The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature c ...

The full datasheet for this product is available here.The Sonoma County hydrologic data deliverables were produced in fall 2015 and winter 2016 from the countywide 2013 LiDAR data. The hydrologic products include a set of vector deliverables and a set of raster deliverables. Vector products include stream centerlines, confluence points, hydroenforcement burn locations, and watersheds. Raster products include flow direction, flow accumulation, and a hydroenforced bare earth digital elevation model (DEM). Hydroenforcement of a DEM imparts the true elevations of culverts, pipelines, and other buried passages for water into a Digital Elevation Model, creating a DEM suitable for modeling the flow of surface water.

The extent of all deliverables is all of Sonoma County, the Lake Sonoma watershed in Mendocino County, and the Lake Mendocino area. Appropriate Use: These hydrologic datasets are a mostly-automated first step in the eventual development of a 'localized' or 'LiDAR enhanced' National Hydrography Dataset (NHD). They are suitable for landscape level planning and hydrologic modeling. These data products do not contain a guarantee of accuracy or precision and – without site specific validation and/or refinement – should not be relied upon for engineering level or very fine scale decision making. Detailed Dataset Description:These hydrologic data products were produced by Quantum Spatial. Quantum Spatial used mainly automated methods to create the hydrologic data products. Quantum Spatial included a short data report with the hydrologic datasets titled Sonoma County Hydroenforcement Technical Data Report - access that report here: https://sonomaopenspace.egnyte.com/dl/nHT2fGg8TP

The individual hydrologic data products are described briefly below.

  Vector Hydro Products (contained in this file gdb):    

Stream Centerlines – Centerlines of streams in Sonoma County. An area of flow concentration is considered a stream if its flow accumulation (upstream catchment area) exceeds 5 acres and a clearly defined channel exists. Where possible, stream centerline names (GNIS_Name) are consistent with the NHD. Hydroenforcement Burn Locations - Line features that represent locations where hydroenforcement occurred. Confluence Points – Points that represent stream intersections (confluences).Watersheds (HUC2 through HUC16) – Watershed boundaries for nested hydrologic units from HUC 2 (region) to HUC 16 (eighth level sub-watershed). Where possible, watershed names are consistent with the NHD. Watershed mapping conventions follow those for NHD's Watershed Boundary Dataset (http://nhd.usgs.gov/wbd.html).

Raster Hydrologic Products (1-meter resolution - available at http://sonomavegmap.org/data-downloads)Hydroenforced Digital Elevation Model – The Hydroenforced DEM is the LiDAR derived (2013) bare earth DEM with contours, pipelines and other buried passages to water 'burned in', so that the DEM correctly models surface water flow.Flow Direction Rasters – Values in a flow direction raster represent one of eight directions (pixel values range from 1 to 8); No Data represents areas where there is no flow off of the pixel (sinks).Flow Accumulation Rasters – Flow accumulation is a measure of upstream catchment area. Pixel values in a flow accumulation raster represent the cumulative number of upstream pixels (in other words, the count of pixels that contribute flow to a given pixel).

The Geospatial Fabric version 1.1 (GFv1.1 or v1_1) is a dataset of spatial modeling units covering the conterminous United States (CONUS) and most major river basins that flow in from Canada. The GFv1.1 is an update to the original Geospatial Fabric (GFv1, Viger and Bock, 2014) for the National Hydrologic Modeling (NHM). Analogous to the GFv1, the GFv1.1 described here includes the following vector feature classes: points of interest (POIs_v1_1), a stream network (nsegment_v1_1), and hydrologic response units (nhru_v1_1), with several additional ancillary tables. These data are contained within the Environmental Systems Research Institute (ESRI) geodatabase format (GFv1.1.gdb).

Fundamental Base of Geographic Data of the Czech Republic (ZABAGED®) is a vector geographic digital model of the territory of the Czech Republic (ČR).At present time the planimetric component of ZABAGED® consists of 136 feature types as settlements, communications, utility networks and pipelines, hydrography, administrative units and protected areas, vegetation and land cover, terrain relief and selected data about survey control points. The features are represented by 2D vector spatial component and a descriptive component containing qualitative and quantitative information about features.

