The Digital Elevation Model Imagery Catalog layer describes precision elevation datasets acquired from LiDAR and aerial / satellite sensors currently archived in the department. Precision elevation products are defined as Digital Terrain Models (or bare Earth Digital Elevation Models) captured from either LiDAR sources or photogrammetrically derived from aerial photography. LiDAR classified point clouds and derived Digital Terrain Models under a CC-BY license have been uploaded to the ELVIS Elevation and Depth Online Portal (https://elevation.fsdf.org.au/).

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

Topography. 30 metre Digital Elevation Model (DEM). This layer was merged, clipped and reprojected by CeRDI (Federation University Australia). A coloured-relief map was generated and rendered as an RGB GeoTIFF. Elevation data originally sourced from Geoscience Australia's Elevation Information System (ELVIS).

The National Digital Elevation Model (DEM) 1 Second Hydrologically Enforced product, derived from the National DEM SRTM 1 Second and National Watercourses, lakes and Reservoirs.

This collection provides a seamlessly merged, hydrologically robust Digital Elevation Model (DEM) for the Murray Darling Basin (MDB), Australia, at 5 m and 25 m grid cell resolution.

This composite DEM has been created from all the publicly available high resolution DEMs in the Geoscience Australia (GA) elevation data portal Elvis (https://elevation.fsdf.org.au/) as at November 2022. The input DEMs, also sometimes referred to as digital terrain models (DTMs), are bare-earth products which represent the ground surface with buildings and vegetation removed. The DEMs were either from lidar (0.5 to 2 m resolution) or photogrammetry (5 m resolution) and totalled 852 individual DEMs.

The merging process involved ranking the DEMs, pairing the DEMs with overlaps, and adjusting and smoothing the elevations of the lower ranked DEM to make the edge elevations compatible with the higher-ranked DEM. This method is adapted from Gallant 2019 with modifications to work with hundreds of DEMs and have a variable number of gaussian smoothing steps.

Where there were gaps in the high-resolution DEM extents, the Forests and Buildings removed DEM (FABDEM; Hawker et al. 2022), a bare-earth radar-derived, 1 arc-second resolution global elevation model was used as the underlying base DEM. FABDEM is based on the Copernicus global digital surface model.

Additionally, hillshade datasets created from both the 5 m and 25 m DEMs are provided.

Note: the FABDEM dataset is available publicly for non-commercial purposes and consequently the data files available with this Collection are also available with a Creative Commons NonCommercial ShareAlike 4.0 Licence (CC BY-NC-SA 4.0). See https://data.bris.ac.uk/datasets/25wfy0f9ukoge2gs7a5mqpq2j7/license.txt Lineage: For a more detailed lineage see the supporting document Composite_MDB_DEM_Lineage.

DATA SOURCES 1. Geoscience Australia elevation data (https://elevation.fsdf.org.au/) via Amazon Web Service s3 bucket. Of the 852 digital elevation models (DEMs) from the GA elevation data portal, 601 DEMs were from lidar and 251 were from photogrammetry. The latest date of download was Nov 2022. The oldest input DEM was from 2008 and the newest from 2022.

1. FABDEM - Forests and buildings removed DEM based on the 1 arc-second Copernicus global digital surface model. Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., Neal, J., 2022. A 30 m global map of elevation with forests and buildings removed. Environ. Res. Lett. 17, 024016. https://doi.org/10.1088/1748-9326/ac4d4f

METHODS Part I. Preprocessing The input DEMs were prepared for merging with the following steps: 1. Metadata for all input DEMs was collated in a single file and the DEMs were ranked from finest resolution/newest to coarsest resolution/oldest 2. Tiled input DEMs were combined into single files 3. Input DEMs were reprojected to GA LCC conformal conic projection (EPSG:7845) and bilinearly resampled to 5 m 4. Input DEMs were shifted vertically to the Australian Vertical Working Surface (AVWS; EPSG:9458) 5. The input DEMs were stacked (without any merging and/or smoothing at DEM edges) based on rank so that higher ranking DEMs preceded the lower ranking DEMs, i.e. the elevation value in a grid cell came from the highest rank DEM which had a value in that cell 6. An index raster dataset was produced, where the value assigned to each grid cell was the rank of the DEM which contributed the elevation value to the stacked DEM (see Collection Files - Index_5m_resolution) 7. A metadata file describing each input dataset was linked to the index dataset via the rank attribute (see Collection Files - Metadata)

Vertical height reference surface https://icsm.gov.au/australian-vertical-working-surface

Part II. DEM Merging The method for seamlessly merging DEMs to create a composite dataset is based on Gallant 2019, with modifications to work with hundreds of input DEMs. Within DEM pairs, the elevations of the lower ranked DEM are adjusted and smoothed to make the edge elevations compatible with the higher-ranked DEM. Processing was on the CSIRO Earth Analytics and Science Innovation (EASI) platform. Code was written in python and dask was used for task scheduling.

