The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019. Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar. The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are: - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM) - Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed. Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range. This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

IMPORTANT INFORMATION: This version (V3) of the dataset is based on aerial imagery from 2024 (month depends on department) and cadastral data from January 2025. It features: updates for 27 departments: 01, 02, 04, 11, 17, 23, 24, 29, 30, 33, 34, 38, 40, 47, 56, 60, 62, 64, 66, 67, 68, 73, 80, 84, 87, 2A, 2B 7 new departments: 77, 78, 91, 92, 93, 94, 95 Data for other departments is not reuploaded in this version of the dataset. Please use the version selector and goes to V2 to access older data for these departments. This dataset contains images of the rooftops of French buildings, with a large portion of the images from metropolitan France available. Ultimately, it will include around 40,000,000 images, organized by department. This dataset is related to the scientific publication "Thebault, Nerot, Govehovitch, Ménézo - A comprehensive building-wise Residential Photovoltaic system detection in heterogeneous urban and rural areas: application to French territories" Applied Energy, 2025, doi.org/10.1016/j.apenergy.2025.125630 Aerial Land Imagery The aerial imagery used in this study comes from the Institut National de l'Information Géographique et Forestière (IGN), the French national geographic institute. These images are provided in 25 km² RGB tiles with a resolution of 20 cm. The tiles are organized by French department and are freely accessible as JP2 raster files BD Ortho - Institut National Géographique. All the imagery utilized in this project is less than four years old. The availability of department-level imagery is fundamental to our methodology, as both cadastral data processing and the analysis of CNN model predictions are performed at this geographic scale. Building Registry The location and geometry of each building were extracted from a national building registry distributed by the French Etalab project. For each French department, a single SHP file is provided, containing building geometries stored as polygon features. Data Post-Processing Both raster (aerial imagery) and vector (building registry) data were processed using PyQGIS via QGIS. The preprocessing of vector data follows several steps. First, polygons with an area smaller than 10 m² were discarded, as they typically represent small, likely non-residential buildings, which are unlikely to host PV panels. Next, a 4-meter buffer was applied to each polygon to account for the frequent spatial discrepancies between the building registry and the actual building locations. To include additional contextual information in each final image and accommodate these shifts, each polygon was replaced with its oriented rectangular bounding box, minimizing the area of the box. Finally, the X and Y coordinates and a department-based unique identifier were added to each polygon feature. Creation of Building Images Each building polygon was intersected with the corresponding aerial imagery raster to generate a cropped image. These images were saved to individual files. For example, the Herault department (34), one of the more populated regions of France, contains approximately 700,000 images, with an average image size of 120x120 pixels. Notably, 97.9% of these images are smaller than 250x250 pixels. Approximately 1.5% of buildings span multiple raster tiles, resulting in final images that do not fully capture the entire rooftop. Ce jeu de donnée contient les images des toitures des batiments Française, une grande partie des images du territoire métropolitain Français sont disponible. A terme il contiendra environ 40 000 000 images, organisées par départements. Imagerie aérienne L’imagerie aérienne utilisée dans cette étude provient de l’Institut National de l’Information Géographique et Forestière (IGN), l’institut géographique national français. Ces images sont fournies sous forme de tuiles RGB de 25 km² avec une résolution de 20 cm. Les tuiles sont organisées par département français et sont accessibles gratuitement en tant que fichiers raster JP2 (BD Ortho - Institut National Géographique). Toutes les images utilisées dans ce projet ont moins de quatre ans. La disponibilité d’images aériennes à l’échelle départementale est fondamentale pour notre méthodologie, car à la fois le traitement des données cadastrales et l’analyse des prédictions du modèle CNN sont effectués à cette échelle géographique. Registre des bâtiments La localisation et la géométrie de chaque bâtiment ont été extraites d’un registre national des bâtiments distribué par le projet Etalab en France. Pour chaque département français, un fichier SHP unique est fourni, contenant les géométries des bâtiments sous forme de polygones. Post-traitement des données Les fichiers raster (imagerie aérienne) et vectoriels (registre des bâtiments) ont été traités avec PyQGIS via QGIS. Le prétraitement des données vectorielles suit plusieurs étapes. Tout d’abord, les polygones ayant une surface inférieure à 10 m² ont été exclus, car ils représentent généralement des petits bâtiments,...