The GDAL/OGR libraries are open-source, geo-spatial libraries that work with a wide range of raster and vector data sources. One of many impressive features of the GDAL/OGR libraries is the ViRTual (VRT) format. It is an XML format description of how to transform raster or vector data sources on the fly into a new dataset. The transformations include: mosaicking, re-projection, look-up table (raster), change data type (raster), and SQL SELECT command (vector). VRTs can be used by GDAL/OGR functions and utilities as if they were an original source, even allowing for chaining of functionality, for example: have a VRT mosaic hundreds of VRTs that use look-up tables to transform original GeoTiff files. We used the VRT format for the presentation of hydrologic model results, allowing for thousands of small VRT files representing all components of the monthly water balance to be transformations of a single land cover GeoTiff file.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/

6 inch resolution raster image of New York City, classified by landcover type.

High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

The compressed package (Study_code.zip) contains the code files implemented by an under review paper ("What you see is what you get: Delineating urban jobs-housing spatial distribution at a parcel scale by using street view imagery based on deep learning technique").The compressed package (input_land_parcel_with_attributes.zip) is the sampled mixed "jobs-housing" attributes data of the study area with multiple probability attributes (Only working, Only living, working and living) at the land parcel scale.The compressed package (input_street_view_images.zip) is the surrounding street view data near sampled land parcels (input_land_parcel_with_attributes.zip) with the pixel size of 240*160 obtained from Tencent map (https://map.qq.com/).The compressed package (output_results.zip) contains the result vector files (Jobs-housing pattern distribution and error distribution) and file description (Readme.txt).This project uses some Python open source libraries (Numpy, Pandas, Selenium, Gdal, Pytorch and sklearn). This project complies with the GPL license.Numpy (https://numpy.org/) is an open source numerical calculation tool developed by Travis Oliphant. Used in this project for matrix operation. This library complies with the BSD license.Pandas (https://pandas.pydata.org/) is an open source library, providing high-performance, easy-to-use data structures and data analysis tools. This library complies with the BSD license.Selenium(https://www.selenium.dev/) is a suite of tools for automating web browsers.Used in this project for getting street view images.This library complies with the BSD license.Gdal(https://gdal.org/) is a translator library for raster and vector geospatial data formats.Used in this project for processing geospatial data.This library complies with the BSD license.Pytorch(https://pytorch.org/) is an open source machine learning framework that accelerates the path from research prototyping to production deployment.Used in this project for deep learning.This library complies with the BSD license.sklearn(https://scikit-learn.org/) is an open source machine learning tool for python.Used in this project for comparing precision metrics.This library complies with the BSD license.

This U.S. Geological Survey (USGS) data release presents a digital database of geospatially enabled vector layers and tabular data transcribed from the geologic map of the Lake Owen quadrangle, Albany County, Wyoming, which was originally published as U.S. Geological Survey Geologic Quadrangle Map GQ-1304 (Houston and Orback, 1976). The 7.5-minute Lake Owen quadrangle is located in southeastern Wyoming approximately 25 miles (40 kilometers) southwest of Laramie in the west-central interior of southern Albany County, and covers most of the southern extent of Sheep Mountain, the southeastern extent of Centennial Valley, and a portion of the eastern Medicine Bow Mountains. This relational geodatabase, with georeferenced data layers digitized at the publication scale of 1:24,000, organizes and describes the geologic and structural data covering the quadrangle's approximately 35,954 acres and enables the data for use in spatial analyses and computer cartography. The data types presented in this release include geospatial features (points, lines, and polygons) with matching attribute tables, nonspatial descriptive and reference tables, and ancillary resource files for correct symbolization, in formats that conform to the Geologic Map Schema (GeMS) developed and released by the U.S. Geological Survey's National Cooperative Geologic Mapping Program (GeMS, 2020). When reconstructed from the geodatabase's vector layers and tabular data that has been symbolized according to specifications encoded in the accompanying style file, and using the supplied Federal Geographic Data Committee (FGDC) GeoAge font for labeling formations and GeoSym fonts for structural line decorations and orientation measurement symbols, this data release presents the Geologic Map as shown on the published GQ-1304 map sheet. These GIS data augment but do not supersede the information presented on GQ-1304. References: Houston, R.S., and Orback, C.J., 1976, Geologic Map of the Lake Owen Quadrangle, Albany County, Wyoming: U.S. Geological Survey Geologic Quadrangle Map GQ-1304, scale 1:24,000, https://doi.org/10.3133/gq1304. U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema)- A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org//10.3133/tm11B10.

