The Globe is deprecated software and as such Spatial Services no longer support the Google Earth platform. Spatial Services have developed a new platform, the Spatial Hosting Portal, which will allow you to search and discover data.

The NSW Foundation Spatial Data Framework data can be found on the Spatial Collaboration Portal

There have been a number of requests for "State Roads" data. This data is currently available via the Data.NSW Spatial Collaboration Portal.

To access Road Segment Data please follow the instructions below;

1. From https://portal.spatial.nsw.gov.au/
2. Click the Browse Data tile
3. Click NSW Data themes tile
4. Click the Transport icon
5. Click the link NSW Transport Theme - Road Name Extent

! homepage

1. Click on Export Data (highlighted above)

! Export Page

1. Do you want to select which layers to export? You can select Yes and choose from the layers provided OR you can select No Thanks. I want to export data from all layers. This will download all data sets. In the example below RoadNameExtent is selected.

! Layers to Export Page

1. Click Next >

2. Do you want to specify an extent? Select No or Yes, by drawing the extent on a map. If you select ‘No’ all the data will be extracted. If you wanted to specify an extent of data extraction, e.g. around Sydney in the screenshot below, use the square icon labelled ‘Draw a rectangle’. This will draw a square centred on the point where you clicked on the map. You can change the shape from the icon labelled ‘Reshape’ to get the required area of extraction.

! Map Selection View

1. Click on Next >

2. Select your preferred Export format. Please note the limitation of ESRI Shape files truncating attribute names to 10 characters.

3. Select your preferred Export datum: Please note the current preference of GDA2020, although that depends on your objective.

4. Select your preferred Export coordinate system: ‘Geographic’ will export the geometries in latitude/longitude. MGAxx coordinate systems will export the geometries in metres.

5. Type your email address

! Attribute Selection

1. Click Export

2. You should see the screen below

! Confirmation Screen

1. Check your inbox for an email from Customer Service with a link to download the data.

This data is currently available via the Data NSW Spatial Collaboration Portal. Road Name Extent is a line feature class representing aggregated road segments that have the same local road name and the segments are relatively contiguous. The data is held an maintained with the Foundation Spatial Data Framework Transport Theme.

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NSW Imagery Theme

Note: Export function is for the Mosaic Index only

Raster format in currently not delivered via the Spatial Collaboration Portal and this is view only service.
For all imagery supply enquires please contact us via the 'https://aus01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fspatialservices.createsend1.com%2Ft%2Ft-l-mhkddut-l-y%2F&data=04%7C01%7CKate.Wilkinson%40customerservice.nsw.gov.au%7Cfacc21fd20ad4d8bc46808d918f12734%7C1ef97a68e8ab44eda16db579fe2d7cd8%7C0%7C0%7C637568246339024087%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=lCsQT7upTxYnH3%2FwHLqpPWtfLT2%2BViKVsy%2FQijmUCEk%3D&reserved=0' rel='nofollow ugc'>Spatial Services Customer Hub

Please Note
WGS 84 service aligned to GDA94
This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments.
In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new 'multiCRS' functionality.

