This dataset shows the footprints of all structures within the City of Melbourne. A building footprint is a 2D polygon (or multi-polygon) representation of the base of a building or structure. The footprint is defined as the boundary of the structure where the walls intersect with the ground plane or podium, rather than an outline of the roof area (roofprint).

Where a building has a significant change in built form, multiple footprint polygons are ‘stacked’ vertically to define shape of the built form. This includes, and is not limited to:

- Tower
- Podium
- Setbacks/offsets

The Australian Height Datum (AHD) is the national vertical datum for Australia. The National Mapping Council adopted the AHD in May 1971 as the datum to which all vertical control mapping would be referred

The data was captured in May 2023.

An in-depth description of the Building Footprint GIS data layer outlining terms of use, update frequency, attribute explanations, and more.

This chipped training dataset is over Hpa-an and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,667 tiles and 44,765 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (1040010033320500). Dataset keywords: Urban, Peri-Urban, River.

Echo’s Building Footprint dataset maps 11M+ precise location boundaries across the UK, France, Spain, Italy, and Germany to power geospatial analysis.

Created using satellite imagery, machine learning, and human validation, these polygons define the exact footprint of stores, offices, and landmarks — enabling high-precision mapping and accurate foot traffic attribution.

Key data points include: - Polygon geometry linked to POIs - Location name, type, and coordinates - Commercial and amenity coverage - Verified, analysis-ready format - Available in major European markets

Ideal for commercial real estate, retail, and proptech teams optimizing site planning, mobility analytics, and investment strategies.

This chipped training dataset is over Manjama and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 4,671 tiles and 60,379 individual buildings. The satellite imagery resolution is 30 cm and was sourced from Maxar ODP (1040010056B6FA00). Dataset keywords: Urban, Peri-Urban.

This chipped training dataset is over Paris and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,027 tiles and 3,468 buildings. The original dataset was sourced from the SpaceNet 2 Dataset before the imagery was tiled down from 650x650 pixel chips and labels were revised to be consistent with the ramp datasets notion of rooftop as the building footprint. Dataset keywords: Urban, Dense.

This chipped training dataset is over Dhaka and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp fine-tune Dhaka Model and contains 11,905 tiles and 189,057 individual buildings. The satellite imagery resolution is 30 cm and sourced from Maxar ODP (BG_Dhaka_19Q3_V0_R6C3, ...R6C4, ...R3C2, ...R2C3, ...R3C4). Dataset keywords: Very Dense Urban, Rural, Agricultural, Forested.

This chipped training dataset is over Accra and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 1,330 tiles and 40,786 buildings. The original dataset was sourced from the Open Cities AI Challenge Dataset before the drone imagery was resampled to 30 cm and the labeled data were improved. Dataset keywords: Urban, Dense.

This chipped training dataset is over Jashore and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in the development and testing of a localized ramp model and contains 7,310 tiles and 80,050 individual buildings. The satellite imagery resolution is 35 cm and was sourced from Maxar ODP (104001003BA7C900). Dataset keywords: Urban, Peri-urban, Rural

Building footprint means the perimeter of a building at the outer edge of the outside walls of the building. Generated with digitizing of 2014 aerial imagery. Anticipated update 2021-2022. 1. Restriction on the use of Material on this websiteUsage and/or downloading this data indicates Your acceptance of the terms and conditions below.The data here controlled and operated by the Corporation of the County of Lennox and Addington (referred to the “County” herein) and is protected by copyright. No part of the information herein may be sold, copied, distributed, or transmitted in any form without the prior written consent of the County. All rights reserved. Copyright 2023 by the Corporation of the County of Lennox and Addington.2. DisclaimerThe County makes no representation, warranty or guarantee as to the content, accuracy, currency or completeness of any of the information provided on this website. The County explicitly disclaims any representations, warranties and guarantees, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose.3. Limitation of LiabilityThe County is not responsible for any special, indirect, incidental or consequential damages that may arise from the use of or the inability to use, any web pages and/or the materials contained on the web page whether the materials are provided by the County or by a third party. Without limiting the generality of the foregoing, the County assumes no responsibility whatsoever for: any errors omissions, or inaccuracies in the information provided, regardless of how caused; or any decision made or action taken or not taken by the reader or other third party in reliance upon any information or data furnished on any web page.The Data is provided "as is" without warranty or any representation of accuracy, timeliness or completeness. The burden for determining accuracy, completeness, timeliness, merchantability and fitness for or the appropriateness for use rests solely on the requester. Lennox and Addington County makes no warranties, express or implied, as to the use of the Data. There are no implied warranties of merchantability or fitness for a particular purpose. The requester acknowledges and accepts the limitations of the Data, including the fact that the Data is dynamic and is in a constant state of maintenance, corrections and update.