The Transboundary Geospatial Fabric (TGF) is a dataset of spatial modeling units consistent with the Geospatial Fabric for National Hydrologic Modeling (abbreviated within this document as GFv1, Viger and Bock, 2014). These features were derived from National Hydrography Dataset Plus High Resolution data (NHDPlus HR, U.S. Geological Survey [USGS], 2018) in the following conterminous United States (CONUS) - Canada transboundary four-digit Hydrologic Units (HUC4): 0101, 0105, 0108, 0901, 0902, 0903, 0904, 1005, 1006, 1701, 1702, and 1711. The data described here include the following vector feature classes: points of interest (POIs), a stream network (nsegment), major waterbodies (waterbodies), and hydrologic response units (nhru). These feature classes are contained within the Environmental Systems Research Institute (ESRI) geodatabase format (TGF.gdb).

These data (vector and raster) were compiled for spatial modeling of salinity yield sources in the Upper Colorado River Basin (UCRB) and describe different scales of watersheds in the Upper Colorado River Basin (UCRB) for use in salinity yield modeling. Salinity yield refers to how much dissolved salts are picked up in surface waters that could be expected to be measured at the watershed outlet point annually. The vector polygons are small catchments developed originally for use in SPARROW modeling that break up the UCRB into 10,789 catchments linked together through a synthetic stream network. The catchments were used for a machine learning based salinity model and attributed with the new results in these vector GIS datasets. Although all of these feature classes include the same polygons, the attribute tables for each include differing outputs from new salinity models and a comparison with SPARROW model results from previous research. The new model presented in these datasets utilizes new predictive soil maps and a more flexible random forest function to improve on previous UCRB salinity spatial models. The raster data layers represent aspects of soils, topography, climate, and runoff characteristics that have hypothesized influences on salinity yields.

Purpose:To provide detailed 2-foot elevation contours across Wood County, supporting topographic analysis, land use planning, environmental assessments, and infrastructure development.Supplemental Information:This dataset offers precise elevation data based on LiDAR, valuable for environmental planning, flood risk assessment, and site development.Keywords:Elevation, Contours, Topography, LiDAR, Wood County, Environmental PlanningLineage:Source(s): Derived from LiDAR data collected by Wood County GIS. The data was processed to generate 2-foot interval contours, ensuring high accuracy and consistency.Process Steps:LiDAR data was processed to create contour lines at a 2-foot interval, using digital elevation models and specialized software to ensure accuracy in elevation representation.Accuracy and Consistency:Vertical Accuracy: Consistent with LiDAR standards, suitable for detailed elevation and landform analysis.Positional Accuracy: Verified against ground control points to align with Wood County’s high-accuracy mapping standards.Logical Consistency: Complete, continuous contour lines with no overlaps or breaks, ensuring seamless integration in topographic maps.Data Type:Vector (Polyline)Composition:Polyline features representing 2-foot contour intervals across Wood County.Spatial Reference:Coordinate System: Web Mercator (WGS 84)WKID: 3857Time Period of Content:Reflects conditions at the time of the most recent LiDAR acquisition and processing.Currentness Reference:Updated in line with new LiDAR acquisitions or significant topographical changes, supporting infrastructure and environmental projects.Contacts:Primary Contact:David L. Price, GIS Coordinator, Wood County GIS DepartmentPhone: (419) 373-3984Email: dprice@woodcountyohio.govDistributor:Available upon request through Wood County GIS or accessible via the Wood County Public Geolibrary.Distribution Liability:Wood County disclaims responsibility for misuse or incorrect application of this dataset. Provided "as-is" without warranty.Metadata Date:October 21, 2024Metadata Review Date:October 21, 2025Metadata Contact:David L. Price, (419) 373-3984, dprice@woodcountyohio.gov

