This dataset features a collection of historical orthorectified aerial photographed images of the Brisbane City Council local government area captured by piloted aircraft during 1946.Prior to satellite imagery, extensive use was made of aerial photography to capture land information. The 1946 imagery service uses the Geocentric Datum of Australia 1994 (GDA94) datum and is projected in Zone 56 of the Map Grid of Australia (MGA56).This dataset is a tile layer, to view the images or to access the data, use the ArcGIS Hub, HTML and API links in the Data and resources section below.

Aerial image (true ortho) mosaic of the City of Melbourne municipal area. The true ortho image has been corrected to provide a true ‘top down’ view of the city, removing building lean and other interference typically found in oblique aerial imagery. The aerial image was captured May 2020 and is available for download in georeferenced format (JPEG2000). Capture Information - Capture Date: May 2020 - Capture Pixel Size: 10cm ground sample distance - Map Projection: MGA 2020 Zone 55 – 11 cm absolute accuracy Limitations:

Whilst every effort is made to provide the data as accurate as possible, the content may not be free from errors, omissions or defects. Preview Image: See an example image showing the data quality of the aerial:

Download: Download the aerial image data as a zipped .jpg2000 file. (45GB)

This Hub contains information, resources and discovery of Commonwealth Historical Aerial Photography across Australia. Geoscience Australia has developed the Historical Aerial Photography (HAP) collection in an online data delivery system. Using the application, uses can search and download the commonwealth photography collection for free. The hub demonstrates the breadth of the collection and showcases the efforts in collecting and curating an extensive physical collection of film and documents.

Geoscience Australia has the most extensive historical aerial photography collection in terms of land coverage and time (from 1928-1996). This online catalogue provides means of easy search of the collection records. The mapping system allows users to understand what information is available and, if digitised, to preview and download the image data.

The application contains a map which users can search areas, current location or an area of interest, as well as customize the search criteria (date range, film number etc). The search results list the available aerial photography or flight line diagram, and if is available for direct download for free.

Geoscience Australia has the most extensive historical aerial photography collection in terms of land coverage and time (from 1928-1996). This online catalogue provides means of easy search of the collection records. The mapping system allows users to understand what information is available and, if digitised, to preview and download the image data.

The application contains a map which users can search areas, current location or an area of interest, as well as customize the search criteria (date range, film number etc). The search results list the available aerial photography or flight line diagram, and if is available for direct download for free.

Orthorectified mosaicked black & white images from the Aerial Film Archive covering the greater metropolitan area of Adelaide including the Fleurieu Peninsula and parts of the South Australian Murray Darling Basin. Aerial photography was originally captured between January and March in 1949 using an Eagle 4 camera. Imagery is available for the following regions: Adelaide, Adelaide Airport, Gawler, Glenelg, Lefevre Peninsula Port Adelaide, Murray Mouth, Victor Harbor.

Commonwealth historical aerial photography search application embedded. This application allows users to search a map of Australia for nearly a century of aerial photography in our database and download scanned flight diagrams and photographs for free.

This dataset features a collection of historical orthorectified aerial photographed images of the Brisbane City Council local government area captured by piloted aircraft during 1946.Prior to satellite imagery, extensive use was made of aerial photography to capture land information. The 1946 imagery service uses the Geocentric Datum of Australia 1994 (GDA94) and is projected in Zone 56 of the Map Grid of Australia (MGA56).This dataset utilises Brisbane City Council's Open Spatial Data website to provide additional features for viewing and downloading the data.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

This public base layer imagery is a whole of State layer configured as a flattened and seamless single service. It can be used as a background in conjunction with other data services to provide a spatial reference. Locate uses the WA Now imagery service and comprises of 1:100k aerial imagery tiles, aboriginal community, regional and town site mosaics, as well as the Perth Metro 'metropolitan' mosaic. This service, particularly the large scale imagery (ie Perth metro, town sites, aboriginal communities) is a delayed deployment and a minimum of 300 days older than the subscription version of WA Now imagery. It is intended for non-commercial use only and not recommended for use by commercial entities or government agencies. © Western Australian Land Information Authority (Landgate) 2016. Access to Landgate’s publicly available data is subject to the terms and conditions of the SLIP Transaction - Personal Use Licence. © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.