Echo’s Building Footprint dataset maps 38M+ precise location boundaries across the UK, France, Spain, Italy, and Germany to power geospatial analysis.

Created using satellite imagery, machine learning, and human validation, these polygons define the exact footprint of stores, offices, and landmarks — enabling high-precision mapping and accurate foot traffic attribution.

Key data points include: - Polygon geometry linked to POIs - Location name, type, and coordinates - Commercial and amenity coverage - Verified, analysis-ready format - Available in major European markets

Ideal for commercial real estate, retail, and proptech teams optimizing site planning, mobility analytics, and investment strategies.

This chipped training dataset is over Lubumbashi and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 2 dataset, meaning it has NOT been thoroughly reviewed and improved. This dataset was produced for the ramp project and contains 8,498 tiles and 148,459 individual buildings. The satellite imagery resolution is 30 cm and was sourced from Maxar ODP (1040010058041300). Dataset keywords: Urban, Peri-urban, Rural

Buildings_BACI

   File Geodatabase Feature Class


   Thumbnail Not Available

   Tags

   Buildings, structures, ruins, storage tanks, silos, water towers, Baltimore City Planimetric, Biophysical Resources, Land, Socio-Economic Resources, Capital




   Summary


   This data was created as a landbase feature as part of the planimetric data.


   Description


   This dataset represents photogrammetrically captured Building footprints => 100sq. ft. including storage tanks, silos, water towers, power plants, substations, and structures under construction and ruins. Feature capture rules:


   Buildings - Outline edge of roofline. All buildings shall be captured as polygons. In commercial areas especially, it is important that the plotted building represent the face of the building where it meets the sidewalk. Polygons shall be created for the outer boundary of the building when a partywall exists. Does not include sheds and small temporary structures. Attached garages shall be represented as part of the building structure. Large structures such as stadiums shall also be represented.

   Structures under construction or demolition - Delineate the rooflines of all buildings under construction as interpreted from aerial photography. If roofline is not visible compile visible foundation or walls

   Ruins - Delineate old overgrown areas of old structures that have been demolished or are in disrepair. Original data will be reclassified to define as separate subtype.

   Storage tanks, silos, and water towers - Outlines of all storage tanks, silos and water towers. . Original data will be reclassified to define as separate subtype.

   Power plants and substations - Outline of power plant and substation structure. . Original data will be reclassified to define as separate subtype.


   Credits

   There are no credits for this item.


   Use limitations


   Every reasonable effort has been made to ensure the accuracy of these data. The City of Baltimore, Maryland makes no representations nor warranties, either express or implied, regarding the accuracy of this information or its suitability for any particular purpose whatsoever. The data is licensed "as is" and the City of Baltimore will not be liable for its use or misuse by any party. Reliance of these data is at the risk of the user.


   Extent



   West -76.714715  East -76.525355 

   North 39.375162  South 39.193953 




   Scale Range

   There is no scale range for this item.