Purpose:To provide a detailed spatial representation of drainage paths derived from LiDAR data within Wood County. This dataset supports hydrological analysis, resource management, and environmental planning.Supplemental Information:This dataset was created using high-resolution LiDAR data to identify and map drainage paths, ensuring accuracy in representing hydrological flow patterns and their attributes.Keywords:LiDAR, Drainage Paths, Hydrology, Environmental Planning, Resource Management, Wood CountyLineage:Source(s):Derived from high-resolution LiDAR data collected for Wood County.Process Step(s):The dataset was generated by Intern Kingsley Kanjin using advanced LiDAR processing techniques to delineate drainage paths. Steps included point cloud processing, flow accumulation analysis, and vectorization to create a line layer of drainage paths.Positional Accuracy:Consistent with the original LiDAR data, ensuring a high degree of spatial accuracy for hydrological modeling and analysis.Attribute Accuracy:Attributes have been verified for consistency with the LiDAR-derived flow accumulation and drainage path modeling.Logical Consistency:The dataset is logically consistent, with continuous drainage lines validated for hydrological network integrity.Type of Data:Vector (Polyline)Composition:The dataset consists of polyline features representing drainage paths. Attributes may include flow direction, stream order, and accumulation values.Coordinate System:NAD 1983 NSRS2007 StatePlane Ohio North FIPS 3401 (US Feet)Projection:Lambert Conformal ConicDatum:NAD 1983 NSRS2007Time Period of Content:Data represents conditions as captured during the LiDAR survey for Wood County.Currentness Reference:The dataset is updated based on new LiDAR surveys or significant changes in land use and hydrological features.ContactsPrimary Contact:Name: David L. PriceOrganization: Wood County GIS DepartmentPosition: GIS CoordinatorAddress: 1 Court House Sq., Bowling Green, OH 43402Phone: (419) 373-3984Email: dprice@woodcountyohio.govDistributor:Name: Wood County GIS DepartmentResource Description: Available upon request through the Wood County GIS Public Data Site.Distribution Liability:Wood County is not responsible for improper or incorrect use of this dataset. The dataset is provided "as-is" without warranty for completeness or accuracy for any specific use.Metadata Date:January 9, 2025Metadata Review Date:January 9, 2026Metadata Contact:Name: David L. PricePhone: (419) 373-3984Email: dprice@woodcountyohio.gov

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset is a complete state-wide digital land use map of Queensland. The dataset is a product of the Queensland Land Use Mapping Program (QLUMP) and was produced by the Queensland Government. It presents the most current mapping of land use features for Queensland, including the land use mapping products from 1999, 2006 and 2009, in a single feature layer. This dataset was last updated July 2012. See additional information also.

Purpose

Indicates the current primary use or management objective of the land.

Dataset History

Source DataQueensland Government - Land use mapping (1999); Landsat TM and ETM imagery; Spot5 imagery; High resolution ortho photography through the Spatial Imagery Subscription Plan (SISP); Queensland Digital Cadastral Database (DCDB) (2009), Queensland Valuation and Sales Database (QVAS) (2009); Queensland Nature Refuges (2009); Queensland Estates (2009); Queensland Herbarium's Regional Ecosystem, Water Body and Wetlands datasets (2009); Statewide Landcover & Trees Study (SLATS) Queensland Dams and Waterbodies (2009) and land cover change data; scanned aerial photography (1999-2009).Additional verbal & written information on land uses & their locations was obtained from regional Queensland Government officers, Local Government Authorities, land owners & managers, private industry as well as from field observations & checking.Data captureA range of existing digital datasets containing land use information was collated from the Queensland Government spatial data inventory and prepared for use in a GIS using ArcGIS and ERDAS Imagine software.Processing steps To compile the 1999 baseline mapping, datasets containing baseline land cover (supplied by SLATS), Protected Areas, State Forest and Timber Reserves, plantations, coastal wetlands, reserves (from DCDB) and logged forests were interpreted in a spatial model to produce a preliminary land use raster image.The model incorporated a decision matrix which assigned each pixel a specific land use class according to a set of pre-determined rules.Individual catchments were clipped from the model output and enhanced with additional land use information interpreted primarily from Landsat TM and ETM imagery as well as scanned and hardcopy aerial photography (where available). The DCDB and other datasets containing land use information were used to help identify property and land use type boundaries. This process produced a draft land use raster.Verification of the draft land use dataset, particularly those with significant areas of intensive land uses, was undertaken by comparing mapped land use classes with observed land use classes in the field where possible. The final raster image was converted to a vector coverage in ARC/Info and GIS editing performed.The existing 1999 baseline (or later where available) land use dataset (vector) formed the basis for the 2006 and 2009 land use mapping. The 2006 & 2009 datasets were then updated primarily by interpretation of SPOT5 imagery, high-res orthophotography, scanned aerial photography and inclusion of expert local knowledge. This was performed in an ESRI ArcSDE geodatabase replication infrastructure, across some nine regional offices. The DCDB, QVAS, Estates, Queensland Herbarium wetlands and SLATS land cover change and waterbody datasets were used to assist in identification and delineation of property and land use type boundaries. Digitised areas of uniform land use type were assigned to land use classes according to ALUMC Version 7 (May 2010).This "current" land use mapping product presents a complete state-wide land use map of Queensland, after collating the most current land use datasets within a single mapping layer.An independent validation was undertaken to assess thematic (attribute) accuracy under the ALUM classification. Please refer to the orignal source data for the validation results.

