Electronic maps (E-maps) provide people with convenience in real-world space. Although web map services can display maps on screens, a more important function is their ability to access geographical features. An E-map that is based on raster tiles is inferior to vector tiles in terms of interactive ability because vector maps provide a convenient and effective method to access and manipulate web map features. However, the critical issue regarding rendering tiled vector maps is that geographical features that are rendered in the form of map symbols via vector tiles may cause visual discontinuities, such as graphic conflicts and losses of data around the borders of tiles, which likely represent the main obstacles to exploring vector map tiles on the web. This paper proposes a tiled vector data model for geographical features in symbolized maps that considers the relationships among geographical features, symbol representations and map renderings. This model presents a method to tailor geographical features in terms of map symbols and ‘addition’ (join) operations on the following two levels: geographical features and map features. Thus, these maps can resolve the visual discontinuity problem based on the proposed model without weakening the interactivity of vector maps. The proposed model is validated by two map data sets, and the results demonstrate that the rendered (symbolized) web maps present smooth visual continuity.

Building View, vector tiles is a viewing service consisting of Lantmäteriet’s most detailed topographical data for buildings and construction areas. The format, vector tiles, provides different possibilities for use than view services in raster format. The product allows you to connect information from the map with our direct access services. In addition, custom style files can be defined for a separate look on the map. The information is displayed at scale 1: 10 000.

Topography Display, vector tiles is a display service consisting of Lantmäteriet’s most detailed topographical data. It contains, among other things, buildings, land laws, roads, watercourses, regulations and city names. The vector tiles format provides other possibilities for use than display services in raster format. The product provides the opportunity to connect information from the map with our direct access services. In addition, custom style files can be defined for a custom look on the map

The NAIP Imagery Hybrid (US Edition) web map features recent high-resolution National Agriculture Imagery Program (NAIP) imagery for the United States and is optimized for display quality and performance. The map also includes a reference layer. This NAIP imagery is from the USDA Farm Services Agency. The NAIP imagery in this map has been visually enhanced and published as a raster tile layer for optimal display performance.NAIP imagery collection occurs on an annual basis during the agricultural growing season in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection.This basemap is available in the United States Vector Basemaps gallery and uses NAIP Imagery and World Imagery (Firefly) raster tile layers. It also uses the Hybrid Reference (US Edition) and Dark Gray Base (US Edition) vector tile layers.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

This dataset is a derivative of the WSF3D raster dataset tailored for the web. As a tiled vector dataset, it enables dynamic client-side visualization of the WSF3D metrics

These tiles are raster images of the Topo vector data from Corax Topo 2006 vector dataset, created using ArcGIS in the NZTM projection. They are not the scanned hardcopy maps published by LINZ. All tiles are georeferenced and clipped exactly. Annotation can span the tile boundaries.

The scale of the data is designed to be printed at 1:50,000 but the lower resolutions we have for screens compared to a page means that they are more readable at 1:25,000 scale.

To reduce clutter the patterned filled symbols have been simplified to single colours, contours have been thinned.

Annotation from the topo set has been used for labelling, however that did not include road names, spot heights and descriptive text. These have been placed automatically, so there are some clashes.

Pixel size is 4.0 metres ground which results in much smaller file sizes (~ 40 MB) than a high resolution version of an A1 sheet that LINZ issue from the new series (140 MB)

The tiles are half NZTopo50 sheets, 24 km x 18 km covering 43,200 Ha. The rate set is equivalent to $3.00 + GST per tile or $6 + GST per sheet for large areas. You may find downloading the NZTopo50 index layer to make calculation of the cost easier.

Although there are newer versions of the LINZ data (now v 15) there are very, very few edits in each version, and they are limited to a few tiles, so for most of the country this is still identical to the latest version.

Source Corax Topo 2006, derived from LINZ Topo version 13. Crown Copyright Reserved.

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 Flood Inundation Mapping (FIM) Visualization Deck is a web-based application designed to display and compare flood extent and depth information across various temporal and scenario conditions. It provides a front-end interface for accessing geospatial flood data and interacting with mapped outputs generated from hydraulic modeling.