The data found in this repository contain the basis for the historical, architectural, and geo-spatial analyses discussed in the dissertation entitled: CITADEL – Computation Investigation of the Topographical and Architectural Designs in an Evolving Landscape. These data include the following categories. 1. Photogrammetric Data: all photos, calibration information, and Agisoft Metashape projects for the four sites. All post-processed 3D models of the photogrammetric process and their associated perspectives from which orthophotos were generated for the construction research. 2. Laserscan Data: all raw data and calibration information pertaining the four sites as recorded by the Riegl VZ-400 laser scanner, and all post-processed 3D models of the sites. 3. GIS Data: all historical maps that were geo-referenced in the project, the entire QGIS project file with all associated layers, all raster and vector data saved as individual files, and all shapefiles saved as individual files. 4. Graph Database: all spreadsheets containing the base information drawn from the charters provided by online and analog sources. The entire Cypher Script as well as instruction for importing the data into Neo4j. The rubric outlining how the status and administration positions of the individuals in the charters were ranked relative to one another. The cognitive development of the database’s structure represented by graph schemas over time. 5. Architectural Plans: the roombook outlining every wall, architectural element, and building phase of the four sites. All 76 architectural plans of the construction research using orthophotos of the photogrammetric models.

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2023. It complements a series of maps that are produced annually at the Thünen Institute beginning in 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and, e.g., uncultivated areas. The generation of the maps involved four steps. Firstly, a raster map was derived from a time series of Sentinel-1, Sentinel-2, Landsat 8, and additional environmental data following the approach of Blickensdörfer et al. (2022). Secondly, agricultural field boundaries were generated based on monthly composites of Sentinel-2 and Landsat 8/9 images using the approach of Waldner et al. (2021). Thirdly, the agricultural fields were spatially overlaid on the raster map, and the class per field was assigned via majority voting. Finally, all the fields were simplified to remove redundant vertices while preserving essential shape. All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). Version v201: Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. References: Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Waldner, F., Diakogiannis, F.I., Batchelor, K., Ciccotosto-Camp, M., Cooper-Williams, E., Herrmann, C., Mata, G., & Toovey, A. (2021). Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images. Remote Sensing, 13, 2197. _ National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

Reason for SelectionProtected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. Because beaches in Puerto Rico and the U.S. Virgin Islands are open to the public, beaches also provide important outdoor recreation opportunities for urban residents, so we include beaches as parks in this indicator.Input DataSoutheast Blueprint 2023 subregions: CaribbeanSoutheast Blueprint 2023 extentNational Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) Coastal Relief Model, accessed 11-22-2022Protected Areas Database of the United States (PAD-US) 3.0: VI, PR, and Marine Combined Fee EasementPuerto Rico Protected Natural Areas 2018 (December 2018 update): Terrestrial and marine protected areas (PACAT2018_areas_protegidasPR_TERRESTRES_07052019.shp, PACAT2018_areas_protegidasPR_MARINAS_07052019.shp) 2020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 3-14-2023A polygon from this dataset is considered a park if the “leisure” tag attribute is either “park” or “nature_reserve”, and considered a beach if the value in the “natural” tag attribute is “beach”. OpenStreetMap describes leisure areas as “places people go in their spare time” and natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format and translated ton an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page. TNC Lands - Public Layer, accessed 3-8-2023U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact jdwyer@lynker.com for more information)Mapping StepsMost mapping steps were completed using QGIS (v 3.22) Graphical Modeler.Fix geometry errors in the PAD-US PR data using Fix Geometry. This must be done before any analysis is possible.Merge the terrestrial PR and VI PAD-US layers.Use the NOAA coastal relief model to restrict marine parks (marine polygons from PAD-US and Puerto Rico Protected Natural Areas) to areas shallower than 10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature.Merge into one layer the resulting shallow marine parks from marine PAD-US and the Puerto Rico Protected Natural Areas along with the combined terrestrial PAD-US parks, OpenStreetMap, TNC Lands, and USVI beaches. Omit from the Puerto Rico Protected Areas layer the “Zona de Conservación del Carso”, which has some policy protections and conservation incentives but is not formally protected.Fix geometry errors in the resulting merged layer using Fix Geometry.Intersect the resulting fixed file with the Caribbean Blueprint subregion.Process all multipart polygons to single parts (referred to in Arc software as an “explode”). This helps the indicator capture, as much as possible, the discrete units of a protected area that serve urban residents.Clip the Census urban area to the Caribbean Blueprint subregion.Select all polygons that intersect the Census urban extent within 1.2 miles (1,931 m). The 1.2 mi threshold is consistent with the average walking trip on a summer day (U.S. DOT 2002) used to define the walking distance threshold used in the greenways and trails indicator. Note: this is further than the 0.5 mi distance used in the continental version of the indicator. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used to join the parks to their buffers.Create a 1.2 mi (1,931 m) buffer ring around each park using the multiring buffer plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 1.2 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using overlap analysis. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix. This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤2% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: In the continental version of this indicator, we used a threshold of 10%. In the Caribbean version, we lowered this to 2% in order to capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Join the buffer attribute table to the previously selected parks, retaining only the parks that exceeded the 2% urban area overlap threshold while buffered. Buffer the selected parks by 15 m. Buffering prevents very small parks and narrow beaches from being left out of the indicator when the polygons are converted to raster.Reclassify the polygons into 7 classes, seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Polygon to Raster function. Assign values to the pixels in the resulting raster based on the polygon class sizes of the contiguous park areas.Clip to the Caribbean Blueprint 2023 subregion.As a final step, clip to the spatial extent of Southeast Blueprint 2023. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:6 = 75+ acre urban park5 = >50 to <75 acre urban park4 = 30 to <50 acre urban park3 = 10 to <30 acre urban park2 = 5 to <10 acre urban park1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources. This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.This indicator includes parks and beaches from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a park) or incorrect tags (e.g., labelling an area as a park that is not actually a park). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future.Other Things to Keep in MindThis indicator calculates the area of each park using the park polygons from the source data. However, simply converting those park polygons to raster results in some small parks and narrow beaches being left out of the indicator. To capture those areas, we buffered parks and beaches by 15 m and applied the original area calculation to the larger buffered polygon, so as not to inflate the area by including the buffer. As a result, when the buffered polygons are rasterized, the final indicator has some areas of adjacent pixels that receive different scores. While these pixels may appear to be part of one contiguous park or suite of parks, they are scored differently because the park polygons themselves are not actually contiguous. The Caribbean version of this indicator uses a slightly different methodology than the continental Southeast version. It includes parks within a 1.2 mi distance from the Census urban area, compared to 0.5 mi in the continental Southeast. We extended it to capture East Bay and Point Udall based on feedback from the local conservation community about the importance of the park for outdoor recreation. Similarly, this indicator uses a 2% threshold of overlap between buffered parks and the Census urban areas, compared to a 10% threshold in the continental Southeast. This helped capture small parks that dropped out of the indicator when we extended the buffer distance to 1.2 miles. Finally, the Caribbean version does not use the impervious surface cutoff applied in the continental Southeast because the landcover data available in the Caribbean does not assess percent impervious in a comparable way.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint

The Global River Topology (GRIT) is a vector-based, global river network that not only represents the tributary components of the global drainage network but also the distributary ones, including multi-thread rivers, canals and delta distributaries. It is also the first global hydrography (excl. Antarctica and Greenland) produced at 30m raster resolution. It is created by merging Landsat-based river mask (GRWL) with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask (rivers narrower than approx. 30m). Crucially, it uses a new 30m digital terrain model (FABDEM, based on TanDEM-X) that shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a combination of elevation, flow angle, heuristic and continuity approaches (based on RivGraph). The network topology (lines and nodes, upstream/downstream IDs) is available as layers and attribute information in the GeoPackage files (readable by QGIS/ArcMap/GDAL).

A map of GRIT segments labelled with OSM river names is available here: Map with names

Report bugs and feedback

Your feedback and bug reports are welcome here: GRIT bug report form

The feedback may be used to improve and validate GRIT in future versions.

Regions

Vector files are provided in 7 regions with the following codes:

• AF - Africa
• AS - Asia (excl. Siberia)
• EU - Europe
• NA - North America
• SA - South America
• SI - Siberia
• SP - South Pacific/Australia

The domain polygons (GRITv06_domain_GLOBAL.gpkg.zip) provide 60 subcontinental catchment groups that are available as vector attributes. They allow for more fine-grained subsetting of data (e.g. with ogr2ogr --where and the domain attribute).

Vector files are provided both in the original equal-area Equal Earth Greenwich projection (EPSG:8857) as well as in geographic WGS84 coordinates (EPSG:4326).

Change log

• v0.6 - 2024-05-30
  • Rivers/streams outside of the GRWL mask forced by all OSM water lines (not only those with waterway=river/canal)
  • Some manual directions in the Irrawaddy delta and fixed erronous sink in the Volga delta
• v0.5 - 2024-02-14
  • Cyclicity and discontinuities resolved through improved algorithms, bug fixes, more sophisticated cycle solving algorithms and some manually forced directions. Only insignificant cycles (non-sinks, less than 50) were removed.
  • Added segment and reach attributes
  • Computational domain fixes
  • Segments include OSM river names
  • Asia domain split into Siberia and rest of Asia
  • Vector files available in EPSG:8857 and EPSG:4326
• v0.4 - 2023-03-11
  
  • First globally complete dataset published

Network segments

Lines between inlet, outlet, confluence and bifurcation nodes. Files have lines and nodes layers.