The historical settlement data compilation for Spain (HISDAC-ES) is a geospatial dataset consisting of over 240 gridded surfaces measuring the physical, functional, age-related, and evolutionary characteristics of the Spanish building stock. We scraped, harmonized, and aggregated cadastral building footprint data for Spain, covering over 12,000,000 building footprints including construction year attributes, to create a multi-faceted series of gridded surfaces (GeoTIFF format), describing the evolution of human settlements in Spain from 1900 to 2020, at 100m spatial and 5 years temporal resolution. Also, the dataset contains aggregated characteristics and completeness statistics at the municipality level, in CSV and GeoPackage format.!!! UPDATE 08-2023 !!!: We provide a new, improved version of HISDAC-ES. Specifically, we fixed two bugs in the production code that caused an incorrect rasterization of the multitemporal BUFA layers and of the PHYS layers (BUFA, BIA, DWEL, BUNITS sum and mean). Moreover, we added decadal raster datasets measuring residential building footprint and building indoor area (1900-2020), and provide a country-wide, harmonized building footprint centroid dataset in GeoPackage vector data format.File descriptions:Datasets are available in three spatial reference systems:HISDAC-ES_All_LAEA.zip: Raster data in Lambert Azimuthal Equal Area (LAEA) covering all Spanish territory.HISDAC-ES_IbericPeninsula_UTM30.zip: Raster data in UTM Zone 30N covering all the Iberic Peninsula + Céuta and Melilla.HISDAC-ES_CanaryIslands_REGCAN.zip: Raster data in REGCAN-95, covering the Canary Islands only.HISDAC-ES_MunicipAggregates.zip: Municipality-level aggregates and completeness statistics (CSV, GeoPackage), in LAEA projection.ES_building_centroids_merged_spatjoin.gpkg: 7,000,000+ building footprint centroids in GeoPackage format, harmonized from the different cadastral systems, representing the input data for HISDAC-ES. These data can be used for sanity checks or for the creation of further, user-defined gridded surfaces.Source data:HISDAC-ES is derived from cadastral building footprint data, available from different authorities in Spain:Araba province: https://geo.araba.eus/WFS_Katastroa?SERVICE=WFS&VERSION=1.1.0&REQUEST=GetCapabilitiesBizkaia province: https://web.bizkaia.eus/es/inspirebizkaiaGipuzkoa province: https://b5m.gipuzkoa.eus/web5000/es/utilidades/inspire/edificios/Navarra region: https://inspire.navarra.es/services/BU/wfsOther regions: http://www.catastro.minhap.es/INSPIRE/buildings/ES.SDGC.bu.atom.xmlData source of municipality polygons: Centro Nacional de Información Geográfica (https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)Technical notes:Gridded dataFile nomenclature:./region_projection_theme/hisdac_es_theme_variable_version_resolution[m][_year].tifRegions:all: complete territory of Spaincan: Canarian Islands onlyibe: Iberic peninsula + Céuta + MelillaProjections:laea: Lambert azimuthal equal area (EPSG:3035)regcan: REGCAN95 / UTM zone 28N (EPSG:4083)utm: ETRS89 / UTM zone 30N (EPSG:25830)Themes:evolution / evol: multi-temporal physical measurementslanduse: multi-temporal building counts per land use (i.e., building function) classphysical / phys: physical building characteristics in 2020temporal / temp: temporal characteristics (construction year statistics)Variables: evolutionbudens: building density (count per grid cell area)bufa: building footprint areadeva: developed area (any grid cell containing at least one building)resbufa: residential building footprint arearesbia: residential building indoor areaVariables: physicalbia: building indoor areabufa: building footprint areabunits: number of building unitsdwel: number of dwellingsVariables: temporalmincoy: minimum construction year per grid cellmaxcoy: minimum construction year per grid cellmeancoy: mean construction year per grid cellmedcoy: median construction year per grid cellmodecoy: mode (most frequent) construction year per grid cellvarcoy: variety of construction years per grid cellVariable: landuseCounts of buildings per grid cell and land use type.Municipality-level datahisdac_es_municipality_stats_multitemporal_longform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in long form. Note that a value of 0 for the year attribute denotes the statistics for records without construction year information.hisdac_es_municipality_stats_multitemporal_wideform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in wide form. Note that a value of 0 for the year suffix denotes the statistics for records without construction year information.hisdac_es_municipality_stats_completeness_v1.csv: This CSV file contains the missingness rates (in %) of the building attribute per municipality, ranging from 0.0 (attribute exists for all buildings) to 100.0 (attribute exists for none of the buildings) in a given municipality.Column names for the completeness statistics tables:NATCODE: National municipality identifier*num_total: number of buildings per municperc_bymiss: Percentage of buildings with missing built year (construction year)perc_lumiss: Percentage of buildings with missing landuse attributeperc_luother: Percentage of buildings with landuse type "other"perc_num_floors_miss: Percentage of buildings without valid number of floors attributeperc_num_dwel_miss: Percentage of buildings without valid number of dwellings attributeperc_num_bunits_miss: Percentage of buildings without valid number of building units attributeperc_offi_area_miss: Percentage of buildings without valid official area (building indoor area, BIA) attributeperc_num_dwel_and_num_bunits_miss: Percentage of buildings missing both number of dwellings and number of building units attributeThe same statistics are available as geopackage file including municipality polygons in Lambert azimuthal equal area (EPSG:3035).*From the NATCODE, other regional identifiers can be derived as follows:NATCODE: 34 01 04 04001Country: 34Comunidad autónoma (CA_CODE): 01Province (PROV_CODE): 04LAU code: 04001 (province + municipality code)

