Congo Basin forest roads

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Congo **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0686 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0037 million km (5.3823%) - Unpaved roads: 0.011 million km (15.9971%) About 0.0539 million km (78.6206%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0001 million km (0.1918%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

Logging concessions refers to areas allocated by a government for harvesting timber and other wood products in a public forest. Logging concessions are distinct from wood fiber concessions, where tree plantations are established for the exclusive production of pulp and paper products. “Concession” is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber and other wood products from public forests; terminology varies at the national level, however, and includes "forest permits," "tenures," "licenses," and other terms. This data set displays logging concessions as a single layer assembled by aggregating data for multiple countries. The data may come from government agencies, NGOs, or other organizations and varies by date and data sources. Only active or available concessions are included in this layer. Inactive or expired concessions are excluded as defined by the dataset source. As available: additional data source information is linked below: - [Cambodia]() - Cameroon - Democratic Republic of the Congo - Gabon - Liberia - Suriname If you are aware of concession data for additional countries, please email us [here](mailto:gnw@wri.org). Displays boundaries of forested areas allocated by governments to companies for harvesting timber and other wood products.

This dataset, assembled by Global Nature Watch, aggregates deforestation alerts from three alert systems (GLAD-L, GLAD-S2, RADD) into a single, integrated deforestation alert layer. This integration allows users to detect deforestation events faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated. The source alert systems are derived from satellites of varying spectral and spatial resolutions. 30 m GLAD Landsat-based alerts are up-sampled to match the 10 m spatial resolution of Sentinel-based alerts (GLAD-S2, RADD). This avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels. Alerts are classified as high confidence when detected twice by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or three) of these systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system. A study conducted by Wageningen University in collaboration with researchers from Global Nature Watch and University of Maryland's GLAD lab found that integrating alert systems results in faster detection of new disturbances by days to months, and also shortens the delay to increase confidence. Combined alerts have a higher producer's accuracy (fewer false negatives), but a lower user's accuracy (more false positives) since the commission errors from each system are combined; however, 'highest confidence' alerts, where more than one system detected the change, effectively eliminated false detections. Learn more: https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82 The integrated deforestation alerts are available on **Google Earth Engine** with asset ID: projects/forma-250/assets/gfw_integrated_alerts/default_latest **Download** the raster tiles here: https://data.globalforestwatch.org/datasets/gfw::integrated-deforestation-alerts/about Monitor forest disturbance in near-real-time using integrated alerts from three alerting systems

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Democratic Republic of the Congo **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.6182 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0114 million km (1.8449%) - Unpaved roads: 0.2246 million km (36.3305%) About 0.3822 million km (61.8246%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0011 million km (0.2792%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