Dataset Citation

Queensland Department of Science, Information Technology, Innovation and the Arts (2013) Bioregional_Assessment_Programme_Land use mapping - Queensland current. Bioregional Assessment Source Dataset. Viewed 21 December 2017, http://data.bioregionalassessments.gov.au/dataset/740d257f-b622-49c2-9745-be283239add3.

High resolution land cover data set for New York City. This is the 3ft version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied:

The Geofabric Surface Network product provides a set of related feature classes to be used as the basis for production of consistent hydrological surface stream network analysis. This product contains a topographically consistent representation of the (major) surface water features of Australia (excluding external territories). Primarily, these are natural surface hydrology features but the product also contains some man-made features (notably reservoirs and other hydrographic features).

The Geofabric Surface Network product is based upon the input from ANUDEM Derived Streams V1.1.2 (ANUDEM Streams) which is the vectorised version of the nine second ANUDEM derived raster steams product. The product is related to, but distinct from, the stream network contained in the Geofabric Surface Cartography product. The network product represents the flow direction of streams over the surface of the terrain, based on the GEODATA Nine Second Digital Elevation Model (DEM-9S) Version 3. This product is more generalised than the Geofabric Surface Cartography and represents the main channels of the stream, particularly in areas where streams are heavily anabranched or disconnected.

In addition, the stream connectivity represents a stream flow over the terrain, regardless of the presence of a corresponding Geofabric Surface Cartography stream segment. This means that the Geofabric Surface Cartography product may represent a stream as an interrupted or intermittent feature, whereas this product represents the same stream as a continuous connected feature. That is, the path that a stream would take (according to the terrain model) if sufficient water were available for flow. This product is fully topologically correct which means that all the stream segments flow in the correct direction. It also has full connectivity based on the flow of water across a terrain model.

This product contains six feature types including: Waterbody, Network Stream, Network Node, Catchment, Network Connectivity (Upstream) and Network Connectivity (Downstream).

Purpose

This product contains a topographic representation of the (major) surface water features of 'geographic Australia' excluding external territories. It is intended to be used as the basis for production of consistent surface stream network analysis.

Geofabric Surface Network is intended to be used in stream flow tracing operations, using its full topological connection. The product can support the spatial selection of associated hydrological features as inputs for spatial analysis/modelling.

This product is intended to supplement the Geofabric Surface Cartography, Geofabric Surface Catchments and Geofabric Hydrology Reporting Catchments data products. This product is also used to support the definition of the Geofabric Surface Catchments and Geofabric Hydrology Reporting Catchments products and provides a spatial framework for analysis and assessment of streams and their catchments.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied:

Lineage statement: Geofabric Surface Network is part of a suite of Geofabric products produced by the Australian Bureau of Meteorology. The geometry of this product is largely derived from the ANUDEM Derived Streams V1.1.2 (ANUDEM Streams). It consists of water bodies such as swamps, reservoirs, lakes, etc as derived from AusHydro V1, as well as the stream lines and stream line connectors through these water bodies. The ANUDEM Streams are firstly vectorised to be usable in vector line feature format and are then informed and modified by the coincident locations of the AHGFMappedStream feature class. The features are organised into specific feature class subtypes, based upon both the inputs from the AusHydro V1.7.2 and their behaviour within the AHGF Network Stream relationships. All of the AHGFNetworkStream and AHGFWaterbody features participate in the connected stream flow topology.

This product also contains the AHGFCatchment features that are derived from the National Catchment Boundaries V1.1.4. The AGHFCatchment feature class consist of the lowest level stream flow catchments based upon the inputs from ANUDEM Streams. The catchment boundaries are based upon a single AHGFNetworkStream extent over GEODATA National 9 Second DEM grid. These catchments form the basis of aggregated catchment boundaries, either by Contracted Nodes or by Pfafstetter ID Levels.

All of these features participate in the connected stream flow topology.

Changes at v2.1

! Addition of Beta Monitoring Point Table including 479 ghost nodes

 connected to the network.

- New Water Storages in the WaterBody FC.

Changes at v2.1.1

! Replacement of Beta Monitoring Point Table and inclusion of 3,310

(formerly 479) ghost nodes connected to the stream network.



! 16 New BoM Water Storages attributed in the AHGFWaterBody feature class

and 1 completely new water storage feature added.



