Soil Landscape Map Units have recognizable topographic features formed on a particular geological material and containing a limited and defined range of soils. They therefore have similar land and soil characteristics and land suitability. Each Soil Landscape Map Unit incorporates a Land System, and a Soil Landscape Unit specific to that Land System. Each Soil Landscape Unit has an assigned Land Type.

Statewide Road Network including sealed and unsealed roads. The dataset represents navigable roads, including public and private access roads and tracks. A separate data layer stores 'unformed' DCDB centrelines which do not represent navigable roads. A limited number of associated features are stored separately as point features. Automatically updated when changes occur.

To assist with the regulation and administration of land transactions in South Australia, counties and hundreds were established; the first hundreds were proclaimed in 1846.This dataset contains images of over 1,000 selected South Australia hundred maps, historical cadastral mapping at scale 1:63,360\r \r Use in conjunction with the Flickr API https://www.flickr.com/services/api/\r

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Vegetation Map Of Sheet Sa.24
This dataset falls under the category Environmental Data Other.
It contains the following data: Based on the mapping made by the RADAMBRASIL project, this information was updated in accordance with the Brazilian Vegetation Technical Manual, published by IBGE.
This dataset was scouted on 2022-02-23 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.amerigeoss.org/dataset/cren_vegetacaosa24 URL for data access and license information. Please note: This link leads to an external resource. If you experience any issues with its availability, please try again later.

This dataset depicts land use across South Australia according to the Australian Land Use and Management (ALUM) Classification Version 8 aggregated from surveys in 2008, 2014 and 2016. It forms part of the Australian Collaborative Land Use and Management Program (ACLUMP) land use mapping. The dataset is a combination of land use data mapped over recent years. The data were derived from an initial desktop interpretation of aerial imagery followed by an on-ground field survey. This dataset will be updated when any new land use mapping is undertaken in a part of SA.

The Development Plan Map Index is used to determine which map to refer to in South Australian Development Plans. Link: http://www.sa.gov.au/topics/housing-property-and-land/local-government/development-plans/online-development-plans. Every council in South Australia has a Development Plan that specifies the type of development that can occur in that council area.

This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format.

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. The data-sets contain the population surfaces, metadata, and data quality layers. The population data surfaces are stored as GeoTIFF files for use in remote sensing or geographic information system (GIS) software. The data can also be explored via an interactive map - http://columbia.maps.arcgis.com/apps/View/index.html?appid=ce441db6aa54494cbc6c6cee11b95917 Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

This map features satellite imagery for the world and high-resolution aerial imagery for many areas. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the World Imagery map service description.

Bitly Link for this page: https://bit.ly/mvcfloodcompareAll datasets presented here are compiled by organizations other than the Martha's Vineyard Commission (MVC). The MVC has simply pulled these datasets into one map viewer for ease of direct visual comparison. The MVC encourages the viewer of this map to do their due diligence and research to understand the wide array of methodologies used to model flood inundation and sea level rise.The 3 Flood Inundation datasets presented are: FEMA (100 year or 1% annual probability flood zone) as per Effective Year 2016 data release.Mass Coast - Coastal Flood Risk Model (presented as flood probability for 3 future time horizons)Storm Tide Pathways - Flood Inundation Extents based on Total Water Level (in feet) relative to MLLW. There's a separate data layer for each inundation plane in half foot increments from 2.5ft MLLW to 19.5ft MLLW.SLOSH - Hurricane Inundation - Worst Case ScenarioVarious Links to learn more about these datasets:FEMA 2016 Data for Dukes CountyCoastal Zone Management Viewer: Mass Coast - Coastal Flood Risk Model Mass Coast 2030 Flood RiskMass Coast 2050 Flood RiskMass Coast 2070 Flood RiskMass Coast FAQStorm Tide Pathways App and Storm Tide Pathways InfoSLOSH - produced by NOAA & NWS v3 June 2022 (high tide scenario)The legend for the Mass Coast (MC-FRM) data shows the:The Probabilities 0.1% (in coral color) to 100% (in dark blue) is the Probability of Inundation - which is the chance of becoming flooded at some point each year.Coast Flood Exceedance Probabilities shown in the legend display the modeled outputs ranging from 0.1% (0.001, otherwise known as the 1,000-year storm) to 100% (1.0), which corresponds to the one-year storm. -- The 100% probability level generally corresponds to the annual high water value (NOT the average high tide).