This dataset enhances pantropical near-real-time forest disturbance monitoring systems by using a deep learning model to classify direct drivers of forest disturbances on a monthly basis. Produced by Wageningen University and Research (WUR), Laboratory of Geo-information Science and Remote Sensing, with support from Global Nature Watch (GNW), the dataset is built upon GNW's integrated disturbance alerts, which contains WUR's RADD (Radar for Detecting Deforestation) alerts and University of Maryland's GLAD (Global Land Analysis & Discovery) alerts, harnessing post-disturbance imagery from Sentinel-1 and Sentinel-2 satellites. Only high and highest confidence alerts are classified. This approach enables the identification of these direct causes driving deforestation: small-scale agriculture, large-scale agriculture, road development, selective logging, mining, wildfire, flooding, and other natural disturbances. The alert drivers offer more targeted enforcement of laws and regulations, improved estimations of ecological impacts, and better understanding of carbon emissions related to forest disturbances. Read the paper here: Rapid satellite monitoring reveals complexity of tropical tree cover loss drivers at fine scale | Research Square **Alert Driver class definitions** **Non-natural drivers (human-caused)** **Small-scale agriculture**: Clearings smaller than 2 ha, commonly related to shifting cultivation (temporary clearing for cultivation with later regrowth) or smallholder farming. In smallholder landscapes with mixed disturbances, this may include artisanal logging and fuelwood collection (typically burned for cooking).  **Small-scale agriculture with fire**: Clearings for small-scale agriculture (see above) where fire was likely used for the clearing, observed by a coinciding VIIRS fire alert and low post-disturbance Sentinel-2 Normalized Burn Ratio **Large-scale agriculture**: Clearings larger than 2 ha for the establishment of crops or pastures, commonly related to industrial agriculture (e.g. production of soy, palm oil, beef, etc.), clearcuts, and large-scale clearings for land speculation. **Large-scale agriculture with fire**: Clearings for large-scale agriculture (see above) where fire was likely used for the clearing, observed by a coinciding VIIRS fire alert and low post-disturbance Sentinel-2 Normalized Burn Ratio **Road development**: Clearings for the establishment of roads, commonly related to facilitating industrial timber harvests, but can include roads for any purpose.  **Selective logging**: Small-scale disturbances caused by selective tree felling and skidding (paths where felled logs are dragged or transported), commonly related to industrial timber harvests.  **Mining**: Forest clearing to facilitate artisanal and industrial mineral extraction. **Natural drivers** **Flooding**: Disturbances or clearings caused by floodings and meandering rivers. This includes both natural and human-induced flooding. **Other natural disturbances**: Disturbances or clearings without visible human-induced cause. This includes windthrows, droughts, landslides and naturally dying trees. **Other drivers** **Wildfire**: Large-scale disturbances due to fire, without immediate land clearing for agricultural activity. This includes both human-induced and naturally induced fires. This class excludes controlled fires used for agricultural clearing, but includes escaped wildfires caused by controlled fires. **Unlabeled**: Pixels where a confidence threshold for a prediction is not reached. The dataset covers the primary humid tropical forest (Turubanova et al. 2018) of the Amazon (Bolivia, Brazil, Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname and Venezuela, restricted to the biogeographic outlines of the Amazon biome as defined by RAISG), Congo Basin (Cameroon, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Republic of the Congo) and Southeast Asia (Brunei, East- Timor, Indonesia, Malaysia and Papua New Guinea). The model was trained on a dataset of labeled disturbance alerts, interpreted with high-resolution Planet imagery across the Amazon, Congo Basin, and Southeast Asia. The model integrates the complementary strengths of radar (Sentinel-1) and optical (Sentinel-2) imagery, leveraging convolutional neural networks (CNNs) to classify disturbances. Even though the dataset initially displayed historical alert data (2022-2025), the classifications were applied in a simulated near-real-time monitoring scenario to classify monthly disturbance alerts into one of the specified classes, achieving an overall accuracy of 0.89 Macro-F1. Accuracy varies by region, with the Congo Basin performing best. After the addition of the DIST-ALERT product, there are more alerts and therefore, areas of misclassification may be larger. Most misclassifications stem from confusion between agriculture and mining, which often co-occur, and between flooding and wildfires. Historical drivers (2022-2025) were classified up to three times within the three months following each alert, with the most confident result retained. These 2022-2025 classifications were postprocessed by selecting per disturbance patch the most occurring driver class, leading to some initial classifications being overwritten by another class in postprocessing. The current method, running from January 2026 onwards, selects the most occurring driver class only for disturbance patches within the three-month windows, As a result, an alert can be updated up to 3 times, and the latest classification is retained. Classifications may therefore change for up to 4 months after the initial alert and do not change after that. . A class ‘unconfident’ is built from pixels that do not exceed a 0.5 class probability from the classification output. When the most occurring class for a disturbance patch is ‘unconfident’, this alerts remains unlabeled. Post-processing steps identified where fire coincided with the clearing of agricultural land, based on the presence of VIIRS fire alerts (within a 500 m buffer around the alert and a 6-month window) and a low post-disturbance normalized-burn ratio (lower than 0.0) in the following month’s Sentinen-2 composite. Smoothing was applied to all classes except road development and selective logging, which occur at finer scales. For patches smaller than 5 km, the dominant driver was assigned. For larger patches in the 2022-2025 data, pixel-level classifications were retained but smoothed with a 5 × 5-pixel majority filter. This step was applied to the whole time series; therefore, the historical product has an advantage in differentiation between small- and large-scale agriculture compared to the operational product. In a near-real-time setting, large clearings may initially be labeled as small-scale until they exceed the 2 ha threshold. In the future, monthly driver attributions for disturbance alerts will expand to new regions. The driver attributions will be continuously mapped and openly distributed via Global Nature Watch, as an extension to the integrated disturbance alerts. We also provide an annual version of this dataset, which incorporates some post processing – specifically the differentiation between large-scale and small-scale agriculture is made at the end of the year, meaning that agricultural clearings which are identified incrementally are identified based on their size at the end of the year. See the integrated disturbance alert layer metadata for additional details about the disturbance alerts. The alert drivers are available on Google Earth Engine with asset IDs: **Latest data** (ee.ImageCollection containing data from January 1, 2026 – end of last month): - projects/wurnrt-drivers/assets/operational_postprocessed_sa_collection - projects/wurnrt-drivers/assets/operational_postprocessed_afr_collection - projects/wurnrt-drivers/assets/operational_postprocessed_sea_collection *Note: this collection updates monthly, so new images will be added over time, but the collection path itself will stay the same.* **Historical archive** (ee.Image containing data from 2022–2025): - projects/wurnrt-drivers/assets/distribution/driverclassification_afr_202201_202512 - projects/wurnrt-drivers/assets/distribution/driverclassification_sa_202201_202512 - projects/wurnrt-drivers/assets/distribution/driverclassification_sea_202201_202512 Assigns probable drivers to the integrated disturbance alerts using machine learning and Sentinel imagery

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name Gabon **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0701 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0046 million km (6.5028%) - Unpaved roads: 0.0186 million km (26.4836%) About 0.047 million km (67.0136%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (0.0057%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, and GIS applications. AI Features - **pred_class**: Model-predicted surface class ("paved" or "unpaved") - **pred_label**: Binary label (0 = paved, 1 = unpaved) - **osm_surface_class**: Surface classification derived from OSM tags - **combined_surface_osm_priority**: Combined classification prioritizing OSM - **combined_surface_DL_priority**: Combined classification prioritizing DL prediction - **n_of_predictions_used**: Number of predictions used for estimation - **predicted_length**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used OSM Attributes - **name** - **name:en** - **name:* (local language)** - **highway** - **surface** - **smoothness** - **width** - **lanes** - **oneway** - **bridge** - **layer** - **source** Urban Classification Attributes - **continent** - **country_iso_a2** - **urban** (0 = rural, 1 = urban) - **urban_area** - **osm_id** - **osm_type** Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org