Core Functions: • Flood Extent Mapping: Visualizes flood extents from modeled scenarios (e.g., 2-year, 10-year, 100-year events) and real-time conditions based on streamflow observations or forecasts. • Flood Depth Visualization: Displays depth rasters over affected areas, derived from hydraulic simulations (e.g., HEC-RAS). • Scenario Comparison: Allows side-by-side viewing of multiple FIM outputs to support calibration or decision analysis. • Layer Management Toolbox: Users can toggle basemaps, adjust layer transparency, load datasets, and control map extents.

Data Inputs: • Precomputed flood inundation extents (raster/tile layers) • Depth grids • Stream gauge metadata • Associated hydraulic model outputs

Technical Stack: • Front-end: Built with JavaScript, primarily using Leaflet.js for interactive map rendering. • Back-end Services: Uses GeoServer to serve raster tiles and vector layers (via WMS/WFS). Uses OGC-compliant services and REST endpoints for data queries. • Data Formats: Raster layers (e.g., GeoTIFF, PNG tiles), vector layers (GeoJSON, shapefiles), elevation models, and model-derived grid outputs. • Database: Integrates with a PostgreSQL/PostGIS backend or similar spatial database for hydrologic and geospatial data management. • Deployment: Hosted via University of Iowa infrastructure, with modular UI elements tied to specific watersheds or study areas.

Intended Use: The application provides a reference and exploratory tool for comparing modeled flood scenarios, visualizing extent and depth data, and interacting with region-specific inundation data products.

Amazon Geoglyphs Spatial Analysis Dataset 2

DATA & Tools

Data Overview and Sources

This dataset was constructed for Phase 2 research analyzing spatial relationships between Amazon geoglyphs and environmental conditions. The analysis includes NDVI and NDMI calculations and grid-based anomaly detection.

Data sources: - Sentinel-2 Composites: forobs.jrc.ec.europa.eu/sentinel/sentinel2_composite - Pan-tropical cloud-free annual composites (2020) - jqjacobs.net: Archaeogeodesy Placemarks (Amazon geoglyph category extracted from Google Earth KML)

File Structure

amazon_geoglyphs_analysis/
├── data/
│  ├── sites_geoglyphs.gpkg      # Site locations (extracted geoglyph coordinates)
│  ├── focus_rgb_swir1_nir_red.tif  # Sentinel-2 composite (RGB: SWIR1, NIR, RED channels)
│  ├── focus_ndvi.tif         # NDVI index (vegetation greenness)
│  ├── focus_ndmi.tif         # NDMI index (vegetation moisture)
│  ├── focus_area.gpkg        # Analysis boundary (study area extent)
│  ├── amazon_grid_anomaly.gpkg    # Grid-based anomaly analysis
│  └── amazon_basin.gpkg       # Amazon basin boundaries
└── analysis_project.qgz       # QGIS project (integrated analysis workflow)

QGIS Processing Workflow

1. Satellite Data Processing (focus_rgb_swir1_nir_red.tif)

• (1) Data Source: Downloaded Sentinel-2 tiles S15_W075 and S15_W065 (2020, false color composite)
• (2) Raster → Miscellaneous → Build Virtual Raster: Merge two tiles into single virtual raster
• (3) Vector → Geoprocessing Tools → Clip Raster by Mask Layer: Clip merged raster to focus_area boundary

2. NDVI Calculation (focus_ndvi.tif)

• (1) Raster → Raster Calculator: Calculate Normalized Difference Vegetation Index Expression: ("focus_rgb_swir1_nir_red@2" - "focus_rgb_swir1_nir_red@3") / ("focus_rgb_swir1_nir_red@2" + "focus_rgb_swir1_nir_red@3")
• (2) Formula: NDVI = (NIR - RED) / (NIR + RED)
• (3) Layer Properties → Symbology: Apply RdYlGr color ramp for vegetation visualization

3. NDMI Calculation (focus_ndmi.tif)

• (1) Raster → Raster Calculator: Calculate Normalized Difference Moisture Index Expression: ("focus_rgb_swir1_nir_red@2" - "focus_rgb_swir1_nir_red@1") / ("focus_rgb_swir1_nir_red@2" + "focus_rgb_swir1_nir_red@1")
• (2) Formula: NDMI = (NIR - SWIR1) / (NIR + SWIR1)
• (3) Purpose: Monitor vegetation water content and drought conditions

4. Grid-based Anomaly Analysis (amazon_grid_anomaly.gpkg)

Layer 1: g_005_ndmi_ndvi (Fine-scale Grid Statistics)