Here we present the GIS dataset for the surficial geology map for the Vestfold Hills, East Antarctica. On the coast of Prydz Bay, the region is one of the largest ice-free areas in Antarctica. Surficial geology mapping at 1:2000 was undertaken with field observations in the 2018/19 and 2019/20 summer seasons as well as aerial photography and satellite imagery interpretation. Units are based on the Geological Survey of Canada Surficial Data Model Version 2.4.0 (Deblonde et al 2019).

This geodatabase, set of layer files (including sample and field observation sites), and metadata statement complement the flat pdf map published in 2021 - https://pid.geoscience.gov.au/dataset/ga/145535.

An ArcGIS shapefile layer showing the extent of all extant and relic Adelie penguin (Pygoscelis adeliae) colonies at Whitney Point, Windmill Islands, February 2006. The field 'Status' describes each polygon as extant, relic or maximum. Extant refers to the area used by breeding birds in the summer 2005/06. Maximum refers to the historic maximal extent of the colony. Relic refers to any colony which was not occupied by any breeding pairs during 2005/06.

Positional accuracy is approx. 1-2 m, after accounting for dGPS errors and errors in identification of the boundaries of colonies. Mapping was conducted after the end of the breeding season, so boundaries were identified as the extent of nest pebbles/fresh faeces, and it was considered that they could be reliably identified to within 0.5m.

Data were acquired using a Trimble Pro XH differential GPS. This work was completed as part of ASAC project 1219 (ASAC_1219).

Also for this project, three aerial photographs of Whitney point showing the adelie penguin colonies and taken on 17 December 1990 were georeferenced.

These aerial photographs are film ANTC1219 run 54 frames 21 to 23.

Work on this project also utilised a Digital Elevation Model (DEM) created for Shirley Island. See the metadata record, 'A digital elevation model (DEM) and orthophoto of the Whitney Point area of the Windmill Islands, Antarctica' for more information (linked below).

Since the 2005/06 summer was a low-ice year the opportunity was also taken to survey with differential GPS a section of coastline about 230 metres long east of Whitney Point on Clark Peninsula. This section of coastline was ice free and accessible. The data was collected with differential GPS on 10 February 2006.

This dataset collection contains A0 maps of the Keppel Island region based on satellite imagery and fine-scale habitat mapping of the islands and marine environment. This collection provides the source satellite imagery used to produce these maps and the habitat mapping data.

The imagery used to produce these maps was developed by blending high-resolution imagery (1 m) from ArcGIS Online with a clear-sky composite derived from Sentinel 2 imagery (10 m). The Sentinel 2 imagery was used to achieve full coverage of the entire region, while the high-resolution was used to provide detail around island areas.

The blended imagery is a derivative product of the Sentinel 2 imagery and ArcGIS Online imagery, using Photoshop to to manually blend the best portions of each imagery into the final product. The imagery is provided for the sole purpose of reproducing the A0 maps.

Methods:

The high resolution satellite composite composite was developed by manual masking and blending of a Sentinel 2 composite image and high resolution imagery from ArcGIS Online World Imagery (2019).

The Sentinel 2 composite was produced by statistically combining the clearest 10 images from 2016 - 2019. These images were manually chosen based on their very low cloud cover, lack of sun glint and clear water conditions. These images were then combined together to remove clouds and reduce the noise in the image.

The processing of the images was performed using a script in Google Earth Engine. The script combines the manually chosen imagery to estimate the clearest imagery. The dates of the images were chosen using the EOBrowser (https://www.sentinel-hub.com/explore/eobrowser) to preview all the Sentinel 2 imagery from 2015-2019. The images that were mostly free of clouds, with little or no sun glint, were recorded. Each of these dates was then viewed in Google Earth Engine with high contrast settings to identify images that had high water surface noise due to algal blooms, waves, or re-suspension. These were excluded from the list. All the images were then combined by applying a histogram analysis of each pixel, with the final image using the 40th percentile of the time series of the brightness of each pixel. This approach helps exclude effects from clouds.

The contrast of the image was stretched to highlight the marine features, whilst retaining detail in the land features. This was done by choosing a black point for each channel that would provide a dark setting for deep clear water. Gamma correction was then used to lighten up the dark water features, whilst not ove- exposing the brighter shallow areas.

