This 3D basemap presents OpenStreetMap (OSM) data and other data sources and is hosted by Esri using the OpenStreetMap style.Esri created the Places and Labels, Trees, and OpenStreetMap layers from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new scene available to the OSM, GIS, and Developer communities.The Buildings layer (beta) presents open buildings data that has been processed and hosted by Esri. Esri created this buildings scene layer using data from the Overture Maps Foundation (OMF) which is supported by Meta, Microsoft, Amazon, TomTom, Esri and other members. Overture includes data from many sources, including OpenStreetMap (OSM). The 3D buildings layer will be updated each month with the latest version of Overture data, which includes the latest updates from OSM, Esri Community Maps, and other sources.Overture Maps is a collaborative project to create reliable, easy-to-use, and interoperable open map data. Member companies work to bring together the best available open datasets, and the resulting data can be downloaded from Microsoft Azure or Amazon S3. Esri is a member of the OMF project and is excited to make this 3D web scene available to the ArcGIS user community.

This National Geographic Style Map (World Edition) web map provides a reference map for the world that includes administrative boundaries, cities, protected areas, highways, roads, railways, water features, buildings, and landmarks, overlaid on shaded relief and a colorized physical ecosystems base for added context to conservation and biodiversity topics. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri, National Geographic or any governing authority.This basemap, included in the ArcGIS Living Atlas of the World, uses the National Geographic Style vector tile layer and the National Geographic Style Base and World Hillshade raster tile layers.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

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Autonomous Vehicles HD Map Market Outlook

The global autonomous vehicles HD map market size in 2023 is valued at approximately USD 2.5 billion and is projected to reach USD 15.8 billion by 2032, growing at a CAGR of 22.5% during the forecast period. This market growth is primarily driven by the increasing demand for high-definition (HD) maps that provide real-time information to support the navigation and operational control of autonomous vehicles.

One of the primary growth factors for the autonomous vehicles HD map market is the rapid advancement in autonomous driving technologies. As major automotive manufacturers and tech companies invest heavily in developing autonomous vehicles, the need for precise and reliable HD maps has become crucial. These maps are essential for autonomous vehicles to navigate complex urban environments accurately and safely. Additionally, HD maps provide crucial data layers such as lane markings, road geometry, and traffic signals, which are vital for autonomous driving systems to make informed decisions.

Another significant growth factor is the increasing adoption of cloud-based solutions for HD mapping. Cloud-based HD maps offer several advantages, including real-time updates, scalability, and lower operating costs. These solutions enable autonomous vehicles to access the most up-to-date maps, ensuring that they can adapt to changing road conditions and traffic patterns. Moreover, cloud-based HD maps facilitate the integration of data from various sources, such as vehicle sensors and IoT devices, enhancing the map's accuracy and reliability.

The growing demand for enhanced safety features in vehicles is also driving the market for autonomous vehicles HD maps. HD maps play a crucial role in enabling advanced driver assistance systems (ADAS) and other safety features in both passenger and commercial vehicles. By providing detailed and accurate information about the road environment, HD maps help in reducing the risk of accidents and improving overall road safety. This has led to increased investments in HD mapping technologies by automotive OEMs and other stakeholders in the autonomous driving ecosystem.

Regionally, the Asia Pacific region is expected to witness significant growth in the autonomous vehicles HD map market. Countries like China, Japan, and South Korea are at the forefront of autonomous vehicle research and development. The strong presence of leading automotive manufacturers, coupled with supportive government policies and investments in smart city infrastructure, is driving the demand for HD maps in this region. Additionally, the increasing adoption of electric and autonomous vehicles in Asia Pacific is further propelling the market growth.

Solution Analysis

The autonomous vehicles HD map market is segmented into cloud-based and embedded solutions. Cloud-based HD mapping solutions are gaining popularity due to their numerous advantages, including real-time updates and scalability. These solutions allow autonomous vehicles to access the most current maps, ensuring that they can navigate accurately and safely. Moreover, cloud-based solutions facilitate the integration of various data sources, such as vehicle sensors and IoT devices, enhancing the map's accuracy and reliability. The lower operating costs associated with cloud-based solutions also make them an attractive option for automotive OEMs and fleet management companies.