Other data on this map include Salt Marshes, Wetlands MassDEP, Wetland Migration SLAMM Model (year 2070 with high sea level rise), Parcel Lines, and Building Roofprints.

This dataset consists of very high resolution urban land cover maps for two African cities, Mekelle, Ethiopia and Polokwane, South Africa for 2020. Maps were generated from Planet SuperDove satellite imagery at 3.125-m spatial resolution, and Worldview-3 satellite imagery (Maxar Techologies) at two spatial resolutions, 2 m for multispectral imagery and 0.5-m spatial resolution for pansharpened imagery. An object-based image classification approach was used to produce a multi-class land cover product for each image source. The aim of this work was to support fine scale urban land cover analyses and comparative assessments between different high resolution satellite imagery sources. The data are provided in shapefile format.

The identification and generation of new mineral exploration targets is increasingly data-driven, requires near real-time, integrated and high-quality digital geological data that aligns with machine learning and artificial intelligence... The identification and generation of new mineral exploration targets is increasingly data-driven, requires near real-time, integrated and high-quality digital geological data that aligns with machine learning and artificial intelligence applications. The South Australia Geology (SA Geology) project recognises these needs and aims to modernise the capture, management, delivery, and scope of South Australia's pre-competitive geological map datasets. The 1st edition of SA Geology focuses on the western Gawler Craton, and it is planned to expand to other regions in future editions. One of the methods for accessing SA Geology data will be through a series of time-slice map layers available on the South Australian Resources Information Gateway (SARIG) Map, integrated with other relevant systems such as the new Digital Explanatory Notes (DEN) application. The SA Geology Time Slice data package includes 15 Time Slice layer groups consisting of 3 geological information layer types that have been filtered by Time Slice Code: Geological Unit Polygons, Structure Lines and Boundary Lines, and one Trendlines layer. Cenozoic time slice (0-64 Ma) Jurassic-Cretaceous time slice (65-200 Ma) Triassic time slice (201-249 Ma) Permian time slice (250-299 Ma) Ordovician-Carboniferous time slice (300-454 Ma) Cambrian time slice (455-544 Ma) Neoproterozoic time slice (545-899 Ma) Meso-Neoproterozoic time slice (900-1249 Ma) Mesoproterozoic time slice (1250-1514 Ma) Paleo-Mesoproterozoic time slice (1515-1639 Ma) Toondulya time slice (1560-1600 Ma) Late Paleoproterozoic time slice (1640-1749 Ma) Early Paleoproterozoic time slice (1750-2299 Ma) Archean-Paleoproterozoic time slice (2300-4600 Ma) Undefined time slice (0-4600 Ma) Trendlines (0-4600 Ma)

Title: Geological map of the Republic of South Africa and the Kingdoms of Lesotho and Swaziland - Scale: 1000000 - Assemblage de 4 feuilles - Sheet number/Numéro de feuille/Bladnummer: NE

Interactive map showing pins for large and local council parks and beaches/ foreshores

This dataset contains images of more than 1000 selected maps and charts with South Australian coverage.\r \r Use in conjunction with the Flickr API https://www.flickr.com/services/api/.

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Enviro Data SA is South Australia’s environment information and data portal. The portal contains a wide range of information and data relating the state’s natural resources including maps, reports, …Show full descriptionEnviro Data SA is South Australia’s environment information and data portal. The portal contains a wide range of information and data relating the state’s natural resources including maps, reports, downloadable data, web applications (data systems). Enviro Data SA incorporates NatureMaps, WaterConnect and SA Climate Ready.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Discover the lush greenery of the City of Unley with our comprehensive Tree Canopy dataset. Explore valuable information on the distribution and coverage of trees, enabling informed decisions for sustainable urban planning. Accessible, open data for a greener future.