Both the high resolution satellite imagery and Sentinel 2 imagery was combined at 1 m pixel resolution. The resolution of the Sentinel 2 tiles was up sampled to match the resolution of the high-resolution imagery. These two sets of imagery were then layered in Photoshop. The brightness of the high-resolution satellite imagery was then adjusting to match the Sentinel 2 imagery. A mask was then used to retain and blend the imagery that showed the best detail of each area. The blended tiles were then merged with the overall area imagery by performing a GDAL merge, resulting in an upscaling of the Sentinel 2 imagery to 1 m resolution.


Habitat Mapping:

A 5 m resolution habitat mapping was developed based on the satellite imagery, aerial imagery available, and monitoring site information. This habitat mapping was developed to help with monitoring site selection and for the mapping workshop with the Woppaburra TOs on North Keppel Island in Dec 2019.

The habitat maps should be considered as draft as they don't consider all available in water observations. They are primarily based on aerial and satellite images.

The habitat mapping includes: Asphalt, Buildings, Mangrove, Cabbage-tree palm, Sheoak, Other vegetation, Grass, Salt Flat, Rock, Beach Rock, Gravel, Coral, Sparse coral, Unknown not rock (macroalgae on rubble), Marine feature (rock).

This assumed layers allowed the digitisation of these features to be sped up, so for example, if there was coral growing over a marine feature then the boundary of the marine feature would need to be digitised, then the coral feature, but not the boundary between the marine feature and the coral. We knew that the coral was going to cut out from the marine feature because the coral is on top of the marine feature, saving us time in digitising this boundary. Digitisation was performed on an iPad using Procreate software and an Apple pencil to draw the features as layers in a drawing. Due to memory limitations of the iPad the region was digitised using 6000x6000 pixel tiles. The raster images were converted back to polygons and the tiles merged together.

A python script was then used to clip the layer sandwich so that there is no overlap between feature types.

Habitat Validation:

Only limited validation was performed on the habitat map. To assist in the development of the habitat mapping, nearly every YouTube video available, at the time of development (2019), on the Keppel Islands was reviewed and, where possible, georeferenced to provide a better understanding of the local habitats at the scale of the mapping, prior to the mapping being conducted. Several validation points were observed during the workshop. The map should be considered as largely unvalidated.

data/coastline/Keppels_AIMS_Coastline_2017.shp:
The coastline dataset was produced by starting with the Queensland coastline dataset by DNRME (Downloaded from http://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={369DF13C-1BF3-45EA-9B2B-0FA785397B34} on 31 Aug 2019). This was then edited to work at a scale of 1:5000, using the aerial imagery from Queensland Globe as a reference and a high-tide satellite image from 22 Feb 2015 from Google Earth Pro. The perimeter of each island was redrawn. This line feature was then converted to a polygon using the "Lines to Polygon" QGIS tool. The Keppel island features were then saved to a shapefile by exporting with a limited extent.

data/labels/Keppel-Is-Map-Labels.shp:
This contains 70 named places in the Keppel island region. These names were sourced from literature and existing maps. Unfortunately, no provenance of the names was recorded. These names are not official. This includes the following attributes:
- Name: Name of the location. Examples Bald, Bluff
- NameSuffix: End of the name which is often a description of the feature type: Examples: Rock, Point
- TradName: Traditional name of the location
- Scale: Map scale where the label should be displayed.

data/lat/Keppel-Is-Sentinel2-2016-19_B4-LAT_Poly3m_V3.shp:
This corresponds to a rough estimate of the LAT contours around the Keppel Islands. LAT was estimated from tidal differences in Sentinel-2 imagery and light penetration in the red channel. Note this is not very calibrated and should be used as a rough guide. Only one rough in-situ validation was performed at low tide on Ko-no-mie at the edge of the reef near the education centre. This indicated that the LAT estimate was within a depth error range of about +-0.5 m.

data/habitat/Keppels_AIMS_Habitat-mapping_2019.shp:
This shapefile contains the mapped land and marine habitats. The classification type is recorded in the Type attribute.