Embedded HD mapping solutions, on the other hand, provide a robust alternative for autonomous vehicles that require high levels of data security and reliability. Unlike cloud-based solutions, embedded HD maps are stored locally within the vehicle's onboard systems, reducing the dependency on external data networks. This is particularly important for autonomous vehicles operating in remote or low-connectivity areas. Additionally, embedded solutions offer faster data processing and lower latency, which are critical for real-time decision-making in autonomous driving scenarios.

The choice between cloud-based and embedded HD mapping solutions often depends on the specific requirements of the end-users. For instance, automotive OEMs and fleet management companies may prefer cloud-based solutions for their cost-effectiveness and ease of integration with existing systems. In contrast, mobility as a service providers might opt for embedded solutions to ensure high levels of reliability and data security. Both solution types are expected to see significant growth, driven by the increasing adoption of autonomous vehicles and the demand for advanced navig

The El Pilar Project has been conducting research at El Pilar, Belize and Guatemala since 1993, and was founded on a base of survey work that goes back to 1983. This unusual archaeological program recognizes the present environment as a part of the ancient Maya past. Our mission is the preservation and conservation of endangered resources through local and international education. Addressing tensions between culture and nature, we use the past as a reference to build a responsible future. Weaving together traditional knowledge and practice with scientific inquiry and interpretation, we promote a deeper awareness of heritage through local partnership.

The University of California Santa Barbara (UCSB) Maya Forest GIS is an essential tool to organize and use the numerous geographic resources involved in our studies, and provide reliable datasets for the project.

The land cover mapping of Victoria, Australia, for 2021/22 was conducted using Sentinel-2 satellite imagery and the random forest machine learning algorithm. This map represents the entire Victoria at a spatial resolution of 20 meters, considerably improving earlier versions that previously were developed using coarser-resolution MODIS imagery. The map follows the FAO Land Cover Classification System to maintain consistency and accuracy in defining land cover types. Sentinel-2 data from April 2020 to March 2021 have been collected to capture seasonal variation relevant to a wide range of applications, from agricultural management to environmental monitoring, including climate change modelling. The data collection was done based on the usage of Sentinel-2 Level-1C orthorectified reflectance data that were further processed with the intention of deriving temporal aggregates of both spectral bands and vegetation indices. These pre-processed data then formed the basis of training a random forest classifier, calibrated with a blend of field data and desktop-derived samples from trustworthy sources. The land cover map has been rigorously validated, achieving an overall accuracy of 86%. This dataset could serve as a base tool in policy formulation, research, and land management applications to enable informed decisions on agricultural policy-making, climate resilience initiatives, and sustainable land use practices.

Unlock the key to understanding the MSCFV Resource Map's diverse layers with our "Resource Map Layer Description - Data Dictionary" PDF Help Sheet. This essential resource serves as your guide to comprehending and harnessing the wealth of data within the map's resource layers. From defining each layer to providing authoritative data sources, this compact guide empowers you with the knowledge needed for effective resource management and decision-making.Inside, you'll find:Layer Definitions: Clear and concise explanations for each resource layer, ensuring you grasp their purpose and relevance.Data Sources: Trustworthy references for every layer's data source, guaranteeing the reliability and credibility of the information you're working with.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

ADMMR map collection: Old Reliable Mine Claim Map; 1 in. to 300 feet; 14 x 11 in.

Empower yourself with the "MSCFV Resource Map Help Sheet," a comprehensive guide that unlocks the full potential of the MSCFV Resource Map. Whether you're a newcomer or a seasoned user, this resource is designed to enhance your map navigation skills, allowing you to make informed decisions and optimize your resource management.Inside this guide, you'll find:Layer Descriptions: Clear explanations of each map layer, helping you understand the purpose and significance of the data presented.Data Sources: Reliable references for the data sources behind each layer, ensuring credibility and trustworthiness.Interactive Features: Tips on how to effectively interact with the map layers, customize the display, and toggle layers on or off to suit your specific needs.This "MSCFV Resource Map Help Sheet" is your key to mastering the map's layers and harnessing their potential. Download your copy now and elevate your resource management to new heights.