• (1) Vector → Research Tools → Create Grid: Create 0.005° interval grid (~550m resolution)
• (2) Vector → Analysis Tools → Zonal Statistics: Calculate zonal statistics for NDVI and NDMI by grid cell
  • Statistics: Mean
  • Target rasters: focus_ndvi.tif, focus_ndmi.tif

Layer 2: g_050_anomaly_count (Anomaly Frequency Analysis)

• (1) Vector → Research Tools → Select by Expression: Identify anomalous grid cells Expression: "ndvi_mean" <= "ndvi_p10" AND "ndmi_mean" <= "ndmi_p10"
• (2) Vector → Research Tools → Create Grid: Create 0.05° interval grid (~5.5km resolution)
• (3) Vector → Analysis Tools → Join Attributes by Location (Summary): Count anomalous fine-scale grids within coarse-scale grids
• (4) Purpose: Identify areas with consistently low vegetation greenness and moisture (potential archaeological signatures)

The multi-scale topographic position color composite (MTPCC) describes the position of a pixel relative to its neighborhood at several spatial scales. It was derived from the national airborne DTM (RGE ALTI ®) at 5 m spatial resolution, which is available from the website of the French National Geographic Institute (IGN) (https://geoservices.ign.fr/).

Dataset includes:

280 GeoTIFF raster files (MTPCC_000.tif) projected in the French Lambert-93 system (EPSG code 2154), each file corresponding to a 50 x 50 km tile. The number indicates the tile of interest ;

1 vector tile index at Google Earth format (tile_index.kmz) showing the location of each tile. This file has been created to facilitate download layer only on area of interest.

To reduce storage space and download time, each raster file has been packed at 7-Zip freeware format.

A complete description of the dataset can be found in Panhelleux, L., Rapinel, S., Lemercier, B., Gayet, G., Hubert-Moy, L., 2023. A 5 m dataset of digital terrain model derivatives across mainland France. Data in Brief 109369. https://doi.org/10.1016/j.dib.2023.109369

GTOPO30 is a global raster digital elevation model (DEM) providing terrain elevation data with a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). GTOPO30 was derived from several raster and vector sources of topographic information. For easier distribution, GTOPO30 has been divided into tiles [https://rda.ucar.edu/datasets/ds758.0/docs/tiles.gif]. Detailed information on the characteristics of GTOPO30 including the data distribution format, the data sources, production methods, accuracy, and hints for users, is found in the GTOPO30 README [https://rda.ucar.edu/datasets/ds758.0/docs/readme.txt] file.

GTOPO30, completed in late 1996, was developed over a three year period through a collaborative effort led by staff at the U.S. Geological Survey's Center for Earth Resources Observation and Science (EROS). The following organizations participated by contributing funding or source data: the National Aeronautics and Space Administration (NASA), the United Nations Environment Program and Global Resource Information Database (UNEP and GRID), the U.S. Agency for International Development (USAID), the Instituto Nacional de Estadistica Geografica e Informatica (INEGI) of Mexico, the Geographical Survey Institute (GSI) of Japan, Manaaki Whenua Landcare Research of New Zealand, and the Scientific Committee on Antarctic Research (SCAR).

The topographic wetness index (TWI) characterizes potential soil wetness as a function of the contributing area and local slope. It was derived from the national airborne DTM (RGE ALTI ®) at 5 m spatial resolution, which is available from the website of the French National Geographic Institute (IGN) (https://geoservices.ign.fr/).

Dataset includes:

280 GeoTIFF raster files (TWI_000.tif) projected in the French Lambert-93 system (EPSG code 2154), each file corresponding to a 50 x 50 km tile. The number indicates the tile of interest ;

1 vector tile index at Google Earth format (tile_index.kmz) showing the location of each tile. This file has been created to facilitate download layer only on area of interest.

To reduce storage space and download time, each raster file has been packed at 7-Zip freeware format.