Format:

GeoTiff (Internal JPEG format - 538 MB)
PDF (A0 regional maps - ~30MB each)
Shapefile (Habitat map, Coastline, Labels, LAT estimate)

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Keppels_AIMS_Regional-maps

This layer contains information on the distribution and vegetation types of High Country Sphagnum Bogs and Fens in the ACT as defined by Nature Conservation (High Country Bogs and Associated Fens) Conservation Advice 2019 (Nature Conservation Act). The dataset includes areas of RAMSAR significance. The threatened Northern Corroboree Frogs are also associated with these ecosystems. Veg mapping was undertaken by Geoff Hope et al in 2009 and later revisited during mapping of ACT Vegetation Communities in 2015-2018 (Baines et al 2018).The ACT Scientific Committee determined that the ecological community High Country Bogs and Associated Fens is eligible for inclusion in the ACT Threatened Ecological Communities List in 2019. Most ACT High Country Bogs and Associated Fens communities are consistent with the nationally listed Alpine Sphagnum Bogs and Associated Fens ecological community.The mountains of the Australian Capital Territory support substantial areas of peat-forming mires in interfluves and valley heads, as well as areas of riparian fen vegetation along streams. While similar fens and bogs occur in the Snowy Mountains, the ACT represents a significant outlier of major biogeographic significance because the mires are near their climatic limits and hence sensitive to climate change.Mapping of the mires was originally completed by Hope et al 2009. The mapping was developed in three stages using orthorectified aerial photography and satellite imagery and extensive field checking. More information can be found at: Hope, G., Nanson, R. and Flett, I. 2009. Technical Report 19. The peat-forming mires of the Australian Capital Territory. Territory and Municipal Services, Canberra. https://www.act.gov.au/_data/assets/pdf_file/0011/2539595/19-peat-forming-mires-of-the-act-2009.pdfKey plant communities of ACT Bogs and Fens (Armstrong et al. 2012):• a2: Baeckea gunniana – Epacris paludosa – Richea continentis – Sphagnum cristatum Wet Heathland of the Australian Alps Bioregion (Alpine/subalpine Bog).• a7: Ranunculus pimpinellifolius – Gonocarpus micranthus herbfield of wetland heathland of the Australian Alps bioregion (Bog).• a8: Carex gaudichaudiana – Myriophyllum pedunculatum – Deschampsia caespitosa Sedgeland of the Australian Alps Bioregion (Alpine/subalpine Fen).• a9: Carex gaudichaudiana – Ranunculus amphitrichus – Phragmites australis Aquatic Herbfield of waterways in the Australian Alps and South-Eastern Highlands Bioregion (Montane Bogs and Fens).Other key plant communities (Hope et al. 2009):• Empodisma minus restiad Fen.• Phragmites – Typha tall sedgelands (Fen).Associated plant communities (Armstrong et al. 2012):• a14: Poa costiniana – Carex gaudichaudiana Subalpine Valley Grassland of the Australian Alps Bioregion (Alpine/subalpine Grasslands/Herbfields).• e59: Hakea microcarpa – Baeckea utilis – Leptospermum myrtifolium Subalpine Wet Heathland on Escarpment and Eastern Tableland Ranges of the South-Eastern Highlands Bioregion.• u193: Hakea microcarpa – Epacris breviflora – Epacris paludosa Montane Wet Heathland of the Australian Alps and western South-Eastern Highlands Bioregions.Note this product includes some areas of a33 vegetation community, which is formally considered to be associated with the threatened community (see ACT High Country Bogs and Fens Action Plan 2024, ACT Government). However, these areas were originally mapped by Hope et al 2009 with high altitude sphagnum bog (HSB) present.For more information on ACT High Country Bogs and Fens, visit https://www.act.gov.au/environment/animals-and-plants/act-threatened-species/high-country-bogs-and-associated-fensUpdates: Majority of the mapping was completed post 2003 fires. However, the dataset is was updated using Near Infrared Imagery 2015 and LiDAR data in 2019. Further updates will be implemented as required if new or better mapping of bog and fen areas become available.Fit for purpose: This dataset was captured at 1:3,000 scale. This dataset is fit for use as a tool for showing presence of bogs and fens in the ACT. Exact boundaries and extents will contract and expand throughout time. This should be considered if data use purpose requires accuracy of greater than 50m.Disclaimer: While all care is taken to ensure accuracy, the ACT Government does not warrant that the map is free from errors. © ACT Government

Taken from sections of the Report:

Introduction and Project Outline

The 2000/01 MAGIP field program for the Antarctic survey season has been scoped to continue and extend the objectives of the Mapping and GIS section of the Antarctic Division in support of the ANARE mapping program (ANAREMAGIP) as well as providing survey support for other ongoing ANARE science programs.