Attribute description of lines layer

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global river segment ID, same as FID
catchment_id	integer	global catchment ID
upstream_node_id	integer	global segment node ID at upstream end of line
downstream_node_id	integer	global segment node ID at downstream end of line
upstream_line_ids	text	comma-separated list of global river segment IDs connecting at upstream end of line
downstream_line_ids	text	comma-separated list of global river segment IDs connecting at downstream end of line
direction_algorithm	float	code of RivGraph method used to set the direction of line
width_adjusted	float	median river width in m without accounting for width of segments connecting upstream/downstream
length_adjusted	float	segment length in m without accounting for width of segments connecting upstream/downstream in m
is_mainstem	integer	1 if widest segment of bifurcated flow or no bifurcation upstream, otherwise 0
strahler_order	integer	Strahler order of segment, can be used to route in topological order
length	float	segment length in m
azimuth	float	direction of line connecting upstream-downstream nodes in degrees from North
sinuousity	float	ratio of Euclidean distance between upstream-downstream nodes and line length, i.e. 1 meaning a perfectly straight line
drainage_area_in	float	drainage area at beginning of segment, partitioned by width at bifurcations, in km2
drainage_area_out	float	drainage area at end of segment, partitioned by width at bifurcations, in km2
drainage_area_mainstem_in	float	drainage area at beginning of segment, following the mainstem, in km2
drainage_area_mainstem_out	float	drainage area at end of segment, following the mainstem, in km2
bifurcation_balance_out	float	(drainage_area_out - drainage_area_mainstem_out) / max(drainage_area_out, drainage_area_mainstem_out), dimensionless ratio
grwl_overlap	float	fraction of the segment overlapping with the GRWL river mask
grwl_value	integer	dominant GRWL value of segment
name	text	river name from Openstreetmap where available, English preferred
name_local	text	river name from Openstreetmap where available, local name
n_bifurcations_upstream	integer	number of bifurcations upstream of segment
domain	text	catchment group ID, see domain index file

Attribute description of nodes layer

Name	Data type	Description
cat	integer	domain internal feature ID
global_id	integer	global river node ID, same as FID
catchment_id	integer	global catchment ID
upstream_line_ids	text	comma-separated list of global river segment IDs flowing into node
downstream_line_ids	text	comma-separated list of global river segment IDs flowing out of node
node_type	text	description of node, one of bifurcation, confluence, inlet, coastal_outlet, sink_outlet, grwl_change
grwl_value	integer	GRWL code at node
grwl_transition	text	GRWL codes of change at grwl_change nodes
cycle	integer	>0 if segment is part of an unresolved cycle, 0 otherwise
continuity_violated	integer	1 if flow continuity is violated, otherwise 0
drainage_area	float	drainage area, partitioned by width at bifurcations, in km2
drainage_area_mainstem	float	drainage area, following the mainstem, in km2
n_bifurcations_upstream	integer	number of bifurcations upstream of node
domain	text	catchment group, see domain index file

Network reaches

Segment lines split to not exceed 1km in length, i.e. these lines will be shorter than 1km and longer than 500m unless the segment is shorter. A simplified version with no vertices between nodes is also provided. Files have lines and nodes layers.

Attribute description of lines layer

Name	Data type	Description
cat	integer	domain internal feature ID
segment_id	integer	global segment ID of reach
global_id	integer	global river reach ID, same as FID
catchment_id	integer	global catchment ID
upstream_node_id	integer	global reach node ID at upstream end of line
downstream_node_id	integer	global reach node ID at downstream end of line
upstream_line_ids	text	comma-separated list of global river reach IDs connecting at upstream end of line
downstream_line_ids	text	comma-separated list of global river reach IDs connecting at downstream end of line
grwl_overlap	float	fraction of the reach overlapping with the GRWL river mask
grwl_value	integer	dominant GRWL value of node
grwl_width_median	float	median width of the

**Departmental nocturnal radiance maps from an assembly of LuoJia 1-01 satellite shots on France, operated only in 2018. ** The 80 departments of metropolitan France that have been covered by cloud-free acquisitions are available in this delivery. These data can be mobilized in the context of reflections on light pollution or the establishment of black frames. The documentation contains a concise instructions for use (FAQ) and a complete production report with examples of use. https://i.imgur.com/TwDD1AJ.png%22carte_france_entiere%22" alt="Preview available data"> An online map visualisation of the data is available on the Cartagene du Cerema portal. The delivery is in the form of a compressed folder (.zip) by department (region for Ile-de-France), containing raster mapping (.tif), a style file for display on the QGIS software (.qml), a vector file indicating the dates and times of acquisition (.shp), and metadata (.xml).