The LUISA Base Map 2018 is a high-resolution land use/land cover map developed and produced by the Joint Research Centre of the European Commission. It corresponds to a modified and improved version of the CORINE Land Cover 2018 map. Compared to CORINE, the LUISA Base Map delivers a higher overall spatial detail and finer thematic breakdown of artificial land use/cover categories (17 categories instead of 11 in CORINE). The LUISA Base Map can be used for multiple purposes and it is more suitable than CORINE for applications requiring fine spatial and/or thematic detail of land use/land cover consistently across Europe, such as land use/cover accounting and modelling. Coverage: EU27, Albania, Bosnia and Herzegovina, Iceland, Kosovo, Liechtenstein, Montenegro, North Macedonia, Norway, Serbia, Switzerland, Turkey, United Kingdom. RESOLUTION: MMU = 1ha (artificial surfaces); MMU = 5ha (non artificial surfaces); pixel resolution = 50m, 100m LINEAGE: The 2018 edition of the LUISA Base Map is constructed by refining the original thematic and spatial detail of the CORINE Land Cover (CLC) 2018. The methodology consists of a structured, automated and reproducible geospatial data fusion approach that integrates disparate but highly detailed land use information from a series of trusted, off-the-shelf datasets onto the CLC 2018 map, relying on data from the reporting year 2018 whenever possible. The main sources include the CLC Change Maps, the Copernicus High Resolution Layers (forest, water, wetlands, and imperviousness layers), the Copernicus Urban Atlas and Coastal Zones, the Global Human Settlement Layer from the Joint Research Centre, as well as the TomTom Multinet and OpenStreeMap. The use of various European-wide remotely sensed imagery as input and a uniform and automated methodology yields high comparability of the map across countries. The LUISA Base Maps 2018 and 2012 were produced using the same method and data sources. However, input data from 2012 and 2018 may not be always comparable. This is especially the case of the Copernicus High Resolution Layers whose sensors and algorithms changed between 2012 and 2018. For this reason, the LUISA Base Maps are not suitable for change detection. For what concerns the accounting of changes in urban fabric for larger geographical units (e.g. NUTS), the effect of differences in input data is limited because the LUISA Base Map uses the Copernicus Imperviousness change layers to detect meaningful changes of urban fabric backwards, using 2018 as the reference period. COMPLETENESS: 100%

This web map is designed to provide an enriched geospatial platform to ascertain the flood potential status of our local place of residence and other land-use activities. Information on the flood risk distribution can be extracted by 5 major magnitudes (very high, high, moderate, low, and very low). The buildings, roads, and rail tracks that are susceptible to flooding based on the identified magnitudes are also included in the web map. In addition, the historical or flood inventory layer, which contains information on the previous flooding disasters that have occurred within the river basin, is included.

This web map is the result of extensive research using available data, open source and custom datasets that are extremely reliable.The collaborative study was done by Dr. Felix Ndidi Nkeki (GIS-Unit, BEDC Electricity PLC, 5, Akpakpava Road, Benin City, Nigeria and Department of Geography and Regional Planning, University of Benin, Nigeria), Dr. Ehiaguina Innocent Bello (National Space Research and Development Agency, Obasanjo Space Centre, FCT-Abuja, Nigeria) and Dr. Ishola Ganiy Agbaje (Centre for Space Science Technology Education, Obafemi Awolowo University, Ile-Ife, Nigeria). The study results are published in a reputable leading world-class journal known as the International Journal of Disaster Risk Reduction. The methodology, datasets, and full results of the study can be found in the paper.

The major sources of data are: ALOS PALSAR DEM; soil data from Harmonised World Soil Database-Food and Agriculture Organisation of the United Nations (FAO); land-use and surface geologic datasets from CSSTE, OAU Campus, Ile-Ife, Nigeria and Ibadan Urban Flood Management Project (IUFMP), Oyo State, Nigeria; transport network data was extracted from Open Street Map; building footprint data was mined from Google open building; and finally, rainfall grid data was downloaded from the Centre for Hydrometeorology and Remote Sensing (CHRS).

Aim

To understand the representativeness and accuracy of expert range maps, and explore alternate methods for accurately mapping species distributions.

Location

Global

Time period

Contemporary

Major taxa studied

Terrestrial vertebrates, and Odonata

Methods

We analyzed the biases in 50,768 animal IUCN, GARD and BirdLife species maps, assessed the links between these maps and existing political and various non-ecological boundaries to assess their accuracy for certain types of analysis. We cross-referenced each species map with data from GBIF to assess if maps captured the whole range of a species, and what percentage of occurrence points fall within the species’ assessed ranges. In addition, we use a number of alternate methods to map diversity patterns and compare these to high resolution models of distribution patterns.