Access API Access 5m DEM Service Access NSW Elevation Service Access ELVIS PlatformNSW Elevation and Depth Theme Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS …Show full description Access API Access 5m DEM Service Access NSW Elevation Service Access ELVIS PlatformNSW Elevation and Depth Theme Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally.Elevation and Depth is the measurement of the Earth’s surface above or below a vertical datum to obtain the height of the land. Data is collected using a range of sensors including: laser, sonar, radar and optical. Technical methodologies are used to derive spot heights, raster surfaces, contours, triangulated irregular networks and digital elevation models. Datasets that form the Elevation and Depth theme include: Historical Contours (2m Urban, 10m and 20m) Current 2m Contours (State wide) Spot Heights Relative Heights Point cloud (LiDAR and Photogrammetrically derived) (available for download from Geoscience Australia ELVIS Platform) Digital Elevation Model (available for download from Geoscience Australia ELVIS Platform) Elevation Data Sets available from additional services. ELVIS – Elevation and Depth Point Clouds - The point cloud data set consists of point clouds captured from LiDAR (Light Detection and Ranging) and derived from airborne imagery using photogrammetric techniques. Spatial Services Point Cloud data is available for on demand download from Geoscience Australia ELVIS Platform. Digital Elevation Models - Digital Elevation Models (DEM) are derived from Spatial Services’ (SS) point cloud data. The DEM is a bare earth representation of the earth’s surface where all the above ground feature has been removed. Spatial Services have a number of different Digital Elevation Models Digital Elevation Model derived from LiDAR - Are 1m or 2m resolution and is not hydrologically enforced (breaklines) or hydrologically conditioned (identification and analysis of sinks). Digital Elevation Model derived Photogrammetry - Data is 5m resolution. Areas of no data caused by steep slopes, shadow and vegetation have been interpolated or filled-in with another data source and will not be as accurate as the bare open ground areas. The data is not hydrologically enforced (breaklines) or hydrologically conditioned (identification and analysis of sinks). Spatial Services Digital Elevation Model data is available for on demand download from. Geoscience Australia ELVIS Platform as 2km x 2km tiles. You can also access the 5 Metre Digital Elevation Model Service in the Collaboration Portal. Elevation and Depth provides an accurate representation of the Earth’s surface enabling evidence-based decision making, 3D modelling, planning and earth surface representation. Elevation and Depth underpins: · Safe hydrographic · Aeronautical and road navigation · Climate science, including climate change adaptation · Emergency management and natural hazard risk assessment · Environmental, including water management · Engineering projects and infrastructure development · Definition of maritime and administrative boundaries · Natural resource exploration. Update frequencies vary for each dataset. Individual current status can be found under each Spatial data profile. The objective is to maintain elevation datasets to meet the FDSI requirements of key data users. Current programs include: · Aerial LiDAR capture program across NSW. · DEM and Point Cloud generation from photogrammetric techniques. Longer term programs include: · Update of contour data using updated DEM data generated from LiDAR and Photogrammetry. · Hydrological enforcement using improved surface models. Metadata Type Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets Elevation and Depth Theme of the Foundation Spatial Data Framework Accuracy The dataset maintains a positional relationship to, and alignment with, the drainage and topographic digital datasets. these data sets were captured primarily by digitising from the best available aerial photography at scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS 84 Geographic 2D WGS 84 Equivalent To GDA94 Spatial Extent Full State Standards and Specifications AS/NZS ISO 19115 - ANZLIC Metadata Profile Version 1.1 AS/NZS ISO 19131:2008 Geographic Information - Data product specifications OGC compliant Web Map Services (WMS) and Web Feature Services (WFS) Metadata for the relevant Spatial Services datasets complies with AS/NZS ISO 19115-2, ANZLIC Metadata Profile v1.1 and ISO 19139 Intergovernmental Committee on Surveying and Mapping (ICSM): Guidelines for Digital Elevation Data DCS Spatial Services: Elevation Data Products Specification and Description (LiDAR) DCS Spatial Services: Elevation Data Products Specification and Description (Airborne Photogrammetry) Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795 Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

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These datasets consist of soil maps generated to assess baselines, drivers and trends for soil health and stability within the NSW Regional Forest Agreement (RFA) regions. The maps are organised into empirical soil maps, digital soil maps, and data cube maps. Empirical soil maps consists of four products. Maps include topsoil pH, carbon, Emerson Aggregate Stability and Soil Profile Quality Confidence. Each map consists of 2,162 units. Maps were generated using the most representative soil profile for each unit available within the Soil and Land Information System (SALIS). The 2008 woody vegetation coverage was used as baseline. Maps reflect values when the sampling occurred with temporal changes not being accounted for. Locations with missing or of poor quality data are identified, providing a confidence rating map as part of the evaluation process. Digital soil maps include map products of key soil condition indicators covering the Regional Forest Agreement regions of eastern NSW. Raster maps of key soil indicators, such as soil carbon, pH, bulk density, hillslope erosion and others, were created at 100 m resolution. For each key soil indicator, maps include baseline (approximately 2008) levels as well as trends of change resulting from different human and natural disturbances such as forest harvesting, uncontrolled stock grazing, climate change and bush fire. Data cube maps include time series of soil organic carbon (SOC) between January 1990 and December 2020 for the Regional Forest Agreement regions of eastern NSW. Products provide estimates of SOC concentrations and associated trends through time. Modelling was carried out using a data cube platform incorporating machine learning space-time framework and geospatial technologies. Important covariates required to drive this spatio-temporal modelling were identified using the Recursive Feature Elimination algorithm (RFE). A web mapping application on the NSW Spatial Collaboration Portal depicts these datasets. Access the webapp through the link below: https://portal.spatial.nsw.gov.au/portal/home/item.html?id=af9c71935f024f4a8f64cb39f5eba007