Part III. Postprocessing 1. A minor correction was made to the 5 m composite DEM in southern Queensland to replace some erroneous elevation values (-8000 m a.s.l.) with the nearest values from the surrounding grid cells 2. A 25 m version of the composite DEM was created by aggregating the 5m DEM, using a 5 x 5 grid cell window and calculating the mean elevation 3. Hillshade datasets were produced for the 5 m and 25 m DEMs using python code from https://github.com/UP-RS-ESP/DEM-Consistency-Metrics

Part IV. Validation Six validation areas were selected across the MDB for qualitative checking of the output at input dataset boundaries. The hillshade datasets were used to look for linear artefacts. Flow direction and flow accumulation rasters and drainage lines were derived from the stacked DEM (step 5 in preprocessing) and the post-merge composite DEM. These were compared to determine whether the merging process had introduced additional errors.

OUTPUTS 1. seamlessly merged composite DEMs at 5 m and 25 m resolutions (geotiff) 2. hillshade datasets for the 5 m and 25 m DEMs (geotiff) 3. index raster dataset at 5 m resolution (geotiff) 4. metadata file containing input dataset information and rank (the rank column values link to the index raster dataset values) 5. figure showing a map of the index dataset and 5m composite DEM (jpeg)

DATA QUALITY STATEMENT Note that we did not attempt to improve the quality of the input DEMs, they were not corrected prior to merging and any errors will be retained in the composite DEM.

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These gully mapping datasets were developed using an algorithm that exploits the topographic signature of gullies to map them across multiple scales. It uses high-resolution (~1 m) airborne LiDAR topography data to map gullies and areas susceptible to gully erosion. The LiDAR datasets used were collected as part of the Reef Trust Gully and Stream Bank Erosion Control Program and cover ~7 000 square kilometres of Great Barrier Reef catchments. For each catchment with suitable data two independent datasets (existing gullies and areas at risk of gullying) are available. These two independent datasets enable comparison between current and future potential gully erosion that may help to prioritise gully remediation works. The data format is GeoTIFF, compatible with most GIS software. Lineage: The input topography data was captured as part of the Reef Trust 3D Terrain Mapping Services project. The data are available on the Elvis - Elevation and Depth - Foundation Spatial Data data portal (including metadata for the LiDAR products used).

Processing of the data was done using the algorithm described in Walker et al. 2020 (https://doi.org/10.1016/j.geomorph.2020.107115).

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For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). Methods

1. INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limited human intervention. Sloan et al. (2023) present a map indicating the various areas of Equatorial Asia from which these images were sourced.
IMAGE NAMING CONVENTION A common naming convention applies to satellite images’ file names: XX##.png where:

XX – denotes the geographical region / major island of Equatorial Asia of the image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

1. INTERPRETING ROAD FEATURES IN THE IMAGES For each of the 200 input satellite images, its road was visually interpreted and manually digitized to create a reference image dataset by which to train, validate, and test AI road-mapping models, as detailed in Sloan et al. (2023). The reference dataset of road features was digitized using the ‘pen tool’ in Adobe Photoshop. The pen’s ‘width’ was held constant over varying scales of observation (i.e., image ‘zoom’) during digitization. Consequently, at relatively small scales at least, digitized road features likely incorporate vegetation immediately bordering roads. The resultant binary (Road / Not Road) reference images were saved as PNG images with the same image dimensions as the original 200 images.