This chipped training dataset is over Wa and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 7,615 tiles and 68,072 individual buildings. The satellite imagery resolution is 32 cm and was sourced from Maxar ODP (1040010056B6FA00). Dataset keywords: Urban, Peri-urban

The Countywide building outline dataset contains building outlines (over 3,000,000) for all buildings in Los Angeles County, including building height, building area, and the parcel number (also known as building footprints). This data was captured from stereo imagery as part of the LAR-IAC2 Project (2008 acquisition) and was updated as part of the LARIAC4 (2014) imagery acquisition.There are a number of sources. All buildings were updated to include changes between 2008 and 2014.City of Los Angeles – building outlines from LAR-IAC2 (2008), stereo generated, for all buildings > 64 square feetThe rest of the County – building outlines from LAR-IAC2 (2008), stereo generated, for all buildings > 400 square feetMost of the buildings in this dataset were generated using stereo imagery. This means that the person capturing the buildings actually saw them in 3-D, and therefore was able to more accurately capture the location of the roof line, since this method eliminated the impacts of building lean (where the height of the building impacts its apparent location). Basically – this is the most accurate method for capturing building outlines. In many cases the location is more accurate than our aerial photography and parcel boundaries.

Map and 2010 data from Australian Bureau of Statistics (ABS) Land Account: South Australia, Experimental Estimates, 2006-2011 (cat. No. 4609.4.55.001) http://www.abs.gov.au/ausstats/abs@.nsf/Latestproducts/4609.4.55.001Feature%20Article1002006%20-%202011?opendocument&tabname=Summary&prodno=4609.4.55.001&issue=2006%20-%202011&num=&view=

Data tables list 2011 SA1s within the 2011 Adelaide Urban Centre/Locality with property area calculations for specified time periods (1990 - 2014), as well corresponding mean building size and mean building size to property area ratio based on 2014 Valuer-General data. The VG data is from a single point in time and contains information for properties as of 2014. (eg. including extensions/renovations or where subdivision has been undertaken, which would include any increases in floor area or decreases in land area that occurred as a result of these). The VG oversee valuations for State Government property transactions and the making and return of council rating valuations. The primary data items used include Land Use, Property Area, Equivalent Main Area and Year built. "Equivalent main area" is the total area under the main roof excluding area of eg. carports, garages, verandahs etc. rather than the building footprint. Only landuse codes with a classification of "Private" were included in the analysis. State Office Land Use Classification 2007 was used to analyse Valuer General data. Only records classified as Single Unit Houses (land use code 1100-1119) were included in the analysis (80% of VG records), but this will include multistory houses. Because the area calculation for multistory houses could exceed the size of the land parcel, a "MeanAreaRatio_adj field has been included - Where the Pre1980_MeanAreaRatio for a property is greater than 100.0, then this has been adjusted down to 100.0 The analysis was restricted to Private Single Unit Houses (excluding multiple unit dwellings and commercial buildings) in an effort to enable the data to be used to understand residential backyard sizes and how these have changed over time. Note that although the figure heading notes a year range of 1990-2014 the original output dataset for Statistical Area Level 1 s(SA1) included records with a null year field (326 records) as well as records dating back to 1838.