A complete description of the dataset can be found in Panhelleux, L., Rapinel, S., Lemercier, B., Gayet, G., Hubert-Moy, L., 2023. A 5 m dataset of digital terrain model derivatives across mainland France. Data in Brief 109369. https://doi.org/10.1016/j.dib.2023.109369

This map contains district-based ACT canopy cover polygons at 1m resolution (vegetation cover above 3m) as at April/May 2020, detected with Light Detection and Ranging (LiDAR). The full ACT vector dataset has been split into districts and 1km tiles to allow for easier viewing.WARNING: Due to complexity and high resolution this dataset may draw slowly or appear to miss some trees. For best results, zoom in, view single districts separately, or download a local copy.LIDAR data was acquired in April/May 2020 for the ACT under contract by Aerometrex, at an average resolution of 12ppm. LiDAR is classified to Level 3 (for ground) and delivered as LAS v1.4 in in GDA2020 MGA zone 55. Visit https://www.planning.act.gov.au/professionals/survey-spatial/spatial-information/lidar-data for more info. Processing was completed on the LAS GDA2020 MGA Zone 55 LAS tile set using AHD vertical datum at 1m resolution. Dataset represents high vegetation above 3m (tree canopy or tree-approximate objects - see caveats).Methodology:High noise errors were reclassified.Digital Surface Model (DSM) and Digital Elevation Model (DEM) surfaces were created using ArcPro 2.8 LAS Dataset Geoprocessing Tools at 1 m resolution.Canopy Height Model CHM was determined = Digital Surface Model (DSM) - Digital Elevation Model (DEM).Pits (empty cells inside tree canopies) of 2 pixel (2x1m) were removed using Nibble.An ACT Urban building footprint layer generated from the 2020 LIDAR dataset by Aerometrex was used to remove spurious canopy portions from building roof areas in the urban area.An NDVI layer from pan-sharpened Pleiades multispectral satellite imagery, acquired in April 2019 (the Uriarra area) and November 2019 (rest of Urban Area) was used to delete erroneous non-vegetative surfaces in urban areas.Canopy holes <=1m2 were filled by Eliminate tool.GDA2020 MGA Zone 55.Tree canopy lower than 3m were removed. Converted to a binary raster, then converted to vector.Other available datasets: ACT 2020 Canopy Height Model (raster), ACT Trees (Individual Delineated Trees with Height) (vector), DEM, DSM, Contours, Building Footprints (© Australian Capital Territory & Aerometrex Limited), ACT Permeability, shrub cover - contact spatialdata@act.gov.au. Full LAS Tile sets can also be obtained in the following vertical datums: GDA2020 ellipsoidal, AHD, AVWS - see https://elevation.fsdf.org.au/. Other derivative products (including other canopy products) are available on request or through the ACT Geospatial Data Catalogue.Road, Block and Division Canopy 2020 Statistics also available here: https://actmapi-actgov.opendata.arcgis.com/maps/act-canopy-cover-2020-statistics/aboutCaveats:1. Please note that most (if not all) CHM-based tree delineation results should be thought of as "tree-approximate objects", and not actual trees. 2. Although every effort was made to remove any erroneous polygons (such as street lights, back yard fences and powerlines), there are likely to be some errors remaining.Creative Commons by Attribution (CCBY) 4.0 (Australian Capital Territory). Any sharing, adaption/transformation and value adding, including commercial use should be attributed to ACT Government (Australian Capital Territory). This dataset has been created from the original LiDAR capture and classification © Australian Capital Territory & Aerometrex Limited 2020.How to cite this data: ACT Government (2020) (Botha, H). ACT Canopy Cover 1m 2020. Environment, Planning and Sustainable Development Directorate (EPSDD), ACT Government. Canberra, ACT. Accessed via ACT Geospatial Data Catalogue.

This dataset is a hosted tile layer of a 10m spatial resolution Land Use/Land Cover (LULC) raster grid, created from several open source data products and covering the Republic of Indonesia for the year 2019/2020. This hosted tile layer aids visualisation of the LULC data used in an interactive R Shiny application to generate landscape metrics at user defined locations. Lineage: This hybrid landcover data was created by mosaicking together the open source data products (i) ESRI 2020 Landcover, (ii) Global industrial and smallholder oil palm, (iii) Global Human Settlement and (iv) Open Street Map. Oil palm and human settlement data was resampled with the same origin (so that pixels align) and projection (EPSG:3857) as the ESRI 2020 Landcover data. Open street map waterways and roads were first converted from vector to raster, then resampled and projected to align with the other layers. The layers were then mosaicked together with the following ranking (high to low): Roads (Open Street Map), Waterways (Open Street Map), Global Human Settlement, Industrial and smallholder oil palm, and ESRI 2020 landcover. The LULC classes in the hybrid dataset are bare ground, built area, clouds, crops, flooded vegetation, grass, human settlement, industrial oil palm, smallholder oil palm, roads, scrub/shrub, trees, and water. The resultant raster grid was then converted to a map tile package and uploaded as a hosted tile layer to ArcGIS Online.