The field component of the program in the Davis/ Mawson area for this season was to primarily establish ground control for existing 1:30000 photography in the Larsemann Hills. Additional tasks included updating of station summaries and the retrieval of data from the tide gauges at Law Base, Mawson and Davis Stations.

The original Antarctic Division's Brief to Surveyors is included as Appendix A of this report.

David Hurd from the Survey and Geographic Information Services Group of Hydro Tasmania and Arthur Moerke, a volunteer assistant, have been the field operatives throughout the season.

The survey program consisted of the following major areas: * Photo control - Larsemann Hills * Completion of various tasks relating to the Davis tide gauge - Installation of second tide gauge at Davis - Downloading the existing tide gauge at Davis. - Timed water-level measurements. - Precise levelling connection of the tide gauge bench marks with the ARGN GPS site at Davis. * Completion of similar tasks described for the tide gauge at Davis Base with the gauge located at Law Base. * Lake levels within the Vestfold Hills. * Inspection and analysis of the reader board and pole at Deep Lake. * Providing GPS coordinates for all uncoordinated survey marks in the Vestfold Hills

Additionally other unscoped inclusions in the program included * Update of station summary for Mawson station * Engineering surveys at Davis, Zhongshan, Progress 2 and Law Base. * Completion of similar tasks described for the tide gauge at Davis Base with the gauge located at Mawson station.

Discussions with Mapping Officer Henk Brolsma prior to departure (26/9/00) agreed that the Auslig Surveyors working in the area this season would utilise their equipment to complete the height connections between the tide gauge benchmarks at Davis Station and AUS99 and to complete the level run as detailed in priority 2

Similarly with the height connection to the Law Base GPS station NMS278 and AUS99 at Davis.

The scope specified that GPS locations were to be processed relevant to the base station at Casey. It was assumed that this was an error and was intended to specify AUS99 at Davis.

Later discussions with the Auslig surveyors also lead to the extension of their involvement to include the tide gauge benchmark at Law Base and the benchmark located on the peak overlooking Law Base, C1.

Recommendations

Several recommendations can be made from the experiences over the summer.

It is understood that there is a differential GPS transmitter system at Davis, which is either not functioning or is turned off. During the stay at Davis we were advised that the expeditioners last winter spent days digging to locate a series of junction boxes that required repair. A GPS unit could be provided with accuracies capable of locating the assets in a very short time. The ability to locate these buried assets could have a significant effect on station life when considering the importance of quick repairs before anything freezes. A system for all station may be worthwhile as a safety measure.

Alternatively, the establishment of a more visible control network possibly in combination with cane line locations and the basic training of one or two expeditioners in the use of a theodolite and tape, which could stay on station, may enable relatively rapid relocation of junction boxes etc.

The tide gauge location was not possible at Mawson station because of the amount of algae/vegetation that accumulated beneath the ice. Also there was significant difficulty in accessing the gauge at Law Base because the ice had broken up but not out. It was fortunate that the weather conditions provided a pond which allowed for access. Assuming that a similar level of growth was present below the ice at Law Base prior to the break up then accessing the gauges in the future should possibly be based on a balance of growth conditions and the condition of the ice.

It is recommended that all poles related to the tide gauge at Davis be repainted annually to ensure that they are readily available when boating allows.

Considering the anticipated shutting down of the old tide gauge at Davis, the establishment of the new gauge may justify a higher priority listing. The installed mounting block for the new gauge has not been painted with anti-fouling paint and because of this will continually be subjected to weed growth. Perhaps it could be arranged so that the barge at Davis could be used to install the new gauge and a replacement mounting block in a single operation. Possibly it could be deployed adjacent to the unused, installed block.

Some form of shore mounted interface with the tide gauges would save significant amounts of time and avoid many logistical issues in regard to boat time and organisation of drivers. Law Base does not have boats or drivers so any future access to the Law Base gauge will need to either arrange for the transportation of these resources or ensure that they are in the area before the ice breaks up.

The timed water level measurements could be made more efficiently and over a longer period with the summer deployment and retrieval of some form of GPS buoy similar to what the university has been using. Alternatively the timed water level measurements using a video camera with timed exposures may be an option.

A handheld GPS with some form of computer interface would be a great asset in the location of benchmarks in the ice-free areas. Having a list of coordinates that could be loaded into a lightweight unit would allow for rapid location of marks prior to deployment of heavy equipment or confirmation of mark identification for levelling. This should be seen as highly desirable.