- SegnoLink attribute update to fix single catchment feature in Tasmania.



- Correction to spelling of Numeralla river in AHGFMappedStream (formerly

Numaralla).



! Metadata updated adding explanation of AHGFNetworkStream AusHydroEr codes

and revision made to description of DrainID field.



- Fixed a series of NoFlow catchments (small internally draining catchments

not related to a stream segment) in Murray-Darling were incorrectly

attributed as externally draining via the ExtrnlBasn field in

AHGFCatchments.



! Usage of the MergedSink attribute changed from v2.1 (see

HR_Catchments_Technical_Overview.pdf for more info).

Processing steps:

1. ANUDEM Streams dataset is received and loaded into the Geofabric development GIS environment.

2. Feature classes from ANUDEM Streams are recomposed into composited Geofabric Feature Dataset Feature Classes in the Geofabric Maintenance Geodatabase.

3. Re-composited feature classes in the Geofabric Maintenance Geodatabase Feature Dataset are assigned unique Hydro-IDs using ESRI ArcHydro for Surface Water (ArcHydro: 1.4.0.180 and ApFramework: 3.1.0.84).

4. Feature classes from the Geofabric Maintenance Geodatabase Feature Dataset are extracted and reassigned to the Geofabric Surface Network Feature Dataset within the Geofabric Surface Network Geodatabase.

A complete set of data mappings, from input source data to Geofabric Products, is included in the Geofabric Product Guide, Appendices.

Dataset Citation

Bureau of Meteorology (2014) Geofabric Surface Network - V2.1.1. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/d84e51f0-c1c1-4cf9-a23c-591f66be0d40.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT).

Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers.

This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Purpose

For use in Bioregional Assessment land classification analyses

Dataset History

NVIS Version 4.1

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Unclassified Forest

• Other Open Woodlands

• Mallee Open Woodlands and Sparse Mallee Shublands

(Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012.

Dataset Citation

Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.

Polygon vector map data covering boundaries for the City of Los Angeles containing 4 features.

Boundary GIS (Geographic Information System) data is spatial information that delineates the geographic boundaries of specific geographic features. This data typically includes polygons representing the outlines of these features, along with attributes such as names, codes, and other relevant information.

Boundary GIS data is used for a variety of purposes across multiple industries, including urban planning, environmental management, public health, transportation, and business analysis.

Available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

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As one of the plain wetland systems in northern China, Baiyangdian Wetland plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of Baiyangdian Wetland ecosystem is also of great significance for the construction of the New Area and future scientific planning. Based on the 10-meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the wetland ecosystem network distribution map and river network distribution map of in Baiyangdian basin in 2017 were drawn by artificial visual interpretation and machine automatic classification, which can provide reference for the wetland connectivity (including hydrological connectivity and landscape connectivity) in Baiyangdian basin. The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (.shp); Baiyangdian basin river channel data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of Baiyangdian basin and the river 3 km buffer) (.tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). The boundary of Baiyangdian basin in this dataset comes from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2B remote sensing image on September 20, 2017 provided by ESA. This data set uses the second, third, fourth and eighth bands of the 10m resolution in the image. The preprocessing operations such as radiometric calibration, mosaic and mosaic are carried out in SNAP and ArcGIS 10.2 software, and the supervised classification is carried out in ENVI software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, drawing the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; based on Google Earth high-resolution satellite image (resolution 0.23m), this paper uses LocaSpaceViewer software to identify and extract river channels by manual visual interpretation. For the river channels with embankment, identify and draw along the embankment; for the river channels without embankment, distinguish according to the spectral difference between the river channels and the surrounding land use types and empirical knowledge, mark the uncertain areas, and conduct field investigation in the later stage, which can ensure that the identified river channels have been extracted. The identified river channels include the main river channel, each classified river channel, abandoned river channel, etc., and all rivers are continuous. It can effectively identify the channel and ensure the accuracy of extraction. According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km, and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, with a length of 866 km, distributed in northeast-southwest direction, mostly at the junction of forest land and cultivated land; there are 337 km2 river channels in plain area, with a length of 1574 km. The Baiyangdian basin is divided into eight land use / cover types: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90% in 2017. Among all the wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of forest land is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the higher altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of marsh, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non-wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are marsh and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. In this study, 8 types of land features including river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land were selected. The total number of samples was 5199, of which 67% was used for supervised classification and 33% for accuracy verification of confusion matrix. The overall accuracy of support vector machine (SVM) classification results in Baiyangdian basin is 84.25%, and kappa coefficient is 0.82. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of Xiong'an New Area and Baiyangdian basin.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset was sourced from the Queensland Department of Natural Resources and Mines in 2012. Information provided by the Department describes the dataset as follows:

