence: CC BY 4.0

Kriukov Rahman and Bastin_Data4Land_How to enrich land-use land-cover w historical vector_s
Copyright © 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
Final published version, 2.88 MBLicence: CC BY 4.0

Cite this

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title = "Data4Land: How to enrich land-use/land-cover with historical vector(s)",

abstract = "Land-use/land-cover (LULC) datasets produced from remote sensing may not consistently capture small/linear features of interest, including ecological barriers, eg. roads, railways and water objects. To overcome this challenge, a flexible workflow was developed to enrich the LULC with vector data (OpenStreetMap and World Database on Protected Areas). Users apply the Data4Land tool through a series of Jupyter Notebooks that address different mandatory and optional functions. The tool was used to enrich example LULC datasets and compute habitat connectivity indices for Catalonia (Spain) and Northern England. If vector data represent ecological barriers, connectivity indices tend to drop at all scales once the enriched LULC datasets are implemented.",

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doi = "10.1016/j.softx.2025.102226",

language = "English",

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T1 - Data4Land: How to enrich land-use/land-cover with historical vector(s)

AU - Kriukov, Vitalii

AU - Rahman, R.

AU - Bastin, Lucy

PY - 2025/9

Y1 - 2025/9

N2 - Land-use/land-cover (LULC) datasets produced from remote sensing may not consistently capture small/linear features of interest, including ecological barriers, eg. roads, railways and water objects. To overcome this challenge, a flexible workflow was developed to enrich the LULC with vector data (OpenStreetMap and World Database on Protected Areas). Users apply the Data4Land tool through a series of Jupyter Notebooks that address different mandatory and optional functions. The tool was used to enrich example LULC datasets and compute habitat connectivity indices for Catalonia (Spain) and Northern England. If vector data represent ecological barriers, connectivity indices tend to drop at all scales once the enriched LULC datasets are implemented.

AB - Land-use/land-cover (LULC) datasets produced from remote sensing may not consistently capture small/linear features of interest, including ecological barriers, eg. roads, railways and water objects. To overcome this challenge, a flexible workflow was developed to enrich the LULC with vector data (OpenStreetMap and World Database on Protected Areas). Users apply the Data4Land tool through a series of Jupyter Notebooks that address different mandatory and optional functions. The tool was used to enrich example LULC datasets and compute habitat connectivity indices for Catalonia (Spain) and Northern England. If vector data represent ecological barriers, connectivity indices tend to drop at all scales once the enriched LULC datasets are implemented.

KW - Spatial data

KW - OpenStreetMap

KW - VGI

KW - Habitat connectivity

KW - Land use

KW - REST API

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UR - https://www.scopus.com/pages/publications/105010681793

U2 - 10.1016/j.softx.2025.102226

DO - 10.1016/j.softx.2025.102226

M3 - Article

SN - 2352-7110

VL - 31

JO - SoftwareX

JF - SoftwareX

M1 - 102226

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Data4Land: How to enrich land-use/land-cover with historical vector(s)

Abstract

Bibliographical note

Data Access Statement

Funding

UN SDGs

Keywords

Access to Document

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