Forest roads (Congo Basin)
Creators
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1.
Wageningen University & Research
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2.
The Nature Conservancy
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3.
Canadian Forest Service
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4.
Leibniz University Hannover
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5.
Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences
Description
Description
Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 5 years of road development (46,311 km) from 2019-2023.
The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:
- NetworkID: A unique ID for each connected road network.
- SegLenM: The length of the road segment (in meters).
- NetLenM: The length of the connected road network (in meters).
- Month: The road segment opening month.
- Year: The road segment opening year.
- MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.
Additional information
- More information about the forest road mapping project can be found at: https://wur.eu/forest-roads
- Continuously updated road maps can be interactively viewed at: https://nrtwur.users.earthengine.app/view/forest-roads
- The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12')
- The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380
Citation
Please cite the following when referring to this dataset:
Slagter B., Fesenmyer K., Hethcoat M., Belair E., Ellis P., Kleinschroth F., Peña-Claros M., Herold M., Reiche J. (2024). Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning. Remote Sensing of Environment
Files
forestroads_afr_2019-01_2023-12.zip
Files
(30.6 MB)
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Additional details
References
- B. Slagter, K. Fesenmyer, M. Hethcoat, E. Belair, P. Ellis, F. Kleinschroth, M. Peña-Claros, M. Herold, J. Reiche (2024) Monitoring road development in Congo Basin forests with multi-sensor satellite imagery and deep learning, Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2024.114380