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These datasets were produced as part of a study undertaken by the University of Newcastle, commissioned by the NSW Natural Resources Commission. The study produced a report, titled ‘Retrospective Analysis of Forest Structure Change: ALS Data Comparison and Interpretation’.

These datasets are part of a web application on the Spatial Collaboration Portal, accessible through the below URL:

https://portal.spatial.nsw.gov.au/portal/apps/experiencebuilder/experience/?id=7ab99290b6514fed880df16af1fcc7e6

Metadata Portal Metadata Information

The NSW Forest Monitoring Steering Committee commissioned the University of Melbourne to deliver baselines, drivers and trends for water quality and quantity in the NSW Regional Forest Agreement (RFA) regions. Following this work, the University of Melbourne was asked to extend the analysis to cover all NSW forested catchments. Both the initial project (RFA regions) and the extension (all NSW forested catchments) are included in this publication. This dataset contains the estimated Mann-Kendall trends (direction and significance) in seven water quality and six water quantity indicators. The trends were estimated using a temporal regression that included a linear trend, the flow effect, a seasonality component and a lag-1 autoregressive residual model for which water quality data were sampled at daily or higher frequencies. For each water quality variable, trends were estimated for catchments which have 50% catchment area covered by forest, and long-term data monitored at the outlet of each catchment. All trends were estimated with the full historical records of each variable at each catchment in RFA regions, and the extension across all NSW forested catchments also produced short term trends. More detailed metadata for each dataset is included. The seven quality indicators are: total phosphorus (TP), total nitrogen (TN), dissolved oxygen (DO), pH, electrical conductivity (EC), turbidity and water temperature (WTemp). The six quantity indicators are: annual flow, annual rainfall-runoff residual, annual high flow, annual low flow, annual 7-day (7d) low flow and annual cease to flow (CTF). Water monitoring sites analysed included those from the WaterNSW, Bureau of Meteorology, Water Data Online (BoM WDO) and Forestry Corporation NSW (FCNSW). A web mapping application on the NSW Spatial Collaboration Portal depicts these datasets. Access the webapp through the link below: https://portal.spatial.nsw.gov.au/portal/home/item.html?id=03950cf226ac4d459b8c8e3631e17afb

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NSW Property Address History Service

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This dataset contains a series of spatial outputs describing probabilistic species predictive occupancy (Species Occupancy Models, or SOM) & habitat suitability (Maximum Entropy, or Maxent) surfaces, the underlying data used to calculate these models & model projections predicting the impact of climate change on flora Maxent surfaces.
Model outputs are combination outputs dependent on known species occurrence in the landscape, the species relationship with environmental variables (covariates) such as temperature, rainfall and topography; and its predicted occurrence based on covariate analysis. Maxent models do not predict actual occupancy, but rather habitat suitability, while SOMs predict actual occupancy. confounding factors such as inter-species competition, geographical barriers and disturbance events play a significant role in species occurrence, and are not considered in Maxent or SOM. Flora Maxent climate change projections used NSW and Australian Regional Climate Modelling (NARCliM) variables to predict habitat suitability for a baseline year 2000 and projections for 2030 and 2070.
Covariates, Fauna & Flora survey records used to create the models are included.
More detailed information regarding each model, its processes and outputs are included in the dataset.
A web mapping application on the NSW Spatial Collaboration Portal depicts Maxent & SOM of a selected group of vulnerable Flora & Fauna from this dataset. Access the webapp through the link below:
https://portal.spatial.nsw.gov.au/portal/home/item.html?id=78e6ae3d34aa45d2b8118fd0308d6459

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Origininally data is sourced from https://data.gov.au/data/dataset/accc-mobile-infrastructure-report-data-release.

In order to load the data in the portal, some coversion and manioulation are done. Hence it is possible to have mismatches between sources data and the published in the spatial collaboration portal.

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