Results

On average 20-30% of species’ non-coastal range boundaries overlapped with administrative national boundaries. In total, 60% of areas with the highest spatial turnover in species (high densities of species range boundaries marking high levels of shift in the community of species present) occurred at political boundaries, especially commonly in Southeast Asia. Different biases existed for different taxa, with gridded analysis in reptiles, river-basins in Odonata (except the Americas) and county-boundaries for Amphibians in the US. On average, up to half (25-46%) species recorded range points fall outside their mapped distributions. Filtered Minimum-convex polygons performed better than expert range maps in reproducing modeled diversity patterns.

Main conclusions

Expert range maps showed high bias at administrative borders in all taxa, but this was highest at the transition from tropical to subtropical regions. Methods used were inconsistent across space, time and taxa, and ranges mapped did not match species distribution data. Alternate approaches can better reconstruct patterns of distribution than expert maps, and data driven approaches are needed to provide reliable alternatives to better understand species distributions.

Methods Materials and methods

We use a combination of approaches to explore the relationship between species range maps and geopolitical boundaries and a subset of geographic features. In some cases we used the density of species range boundaries to explore the relationship between these and various features (i.e. administrative boundaries, river basin boundaries etc.). Additionally, species richness and spatial turnover are used to explore changes in richness over short geographic distances. Analyses were conducted in R statistical software unless noted otherwise. All code scripts are available at https://github.com/qiaohj/iucn_fix. Workflows are shown in Figure S1a-c with associated scripts listed.

Species ranges and boundary density maps

ERMs (Expert range maps) were downloaded from the IUCN RedList website for mammals (5,709 species), odonates (2,239 species) and amphibians (6,684 species; https://www.iucnredlist.org/resources/grid/spatial-data). Shapefile maps for birds were downloaded from BirdLife (10,423 species, http://datazone.birdlife.org/species/requestdis), and for reptiles from the Global Assessment of Reptile Distributions (GARD) (10,064 species; Roll et al., 2017). Each species’ polygon boundaries were converted to a polylines to show the boundary of each species range (Figure S1a-II; codes are lines 7 – 18 in line2raster_xxxx.r ; xxxx varies based on the taxa). The associated shapefile was then split to produce independent polyline files for each species within each taxon (see Figure S1a-I, codes are lines 29 to 83 in the same file above.).

To generate species boundary density maps, species range boundaries were rasterized at 1km spatial resolution with an equal area projection (Eckert-IV), and stacked to form a single raster for each taxon (at the level of amphibians, odonates, etc.). This represented the number of species in each group and their overlapping range boundaries (Figure S1b-II, codes are in line2raster_all.r). Each cell value indicated the number of species whose distribution boundaries overlapped with each cell, enabling us to overlay this rasterized information with other features (i.e. administrative boundaries) so that the overlaps between them can be calculated in R. These species boundary density maps underlie most subsequent analyses. R code and caveats are given in the supplements, links are provided in text and Figure S1.

Geographic boundaries

Spatial exploration of species range boundaries in ArcGIS suggested that numerous geographic datasets (i.e. political and in few cases geographic features such as river basins) were used to delineate the species ranges for different regions and taxa (this is sometimes part of the methodology in developing ERMs as detailed by Ficetola et al., 2014). Thus in addition to analyzing the administrative bias and the percentage of occurrence records within each species’ ERM for all taxa, additional analyses were conducted when other biases were evident in any given taxa or region (detailed later in methods on a case-by-case basis).

For all taxa, we assessed the percentage of overlap between species range boundaries and national and provincial boundaries by digitizing each to 1km (equivalent to buffering thie polyline by 500m), both with and without coastal boundaries. An international map was used because international (Western) assessors use them, and does not necessarily denote agreed country boundaries (https://gadm.org/). The different buffers (500m, 1000m, 2500m, 5000m) were added to these administrative boundaries in ArcMap to account for potential, insignificant deviations from political boundaries (Figure S1b). An R script for the same function is provided in “country_line_buffer.r”.