This map contains district-based ACT canopy cover polygons at 1m resolution (vegetation cover above 3m) as at April/May 2020, detected with Light Detection and Ranging (LiDAR). The full ACT vector dataset has been split into districts and 1km tiles to allow for easier viewing.WARNING: Due to complexity and high resolution this dataset may draw slowly or appear to miss some trees. For best results, zoom in, view single districts separately, or download a local copy.LIDAR data was acquired in April/May 2020 for the ACT under contract by Aerometrex, at an average resolution of 12ppm. LiDAR is classified to Level 3 (for ground) and delivered as LAS v1.4 in in GDA2020 MGA zone 55. Visit https://www.planning.act.gov.au/professionals/survey-spatial/spatial-information/lidar-data for more info. Processing was completed on the LAS GDA2020 MGA Zone 55 LAS tile set using AHD vertical datum at 1m resolution. Dataset represents high vegetation above 3m (tree canopy or tree-approximate objects - see caveats).Methodology:High noise errors were reclassified.Digital Surface Model (DSM) and Digital Elevation Model (DEM) surfaces were created using ArcPro 2.8 LAS Dataset Geoprocessing Tools at 1 m resolution.Canopy Height Model CHM was determined = Digital Surface Model (DSM) - Digital Elevation Model (DEM).Pits (empty cells inside tree canopies) of 2 pixel (2x1m) were removed using Nibble.An ACT Urban building footprint layer generated from the 2020 LIDAR dataset by Aerometrex was used to remove spurious canopy portions from building roof areas in the urban area.An NDVI layer from pan-sharpened Pleiades multispectral satellite imagery, acquired in April 2019 (the Uriarra area) and November 2019 (rest of Urban Area) was used to delete erroneous non-vegetative surfaces in urban areas.Canopy holes <=1m2 were filled by Eliminate tool.GDA2020 MGA Zone 55.Tree canopy lower than 3m were removed. Converted to a binary raster, then converted to vector.Other available datasets: ACT 2020 Canopy Height Model (raster), ACT Trees (Individual Delineated Trees with Height) (vector), DEM, DSM, Contours, Building Footprints (© Australian Capital Territory & Aerometrex Limited), ACT Permeability, shrub cover - contact spatialdata@act.gov.au. Full LAS Tile sets can also be obtained in the following vertical datums: GDA2020 ellipsoidal, AHD, AVWS - see https://elevation.fsdf.org.au/. Other derivative products (including other canopy products) are available on request or through the ACT Geospatial Data Catalogue.Road, Block and Division Canopy 2020 Statistics also available here: https://actmapi-actgov.opendata.arcgis.com/maps/act-canopy-cover-2020-statistics/aboutCaveats:1. Please note that most (if not all) CHM-based tree delineation results should be thought of as "tree-approximate objects", and not actual trees. 2. Although every effort was made to remove any erroneous polygons (such as street lights, back yard fences and powerlines), there are likely to be some errors remaining.Creative Commons by Attribution (CCBY) 4.0 (Australian Capital Territory). Any sharing, adaption/transformation and value adding, including commercial use should be attributed to ACT Government (Australian Capital Territory). This dataset has been created from the original LiDAR capture and classification © Australian Capital Territory & Aerometrex Limited 2020.How to cite this data: ACT Government (2020) (Botha, H). ACT Canopy Cover 1m 2020. Environment, Planning and Sustainable Development Directorate (EPSDD), ACT Government. Canberra, ACT. Accessed via ACT Geospatial Data Catalogue.

The vertical distance to channel network index (VDCNI) expresses the vertical height (in meter) between the elevation of a pixel and the nearest channel. It was derived from the national airborne DTM (RGE ALTI ®) at 5 m spatial resolution, which is available from the website of the French National Geographic Institute (IGN) (https://geoservices.ign.fr/), and the GIS layer of the channel network in the national hydrological database https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274.

Dataset includes:

280 GeoTIFF raster files (VDCNI_000.tif) projected in the French Lambert-93 system (EPSG code 2154), each file corresponding to a 50 x 50 km tile. The number indicates the tile of interest ;

1 vector tile index at Google Earth format (tile_index.kmz) showing the location of each tile. This file has been created to facilitate download layer only on area of interest.