As mentioned in the 98/99 report the use of a digital camera for documentation purposes should be seen as essential in the antarctic environment.

Logistically, it would seem that registration in the ASAC system ensures that expeditioners are not left off lists and that equipment and resources are more easily available. While this did not represent any immoveable barriers, some form of registration may make things easier.

It seems that the levelling in the Vestfolds is given a low priority but is an ongoing project. The number of lakes levelled varies from season to season from around seventy to three or four. While the lakes are currently levelled on an opportunistic basis a suggestion may be to either place a higher priority on completing the extensive lake list or reduce the number of lakes to be levelled so that a continuous data set may be generated. I am under the impression that there has been a thesis written on the lake levels and maybe this could be used as a guide to which lakes should be levelled annually. Possible continuously levelling lakes throughout a season may also provide indications of movement throughout the season in addition to the annual snapshot. This could be particularly relevant in regard to accessing the deep lake reader board.

The rock drill battery is incapable of holding enough charge to complete more than three drill holes. It may be worthwhile to either recondition the battery or replace it.

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on the known details at the time of acquisition. This data was sourced from http://www.energyandresources.vic.gov.au/earth-resources/maps-reports-and-data/geovic. GeoVic is a free web mapping application which allows users to search geospatial databases and to display the results as maps or tables. GeoVic data layers include the following: mineral, petroleum and extractive industry tenements geological maps and interpretations at various scales land-use data aerial photography airborne geophysical survey boundaries gravity, magnetic and radiometric images borehole & well data surface geochemistry results mines and mineral occurrences renewable energy data (eg solar, geothermal and wind) This dataset contains the Traralgon Formation coal isopachs. ## Dataset History This data was sourced from http://www.energyandresources.vic.gov.au/earth-resources/maps-reports-and-data/geovic. ## Dataset Citation Victorian Department of State Development, Business and Innovation (2015) Traralgon Formation coal isopachs. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/05966613-8da7-4124-af6d-964e1210b6ba.

This is the linework for the detail soil survey of the FHARS. This soil survey is part of the infrastructure program for the research station. The soil survey guides field plot layouts and the siting of infrastructure. It also assists with the instrumentation of the site and is a baseline for temporal changes. Lineage: This soil survey follows the principals of a free survey (McKenzie & Grundy, 2008), a conventional qualitative methodology. Free survey is based on the establishment of a local soil classification, in this case using soil profile classes (Powell, 2008) as the taxonomic unit. Soil profile classes (SPC) were identified from soil profile descriptions of 138 soil cores taken on the research farm. Each core was up to 1.5 m in length and 0.049 m in diameter. The recognised soil profile classes were correlated with other existing local classifications, in this case the soil profile classes of the Soils of the Lockyer Valley Alluvial Plains (Powell et al., 2002). After soil profile classes were established for the research farm, the extent of each soil profile class on the research farm was mapped using primarily aerial photography as a base to the mapping. Soil boundaries were delineated using the 138 soil core observations made during field work and a large amount of other data. The soil boundary between mapping units is rarely an abrupt change (as shown on a map) but often represents a diffuse change over a large distance. Where a diffuse boundary is expected, the line has been placed near the centre of the change. Each map unit is represented by one soil profile class. This was possible given the density of the soil observations. 138 observations were made over 66 hectares, which is 1 observation per 0.5 hectare. This is equivalent to a 1: 10,000 scale ‘detailed’ soil survey (Gallant et al., 2008).