To establish where multiple species shared range boundaries we reclassified the species range boundary density rasters for each taxa into richness classes using the ArcMap quartile function (Figure S1). From these ten classes the percentage of the top-two, and top-three quartiles of range densities within different buffers (500m, 1000m, 2500m, 5000m) was calculated per country to determine what percentage of highest range boundary density approximately followed administrative borders. This was done because people drawing ERMs may use detailed administrative maps or generalize near political borders, or may use political shapefiles that deviate slightly. It is consequently useful to include varying distances from administrative features to assess how range boundary densities vary in relation to administrative boundaries. Analyses of relationships between individual species range boundaries and administrative boundaries (coastal, non-coastal) were made in R and scripts provided (quantile_country_buffer_overlap.r).

Spatial turnover and administrative boundaries

Heatmaps of species richness were generated by summing entire sets of compiled species ranges for each taxon in polygonal form (Figure 1; Figure S1b-I). To assess abrupt diversity changes, standard deviations for 10km blocks were calculated using the block statistics function in ArcMap. Abrupt changes in diversity were signified by high standard deviations based on the cell statistics function in ArcGIS, which represented rapid changes in the number of species present. Maps were then classified into ten categories using the quartile function. Given the high variation in maximum diversity and taxonomic representation, only the top two –three richness categories were retained per taxon. This was then extracted using 1km buffers of national administrative boundaries to assess percentages of administrative boundaries overlapping turnover hotspots by assessing what proportion of political boundaries were covered by these turnover hotspots.

Taxon-specific analyses

Data exploration and mapping exposed taxon and regional-specific biases requiring additional analysis. Where other biases and irregularities were clear from visual inspection of the range boundary density maps for each taxa, the possible causes of biases were assessed by comparing range boundary density maps to high-resolution imagery and administrative maps via the ArcGIS server (AGOL). Standardized overlay of the taxon boundary sets with administrative or geophysical features from the image-server revealed three types of bias which were either spatially or taxonomically limited between: 1) amphibians with county borders in the United States, 2) dragonflies and river basins globally and 3) gridding of distributions of reptiles. In these cases, species boundary density maps were used as a basis to identify potential biases which were then explored empirically using appropriate methods.

For amphibians, counties in the United States (US) were digitized using a county map from the US (https://gadm.org/), then buffered by with 2.5km either side. Amphibian species range boundary density maps were reclassified showing where species range boundaries existed (with other non-range boundary areas reclassified as “no data,”) and all species boundaries numerically indicated (i.e. values of 1 indicates one species range boundary, values of 10 indicates ten species range boundaries). Percentages of species boundary areas falling on county and in the buffers, in addition to species range boundaries which did not overlap with county boundaries were calculated to give measures of what percentage of the species boundaries fell within 2.5km of county boundaries.

For Odonata, many species were mapped to river basin borders. We used river basins of levels 6-8 (sub-basin to basin) in the river hierarchy (https://hydrosheds.org) to assess the relationship between Odonata boundaries and river boundaries. Two IUCN datasets exist for Odonata; the IUCN Odonata specialist group spatial dataset

The LUISA Base Map 2012 is a high-resolution land use/land cover map developed and produced by the Joint Research Centre of the European Commission. It corresponds to a modified and improved version of the CORINE Land Cover 2012 map. Compared to CORINE, the LUISA Base Map delivers a higher overall spatial detail and finer thematic breakdown of artificial land use/cover categories (17 categories instead of 11 in CORINE). The LUISA Base Map can be used for multiple purposes and it is more suitable than CORINE for applications requiring fine spatial and/or thematic detail of land use/land cover consistently across Europe, such as land use/cover accounting and modelling. Coverage: EU27, Albania, Bosnia and Herzegovina, Iceland, Kosovo, Liechtenstein, Montenegro, North Macedonia, Norway, Serbia, Switzerland, Turkey, United Kingdom. RESOLUTION: MMU = 1ha (artificial surfaces); MMU = 5ha (non artificial surfaces); pixel resolution = 50m, 100m LINEAGE: The 2012 edition of the LUISA Base Map is constructed by refining the original thematic and spatial detail of the CORINE Land Cover (CLC) 2012. The methodology consists of a structured, automated and reproducible geospatial data fusion approach that integrates disparate but highly detailed land use information from a series of trusted, off-the-shelf datasets onto the CLC 2012 map, relying on data from the reporting year 2012 whenever possible. The main sources include the CLC Change Maps, the Copernicus High Resolution Layers (forest, water, wetlands, and imperviousness layers), the Copernicus Urban Atlas and Coastal Zones, the European Settlement Map 2012 from the Joint Research Centre, as well as the TomTom Multinet and OpenStreeMap. The use of various European-wide remotely sensed imagery as input and a uniform and automated methodology yields high comparability of the map across countries. The LUISA Base Maps 2012 and 2018 were produced using the same method and data sources. However, input data from 2012 and 2018 may not be always comparable. This is especially the case of the Copernicus High Resolution Layers whose sensors and algorithms changed between 2012 and 2018. For this reason, the LUISA Base Maps are not suitable for change detection. For what concerns the accounting of changes in urban fabric for larger geographical units (e.g. NUTS), the effect of differences in input data is limited because the LUISA Base Map uses the Copernicus Imperviousness change layers to detect meaningful changes of urban fabric backwards, using 2018 as the reference period. COMPLETENESS: 100%