To reduce storage space and download time, each raster file has been packed at 7-Zip freeware format.

A complete description of the dataset can be found in Panhelleux, L., Rapinel, S., Lemercier, B., Gayet, G., Hubert-Moy, L., 2023. A 5 m dataset of digital terrain model derivatives across mainland France. Data in Brief 109369. https://doi.org/10.1016/j.dib.2023.109369

Laatste update: 26 januari 2024Vector tilesVector tiles zijn pakketjes met vectoren (punten, lijnen, vlakken). Van vector tiles worden aan de serverkant geen plaatjes gemaakt, zoals bij raster tiles. Bij vector tiles wordt de symbologie in een apart bestand geregeld en opgeslagen.

This map contains district-based ACT canopy cover polygons at 1m resolution (vegetation cover above 3m) as at April/May 2020, detected with Light Detection and Ranging (LiDAR). The full ACT vector dataset has been split into districts and 1km tiles to allow for easier viewing.WARNING: Due to complexity and high resolution this dataset may draw slowly or appear to miss some trees. For best results, zoom in, view single districts separately, or download a local copy.LIDAR data was acquired in April/May 2020 for the ACT under contract by Aerometrex, at an average resolution of 12ppm. LiDAR is classified to Level 3 (for ground) and delivered as LAS v1.4 in in GDA2020 MGA zone 55. Visit https://www.planning.act.gov.au/professionals/survey-spatial/spatial-information/lidar-data for more info. Processing was completed on the LAS GDA2020 MGA Zone 55 LAS tile set using AHD vertical datum at 1m resolution. Dataset represents high vegetation above 3m (tree canopy or tree-approximate objects - see caveats).Methodology:High noise errors were reclassified.Digital Surface Model (DSM) and Digital Elevation Model (DEM) surfaces were created using ArcPro 2.8 LAS Dataset Geoprocessing Tools at 1 m resolution.Canopy Height Model CHM was determined = Digital Surface Model (DSM) - Digital Elevation Model (DEM).Pits (empty cells inside tree canopies) of 2 pixel (2x1m) were removed using Nibble.An ACT Urban building footprint layer generated from the 2020 LIDAR dataset by Aerometrex was used to remove spurious canopy portions from building roof areas in the urban area.An NDVI layer from pan-sharpened Pleiades multispectral satellite imagery, acquired in April 2019 (the Uriarra area) and November 2019 (rest of Urban Area) was used to delete erroneous non-vegetative surfaces in urban areas.Canopy holes <=1m2 were filled by Eliminate tool.GDA2020 MGA Zone 55.Tree canopy lower than 3m were removed. Converted to a binary raster, then converted to vector.Other available datasets: ACT 2020 Canopy Height Model (raster), ACT Trees (Individual Delineated Trees with Height) (vector), DEM, DSM, Contours, Building Footprints (© Australian Capital Territory & Aerometrex Limited), ACT Permeability, shrub cover - contact spatialdata@act.gov.au. Full LAS Tile sets can also be obtained in the following vertical datums: GDA2020 ellipsoidal, AHD, AVWS - see https://elevation.fsdf.org.au/. Other derivative products (including other canopy products) are available on request or through the ACT Geospatial Data Catalogue.Road, Block and Division Canopy 2020 Statistics also available here: https://actmapi-actgov.opendata.arcgis.com/maps/act-canopy-cover-2020-statistics/aboutCaveats:1. Please note that most (if not all) CHM-based tree delineation results should be thought of as "tree-approximate objects", and not actual trees. 2. Although every effort was made to remove any erroneous polygons (such as street lights, back yard fences and powerlines), there are likely to be some errors remaining.Creative Commons by Attribution (CCBY) 4.0 (Australian Capital Territory). Any sharing, adaption/transformation and value adding, including commercial use should be attributed to ACT Government (Australian Capital Territory). This dataset has been created from the original LiDAR capture and classification © Australian Capital Territory & Aerometrex Limited 2020.How to cite this data: ACT Government (2020) (Botha, H). ACT Canopy Cover 1m 2020. Environment, Planning and Sustainable Development Directorate (EPSDD), ACT Government. Canberra, ACT. Accessed via ACT Geospatial Data Catalogue.