This data was originally provided on DVD and contains the converted shapefiles, layer files, raster images and project .mxd files used on the Queensland geology and structural framework map. The maps were done in ArcGIS 9.3.1 and the data stored in file geodatabases, topology created and validated. This provides greater data quality by performing topological validation on the feature's spatial relationships. For the purposes of the DVD, shapefiles were created from the file geodatabases and for MapInfo users MapInfo .tab and .wor files. The shapefiles on the DVD are a revision of the 1975 Queensland geology data, and are both are available for display, query and download on the department's online GIS application.

The Queensland geology map is a digital representation of the distribution or extent of geological units within Queensland. In the GIS, polygons have a range of attributes including unit name, type of unit, age, lithological description, dominant rock type, and an abbreviated symbol for use in labelling the polygons. The lines in this dataset are a digital representation of the position of the boundaries of geological units and other linear features such as faults and folds. The lines are attributed with a description of the type of line represented. Approximately 2000 rock units were grouped into the 250 map units in this data set. The digital data was generalised and simplified from the Department's detailed geological data and was captured at 1:500 000 scale for output at 1:2 000 000 scale.

In the ESRI version, a layer file is provided which presents the units in the colours and patterns used on the printed hard copy map. For Map Info users, a simplified colour palette is provided without patterns. However a georeferenced image of the hard copy map is included and can be displayed as a background in both Arc Map and Map Info.

The geological framework of Queensland is classified by structural or tectonic unit (provinces and basins) in which the rocks formed. These are referred to as basins (or in some cases troughs and depressions) where the original form and structure are still apparent. Provinces (and subprovinces) are generally older basins that have been strongly tectonised and/or metamorphosed so that the original basin extent and form are no longer preserved. Note that intrusive and some related volcanic rocks that overlap these provinces and basins have not been included in this classification. The map was compiled using boundaries modified and generalised from the 1:2 000 000 Queensland Geology map (2012). Outlines of subsurface basins are also shown and these are based on data and published interpretations from petroleum exploration and geophysical surveys (seismic, gravity and magnetics).

For the structural framework dataset, two versions are provided. In QLD_STRUCTURAL_FRAMEWORK, polygons are tagged with the name of the surface structural unit, and names of underlying units are imbedded in a text string in the HIERARCHY field. In QLD_STRUCTURAL_FRAMEWORK_MULTI_POLYS, the data is structured into a series of overlapping, multi-part polygons, one for each structural unit. Two layer files are provided with the ESRI data, one where units are symbolised by name. Because the dataset has been designed for units display in the order of superposition, this layer file assigns colours to the units that occur at the surface with concealed units being left uncoloured. Another layer file symbolises them by the orogen of which they are part. A similar set of palettes has been provided for Map Info.

Dataset History

Details on the source data can be found in the xml file associated with data layer.

Data in this release

*ESRI.shp and MapInfo .tab files of rock unit polygons and lines with associated layer attributes of Queensland geology

*ESRI.shp and MapInfo .tab files of structural unit polygons and lines with associated layer attributes of structural framework

*ArcMap .mxd and .lyr files and MapInfo .wor files containing symbology

*Georeferenced Queensland geology map, gravity and magnetic images

*Queensland geology map, structural framework and schematic diagram PDF files

*Data supplied in geographical coordinates (latitude/longitude) based on Geocentric Datum of Australia - GDA94

Accessing the data

Programs exist for the viewing and manipulation of the digital spatial data contained on this DVD. Accessing the digital datasets will require GIS software. The following GIS viewers can be downloaded from the internet. ESRI ArcExplorer can be found by a search of www.esriaustralia.com.au and MapInfo ProViewer by a search on www.pbinsight.com.au collectively ("the websites").

Metadata

Metadata is contained in .htm files placed in the root folder of each vector data folder. For ArcMap users metadata for viewing in ArcCatalog is held in an .xml file with each shapefile within the ESRI Shapefile folders.

Disclaimer

The State of Queensland is not responsible for the privacy practices or the content of the websites and makes no statements, representations, or warranties about the content or accuracy or completeness of, any information or products contained on the websites.

Despite our best efforts, the State of Queensland makes no warranties that the information or products available on the websites are free from infection by computer viruses or other contamination.