Map relates to Figure BLT24 in the Built environment theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au

The map service can be viewed at http://soe.terria.io/#share=s-sB5VcuCdJXg9TdCi3nz3FT0eojG

Downloadable spatial data also available below.

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

• An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

• The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

• The property’s Flood Factor as well as data on economic loss.

• The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

• Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

• Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Field Name	Type	Description
fsid	int	First Street ID (FSID) is a unique identifier assigned to each location
long	float	Longitude
lat	float	Latitude
zcta	int	ZIP code tabulation area as provided by the US Census Bureau
blkgrp_fips	int	US Census Block Group FIPS Code
tract_fips	int	US Census Tract FIPS Code
county_fips	int	County FIPS Code
cd_fips	int	Congressional District FIPS Code for the 116th Congress
state_fips	int	State FIPS Code
floodfactor	int	The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)
CS_depth_RP_YY	int	Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00
CS_chance_flood_YY	float	Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00
aal_YY_CS	int	The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low
hist1_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist1_event	string	Short name of the modeled historic event
hist1_year	int	Year the modeled historic event occurred
hist1_depth	int	Depth (in cm) of flooding to the building from this historic event
hist2_id	int	A unique First Street identifier assigned to a historic storm event modeled by First Street
hist2_event	string	Short name of the modeled historic event
hist2_year	int	Year the modeled historic event occurred
hist2_depth	int	Depth (in cm) of flooding to the building from this historic event
adapt_id	int	A unique First Street identifier assigned to each adaptation project
adapt_name	string	Name of adaptation project
adapt_rp	int	Return period of flood event structure provides protection for when applicable
adapt_type	string	Specific flood adaptation structure type (can be one of many structures associated with a project)
fema_zone	string	Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders
footprint_flag	int	Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)

Map and 1990 data from Australian Bureau of Statistics (ABS) Land Account: South Australia, Experimental Estimates, 2006-2011 (cat. No. 4609.4.55.001) http://www.abs.gov.au/ausstats/abs@.nsf/Latestpr…Show full descriptionMap and 1990 data from Australian Bureau of Statistics (ABS) Land Account: South Australia, Experimental Estimates, 2006-2011 (cat. No. 4609.4.55.001) http://www.abs.gov.au/ausstats/abs@.nsf/Latestproducts/4609.4.55.001Feature Article1002006 - 2011?opendocument&tabname=Summary&prodno=4609.4.55.001&issue=2006 - 2011&num=&view= Data tables list 2011 SA1s within the 2011 Adelaide Urban Centre/Locality with property area calculations for specified time periods (1990 - 2014), as well corresponding mean building size and mean building size to property area ratio based on 2014 Valuer-General data. The VG data is from a single point in time and contains information for properties as of 2014. (eg. including extensions/renovations or where subdivision has been undertaken, which would include any increases in floor area or decreases in land area that occurred as a result of these). The VG oversee valuations for State Government property transactions and the making and return of council rating valuations. The primary data items used include Land Use, Property Area, Equivalent Main Area and Year built. "Equivalent main area" is the total area under the main roof excluding area of eg. carports, garages, verandahs etc. rather than the building footprint. Only landuse codes with a classification of "Private" were included in the analysis. State Office Land Use Classification 2007 was used to analyse Valuer General data. Only records classified as Single Unit Houses (land use code 1100-1119) were included in the analysis (80% of VG records), but this will include multistory houses. Because the area calculation for multistory houses could exceed the size of the land parcel, a "MeanAreaRatio_adj field has been included - Where the Pre1980_MeanAreaRatio for a property is greater than 100.0, then this has been adjusted down to 100.0 The analysis was restricted to Private Single Unit Houses (excluding multiple unit dwellings and commercial buildings) in an effort to enable the data to be used to understand residential backyard sizes and how these have changed over time. Note that although the figure heading notes a year range of 1990-2014 the original output dataset for Statistical Area Level 1 s(SA1) included records with a null year field (326 records) as well as records dating back to 1838. Map relates to Figure BLT24 in the Built environment theme of the 2016 State of the Environment Report, available at http://www.soe.environment.gov.au The map service can be viewed at http://soe.terria.io/#share=s-vLsXubDrmmC2at735prq96fFQzB Downloadable spatial data also available below.