TASK NAME: NOAA OCM Lidar for Lowndes County, GA with the option to Collect Lidar in Cook and Tift Counties, GA Lidar Data Acquisition and Processing Production Task NOAA Contract No. EA133C11CQ0010 Woolpert Order No. 05271 CONTRACTOR: Woolpert, Inc. This data set is comprised of lidar point cloud data, raster DEM, hydrologic 3-d breaklines, flightline vectors, survey control, project tile index, and project data extent. This task order requires lidar data to be acquired over the Lowndes County, GA area of interest (AOI), and will be acquired as part of this task order. The total area of the Lowndes County, GA AOI is approximately 500 square miles. The lidar data acquisition parameters for this mission are detailed in the lidar processing report for this task order. The lidar data will be acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, four (4) foot pixel raster DEMs of the bare-earth surface ESRI Grid Format. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, flightline vectors, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format. Ground conditions: Water at normal levels; no unusual inundation; no snow. Original contact information: Contact Org: Woolpert Phone: (937) 461-5660

This map contains district-based ACT canopy cover polygons at 1m resolution (vegetation cover above 3m) as at April/May 2020, detected with Light Detection and Ranging (LiDAR). The full ACT vector dataset has been split into districts and 1km tiles to allow for easier viewing.WARNING: Due to complexity and high resolution this dataset may draw slowly or appear to miss some trees. For best results, zoom in, view single districts separately, or download a local copy.LIDAR data was acquired in April/May 2020 for the ACT under contract by Aerometrex, at an average resolution of 12ppm. LiDAR is classified to Level 3 (for ground) and delivered as LAS v1.4 in in GDA2020 MGA zone 55. Visit https://www.planning.act.gov.au/professionals/survey-spatial/spatial-information/lidar-data for more info. Processing was completed on the LAS GDA2020 MGA Zone 55 LAS tile set using AHD vertical datum at 1m resolution. Dataset represents high vegetation above 3m (tree canopy or tree-approximate objects - see caveats).Methodology:High noise errors were reclassified.Digital Surface Model (DSM) and Digital Elevation Model (DEM) surfaces were created using ArcPro 2.8 LAS Dataset Geoprocessing Tools at 1 m resolution.Canopy Height Model CHM was determined = Digital Surface Model (DSM) - Digital Elevation Model (DEM).Pits (empty cells inside tree canopies) of 2 pixel (2x1m) were removed using Nibble.An ACT Urban building footprint layer generated from the 2020 LIDAR dataset by Aerometrex was used to remove spurious canopy portions from building roof areas in the urban area.An NDVI layer from pan-sharpened Pleiades multispectral satellite imagery, acquired in April 2019 (the Uriarra area) and November 2019 (rest of Urban Area) was used to delete erroneous non-vegetative surfaces in urban areas.Canopy holes <=1m2 were filled by Eliminate tool.GDA2020 MGA Zone 55.Tree canopy lower than 3m were removed. Converted to a binary raster, then converted to vector.Other available datasets: ACT 2020 Canopy Height Model (raster), ACT Trees (Individual Delineated Trees with Height) (vector), DEM, DSM, Contours, Building Footprints (© Australian Capital Territory & Aerometrex Limited), ACT Permeability, shrub cover - contact spatialdata@act.gov.au. Full LAS Tile sets can also be obtained in the following vertical datums: GDA2020 ellipsoidal, AHD, AVWS - see https://elevation.fsdf.org.au/. Other derivative products (including other canopy products) are available on request or through the ACT Geospatial Data Catalogue.Road, Block and Division Canopy 2020 Statistics also available here: https://actmapi-actgov.opendata.arcgis.com/maps/act-canopy-cover-2020-statistics/aboutCaveats:1. Please note that most (if not all) CHM-based tree delineation results should be thought of as "tree-approximate objects", and not actual trees. 2. Although every effort was made to remove any erroneous polygons (such as street lights, back yard fences and powerlines), there are likely to be some errors remaining.Creative Commons by Attribution (CCBY) 4.0 (Australian Capital Territory). Any sharing, adaption/transformation and value adding, including commercial use should be attributed to ACT Government (Australian Capital Territory). This dataset has been created from the original LiDAR capture and classification © Australian Capital Territory & Aerometrex Limited 2020.How to cite this data: ACT Government (2020) (Botha, H). ACT Canopy Cover 1m 2020. Environment, Planning and Sustainable Development Directorate (EPSDD), ACT Government. Canberra, ACT. Accessed via ACT Geospatial Data Catalogue.