Ground-based readings of temperature and rainfall, satellite imagery, aerial photographs, ground verification data and Digital Elevation Model (DEM) were used in this study. Ground-based meteorological information was obtained from Bangladesh Meteorological Department (BMD) for the period 1977 to 2015 and was used to determine the trends of rainfall and temperature in this thesis. Satellite images obtained from the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) website (www.glovis.usgs.gov) in four time periods were analysed to assess the dynamics of mangrove population at species level. Remote sensing techniques, as a solution to lack of spatial data at a relevant scale and difficulty in accessing the mangroves for field survey and also as an alternative to the traditional methods were used in monitoring of the changes in mangrove species composition, . To identify mangrove forests, a number of satellite sensors have been used, including Landsat TM/ETM/OLI, SPOT, CBERS, SIR, ASTER, and IKONOS and Quick Bird. The use of conventional medium-resolution remote sensor data (e.g., Landsat TM, ASTER, SPOT) in the identification of different mangrove species remains a challenging task. In many developing countries, the high cost of acquiring high- resolution satellite imagery excludes its routine use. The free availability of archived images enables the development of useful techniques in its use and therefor Landsat imagery were used in this study for mangrove species classification. Satellite imagery used in this study includes: Landsat Multispectral Scanner (MSS) of 57 m resolution acquired on 1st February 1977, Landsat Thematic Mapper (TM) of 28.5 m resolution acquired on 5th February 1989, Landsat Enhanced Thematic Mapper (ETM+) of 28.5 m resolution acquired on 28th February 2000 and Landsat Operational Land Imager (OLI) of 30 m resolution acquired on 4th February 2015. To study tidal channel dynamics of the study area, aerial photographs from 1974 and 2011, and a satellite image from 2017 were used. Satellite images from 1974 with good spatial resolution of the area were not available, and therefore aerial photographs of comparatively high and fine resolution were considered adequate to obtain information on tidal channel dynamics. Although high-resolution satellite imagery was available for 2011, aerial photographs were used for this study due to their effectiveness in terms of cost and also ease of comparison with the 1974 photographs. The aerial photographs were sourced from the Survey of Bangladesh (SOB). The Sentinel-2 satellite image from 2017 was downloaded from the European Space Agency (ESA) website (https://scihub.copernicus.eu/). In this research, elevation data acts as the main parameter in the determination of the sea level rise (SLR) impacts on the spatial distribution of the future mangrove species of the Bangladesh Sundarbans. High resolution elevation data is essential for this kind of research where every centimeter counts due to the low-lying characteristics of the study area. The high resolution (less than 1m vertical error) DEM data used in this study was obtained from Water Resources Planning Organization (WRPO), Bangladesh. The elevation information used to construct the DEM was originally collected by a Finnish consulting firm known as FINNMAP in 1991 for the Bangladesh government.

The Bass Strait Digital Elevation Model (DEM) is a compilation of all available bathymetry data for the area of seabed between the coastlines of Victoria and northern Tasmania, extending approximately 460 km from west of King Island to east of Flinders Island. The Bass Strait is bounded by a continental slope incised with numerous canyons, including the prominent Bass Canyon on the eastern side. The region encompasses islands and exposed rocks, drowned paleo-shorelines and dunefields, fringed by a rugged coastline. Bathymetry mapping of the seafloor is vital for the protection of Bass Strait, allowing for safe navigation of shipping, improved environmental management and resource development. Australian Hydrographic Office-supplied ENC tile spot depths were used to develop the general bathymetry variation across the entire region. Shallow- and deep-water multibeam survey data reveal the complexity of the seafloor for the continental shelf and adjacent canyons which incise the western and eastern sides of Bass Strait. Airborne LiDAR bathymetry acquired by the Australian Hydrographic Office cover most of the northern Tasmanian nearshore and coast, with some coverage gaps supplemented by Landsat-8 satellite derived bathymetry data. The Geoscience Australia-developed Intertidal Elevation Model DEM improves the source data over the intertidal zone. Highly accurate photogrammetry coastline data developed for the Tasmania, Victoria and New South Wales coastlines, and Near Surface Feature data representing shoal features observable in aerial imagery, were used to improve the land/water interface of the numerous island and rock features. All source bathymetry data were extensively edited as 3D point clouds to remove noise, given a consistent WGS84 horizontal datum, and where possible, an approximate MSL vertical datum.

This dataset is not to be used for navigational purposes.