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

The 200 satellite images and the corresponding 200 road-reference images were both subdivided (aka ‘sliced’) into thousands of smaller image ‘tiles’ of 256x256 pixels each. Subsequent to image subdivision, subdivided images were also rotated by 90, 180, or 270 degrees to create additional, complementary image tiles for model development. In total, 8904 image tiles resulted from image subdivision and rotation. These 8904 image tiles are the main data of interest disseminated here. Each image tile entails the true-colour satellite image (256x256 pixels) and a corresponding binary road reference image (Road / Not Road).
Of these 8904 image tiles, Sloan et al. (2023) randomly selected 80% for model training (during which a model ‘learns’ to recognize road features in the input imagery), 10% for model validation (during which model parameters are iteratively refined), and 10% for final model testing (during which the final accuracy of the output road map is assessed). Here we present these data in two folders accordingly:

'Training’ – contains 7124 image tiles used for model training in Sloan et al. (2023), i.e., 80% of the original pool of 8904 image tiles. ‘Testing’– contains 1780 image tiles used for model validation and model testing in Sloan et al. (2023), i.e., 20% of the original pool of 8904 image tiles, being the combined set of image tiles for model validation and testing in Sloan et al. (2023).

IMAGE TILE NAMING CONVENTION A common naming convention applies to image tiles’ directories and file names, in both the ‘training’ and ‘testing’ folders: XX##_A_B_C_DrotDDD where

XX – denotes the geographical region / major island of Equatorial Asia of the original input 1920x886 pixel image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

A, B, C and D – can all be ignored. These values, which are one of 0, 256, 512, 768, 1024, 1280, 1536, and 1792, are effectively ‘pixel coordinates’ in the corresponding original 1920x886-pixel input image. They were recorded within the names of image tiles’ sub-directories and file names merely to ensure that names/directory were uniquely named)

rot – implies an image rotation. Not all image tiles are rotated, so ‘rot’ will appear only occasionally.

DDD – denotes the degree of image-tile rotation, e.g., 90, 180, 270. Not all image tiles are rotated, so ‘DD’ will appear only occasionally.

Note that the designator ‘XX##’ is directly equivalent to the filenames of the corresponding 1920x886-pixel input satellite images, detailed above. Therefore, each image tiles can be ‘matched’ with its parent full-scale satellite image. For example, in the ‘training’ folder, the subdirectory ‘Bo12_0_0_256_256’ indicates that its image tile therein (also named ‘Bo12_0_0_256_256’) would have been sourced from the full-scale image ‘Bo12.png’.

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Remotely sensed topographic (elevation) and bathymetric (depth) information were acquired for the NSW coast (Point Danger to Cape Howe) and southern Queensland (Palm Beach to Point Danger) using Airborne LiDAR Bathymetry (ALB - a combination of Light Detection And Ranging (LiDAR) and Laser Airborne Depth Sounding (LADS) sensors) during July – December 2018. Data were acquired by Fugro Pty Ltd on behalf of NSW Office of Environment and Heritage using a Riegl VQ-820-G ALB (LiDAR) and Fugro LADS High-Definition sensors aboard sub-contracted Corporate Air Cessna C441 (VH-VEH). Funding was provided through the NSW Coastal Reforms package. The objective of the project was to provide high-resolution data better than 3-5 m spaced soundings (0.5 m spot spacing terrestrial; 3.4 m spot spacing marine) from the mean high-water mark to ~200m inland, and from the shore, seaward (LADS - bathymetry) to the point of laser extinction (~20-40m water depth depending on in-water conditions). Positioning data were collected on the ellipsoid ITRF 2014 GRS80 in UTM Z56 and post-processed using local base stations (CORSnet NSW) to provide a Post Processed Kinematic GNSS solution for final aircraft trajectory before being applied to all data. The final data Geotif products are provided on the * Geosciences Australia ELVIS website .They are combined gridded terrestrial (elevation) and subtidal marine (bathymetry) data at 5 x 5 m (horizontal resolution) Geotifs exported using ESRI ArcMap from rasters (weighted average of clean soundings) in GDA 2020 (horizontal datum) to Australian Height Datum (vertical datum) and vertical precision to International Hydrographic Order (IHO) 1B. Data covers an area of 6862 km2 provided in 48 sub-datasets the extents of which are generally defined in their alongshore extent by the boundaries of NSW Secondary Sediment Compartments (Geosciences Australia). Other data outputs will include raw and classified LAS format files, aerial imagery and raw seabed reflectance data to be made available shortly on the ELVIS website. Data packages containing Arc Grids (topo-bathy, contours), XYZ, KMZ, tif, pdf maps and Fledermaus SD files will be made publicly available via the AODN (* Australian Ocean Data network ).\r Metadata, data quality statements and a geographical data coverage ArcGIS shapefile are available via SEED . The data are intended to inform coastal and marine management and should not be used for navigation without additional processing.

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