The State of Queensland disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages and costs you might incur as a result of accessing the websites or using the products available on the websites in any way, and for any reason.

The State of Queensland has included the websites in this document as an information source only. The State of Queensland does not promote or endorse the websites or the programs contained on them in any way.

WARNING: The Queensland Government and the Department of Natural Resources and Mines accept no liability for and give no undertakings, guarantees or warranties concerning the accuracy, completeness or fitness for the purposes of the information provided. The consumer must take all responsible steps to protect the data from unauthorised use, reproduction, distribution or publication by other parties.

Please view the 'readme.html' and 'licence.html' file for further, more complete information

Dataset Citation

Geological Survey of Queensland (2012) Queensland geology and structural framework - GIS data July 2012. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/69da6301-04c1-4993-93c1-4673f3e22762.

Australia's Land Borders is a product within the Foundation Spatial Data Framework (FSDF) suite of datasets. It is endorsed by the ANZLIC - the Spatial Information Council and the Intergovernmental Committee on Surveying and Mapping (ICSM) as a nationally consistent and topologically correct representation of the land borders published by the Australian states and territories.

The purpose of this product is to provide: (i) a building block which enables development of other national datasets; (ii) integration with other geospatial frameworks in support of data analysis; and (iii) visualisation of these borders as cartographic depiction on a map. Although this dataset depicts land borders, it is not nor does it suggests to be a legal definition of these borders. Therefore it cannot and must not be used for those use-cases pertaining to legal context.

This product is constructed by Geoscience Australia (GA), on behalf of the ICSM, from authoritative open data published by the land mapping agencies in their respective Australian state and territory jurisdictions. Construction of a nationally consistent dataset required harmonisation and mediation of data issues at abutting land borders. In order to make informed and consistent determinations, other datasets were used as visual aid in determining which elements of published jurisdictional data to promote into the national product. These datasets include, but are not restricted to: (i) PSMA Australia's commercial products such as the cadastral (property) boundaries (CadLite) and Geocoded National Address File (GNAF); (ii) Esri's World Imagery and Imagery with Labels base maps; and (iii) Geoscience Australia's GEODATA TOPO 250K Series 3. Where practical, Land Borders do not cross cadastral boundaries and are logically consistent with addressing data in GNAF.

It is important to reaffirm that although third-party commercial datasets are used for validation, which is within remit of the licence agreement between PSMA and GA, no commercially licenced data has been promoted into the product. Australian Land Borders are constructed exclusively from published open data originating from state, territory and federal agencies.

This foundation dataset consists of edges (polylines) representing mediated segments of state and/or territory borders, connected at the nodes and terminated at the coastline defined as the Mean High Water Mark (MHWM) tidal boundary. These polylines are attributed to convey information about provenance of the source. It is envisaged that land borders will be topologically interoperable with the future national coastline dataset/s, currently being built through the ICSM coastline capture collaboration program. Topological interoperability will enable closure of land mass polygon, permitting spatial analysis operations such as vector overly, intersect, or raster map algebra. In addition to polylines, the product incorporates a number of well-known survey-monumented corners which have historical and cultural significance associated with the place name.

This foundation dataset is constructed from the best-available data, as published by relevant custodian in state and territory jurisdiction. It should be noted that some custodians - in particular the Northern Territory and New South Wales - have opted out or to rely on data from abutting jurisdiction as an agreed portrayal of their border. Accuracy and precision of land borders as depicted by spatial objects (features) may vary according to custodian specifications, although there is topological coherence across all the objects within this integrated product. The guaranteed minimum nominal scale for all use-cases, applying to complete spatial coverage of this product, is 1:25 000. In some areas the accuracy is much better and maybe approaching cadastre survey specification, however, this is an artefact of data assembly from disparate sources, rather than the product design. As the principle, no data was generalised or spatially degraded in the process of constructing this product.

Some use-cases for this product are: general digital and web map-making applications; a reference dataset to use for cartographic generalisation for a smaller-scale map applications; constraining geometric objects for revision and updates to the Mesh Blocks, the building blocks for the larger regions of the Australian Statistical Geography Standard (ASGS) framework; rapid resolution of cross-border data issues to enable construction and visual display of a common operating picture, etc.

This foundation dataset will be maintained at irregular intervals, for example if a state or territory jurisdiction decides to publish or republish their land borders. If there is a new version of this dataset, past version will be archived and information about the changes will be made available in the change log.