This dataset represents SCC's Recreational Trails, and thus the approximate location and basic attribution of known, recorded, SCC-owned or maintained trails within the Sunshine Coast LGA. Eligible asset types include formed footpaths, cycleways, shared paths, unformed (ie. unexcavated earth) pedestrian or cycle tracks/ trails, and marked road crossings (ie. zebra crossings – paint component only. This feature class also contains unmarked road crossings as non-asset,network connectors.Amongst other exclusions, this feature class does NOT include 'Bridge' asset category items (eg. boardwalks, footbridges, attached vehicle bridge paths), or 'Pedestrian Tunnel' asset class items (ie. pedestrian/ cycle tunnels and associated, internal pathway structure).This dataset was generated by various parties and methodologies, from 2009(ie. the earliest recorded creation date) to current. These include: External development stakeholders, providing ADAC-compliant, ‘As Constructed’ survey drawings or XML files for contributed assets; Internal SCC planning/ engineering staff, providing ADAC-compliant, ‘As Constructed’ survey drawings or XML files for capital (SCC-constructed) assets; Internal engineering/ asset information staff, providing GPS vertices or linework (eg. from digital aerial imagery) of assets.As at 18/02/2015, the entirety of records contained within this dataset were migratedand adapatedfrom existing corporate and non-corporate feature classes(ie. pub.SCC.TranRDvecPathwaysExistingand W:\Apps\geo\Tools\Mobile\Production\EO\Data\NaturalAreas_Working.gdb\Trails).Ongoing data collection is imported by SCC AIS staff, and managed within ESRI ArcGIS SDE database architecture.This dataset is to be considered a standalone layer.

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Drone Technology In Education Sector Market Size 2025-2029

The drone technology in education sector market size is forecast to increase by USD 1.47 billion at a CAGR of 26% between 2024 and 2029.

The Drone Technology in Education sector is experiencing significant growth, driven by the increasing emphasis on Science, Technology, Engineering, and Mathematics (STEM) education and the rising adoption of online retail channels for purchasing educational products. The integration of drone technology in education offers unique learning opportunities, particularly in fields such as engineering, geography, and environmental sciences. This trend is expected to continue as educators seek innovative ways to engage students and prepare them for future careers. However, the market is not without challenges. Safety concerns related to the operation of drones in educational settings remain a significant hurdle. As the demand for skilled drone operators rises, educational establishments are investing in smart education to provide students with valuable, future-ready skills.
Regulations governing the use of drones in educational institutions vary widely, and ensuring the safety of students, staff, and property is a top priority. Addressing these challenges will require collaboration between educators, regulatory bodies, and it providers to establish best practices and guidelines for safe and effective implementation of in education. Companies seeking to capitalize on this market opportunity must prioritize safety and regulatory compliance while delivering innovative and effective solutions to meet the evolving needs of educators and students. Unmanned aerial vehicles (UAVs), or drones, are increasingly being adopted In the education sector for security purposes due to rising concerns over campus safety.

What will be the Size of the Drone Technology In Education Sector Market during the forecast period?

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The market in the education sector in the United States is experiencing significant growth due to the increasing adoption of drones for various applications. Drones are being utilized for multispectral and thermal imaging, aerial videography, field data collection, and remote sensing applications, among others. The integration of drones in education is driven by drone-driven innovation and the availability of drone platforms for research projects. Drone safety protocols and certification standards are essential considerations in this market, ensuring safe and effective drone operations. Drones enable photography and video recording for wildlife monitoring, public service missions, and land surveillance methods.
Advancements include rotary-wing and fixed-wing drones, hybrid drones, and drone mapping capabilities. Hyperspectral imaging and data visualization tools further enhance the utility in education. The market is expected to continue growing as drone-based surveys and drone industry trends shape the future of this dynamic technology. These institutions provide e-learning, STEM classes, group sessions, private lessons, and flying courses, incorporating live video chats, multilingual teachers, and pilot certification.

How is this Drone Technology In Education Sector Industry segmented?

The drone technology in education sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Security surveillance
  Learning


End-user

  Higher education sector
  K-12 sector


Type

  Wing drones
  Multi-rotor drones
  Single rotor drones
  Hybrid drones 


Geography

  North America

    US
    Canada


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK


  South America

    Brazil


  Middle East and Africa

By Application Insights

The security surveillance segment is estimated to witness significant growth during the forecast period. The Drone Ecosystem in the education sector is witnessing significant growth due to the integration of emerging technologies such as Artificial Intelligence (AI), Software Developers, and Drone Simulation. This technological advancement is revolutionizing STEM Education by providing Hands-on Learning experiences in Data Analysis, Flight Planning, and Drone Safety. The adoption of Drone Technology is expanding beyond Aerial Photography to include Precision Agriculture, Infrastructure Inspection, GIS Mapping, Environmental Monitoring, Disaster Response, and Data Collection. Service Providers and Drone Manufacturers are collaborating to offer Drone Training, Pilot Certification, and Mission Control solutions. The use of Drone Technology is also fostering Career Development opportunities in areas like Drone Software, Computer Vision, Machin