Bibliographic record
Abstract
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 7 years of road development (68,186 km) from 2019-2025. 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 (temporarily offline): 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_2025-12') 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 315, 114380.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".