Dataset Abstract:Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments. The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.

USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.

Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.

Dataset: 5 bandsb1: Number of tall monthsb2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcaneb3: ESA crop mask: 0 = non-cropland, 1 = croplandb4: ESRI crop mask: 0 = non-cropland, 1 = croplandb5: GLAD crop mask: 0 = non-cropland, 1 = cropland

The dataset can be accessed on Google Earth Engine (GEE) at https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLADExample GEE script for visualizing and masking the sugarcane maps by country available at:https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD

AquaMaps are computer-generated predictions of natural occurrence of marine species, based on the environmental tolerance of a given species with respect to depth, salinity, temperature, primary productivity, and its association with sea ice or coastal areas. These 'environmental envelopes' are matched against an authority file which contains respective information for the Oceans of the World. Independent knowledge such as distribution by FAO areas or bounding boxes are used to avoid mapping species in areas that contain suitable habitat, but are not occupied by the species. Maps show the color-coded likelihood of a species to occur in a half-degree cell, with about 50 km side length near the equator. Experts are able to review, modify and approve maps.

Environmental envelopes are created in part (FAO areas, bounding boxes, depth ranges) from respective information in species databases such as FishBase and in part from occurrence records available from OBIS or GBIF. AquaMaps predictions have been validated successfully for a number of species using independent data sets and the model was shown to perform equally well or better than other standard species distribution models, when faced with the currently existing suboptimal input data sets (Ready et al. 2010).

The creation of AquaMaps is supported by the following projects: MARA, Pew Fellows Program in Marine Conservation, INCOFISH, Sea Around Us, and Biogeoinformatics of Hexacorals.

Kaschner, K., D.P. Tittensor, J. Ready, T Gerrodette and B. Worm (2011). Current and Future Patterns of Global Marine Mammal Biodiversity. PLoS ONE 6(5): e19653. PDF

Ready, J., K. Kaschner, A.B. South, P.D Eastwood, T. Rees, J. Rius, E. Agbayani, S. Kullander and R. Froese (2010). Predicting the distributions of marine organisms at the global scale. Ecological Modelling 221(3): 467-478. PDF

Copyright Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. (CC-BY-NC) You are welcome to include maps from www.aquamaps.org in your own web sites for non-commercial use, given that such inserts are clearly identified as coming from AquaMaps, with a backward link to the respective source page.

Contacts Rainer Froese, GEOMAR, Coordinator rfroese@geomar.de Kristin Kaschner, Uni Freiburg, model development Kristin.Kaschner@biologie.uni-freiburg.de Ma. Lourdes D. Palomares, UBC, extension to non-fish marine organisms m.palomares@fisheries.ubc.ca Sven Kullander, NRM, extension to freshwater ve-sven@nrm.se Jonathan Ready, NRM, implementation jonathan.ready@gmail.com Tony Rees, formerly with CSIRO, mapping tools Tony.Rees@marinespecies.org Paul Eastwood, SOPAC, validation Paul.Eastwood@sopac.org Andy South, CEFAS, validation andy.south@cefas.co.uk Josephine Rius-Barile, Q-quatics, database programming / data collection j.barile@q-quatics.org Cristina Garilao, GEOMAR, web programming cgarilao@geomar.de Kathleen Kesner-Reyes, Q-quatics, map validation k.reyes@q-quatics.org Elizabeth Bato, Q-quatics, map validation (non-fish) e.david@q-quatics.org

Citing AquaMaps

General citation Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2019. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, version 10/2019.

Cite individual maps as, e.g., Computer Generated Map for Gadus morhua (Atlantic cod). www.aquamaps.org, version 10/2019 (accessed 01 Oct 2019).

Reviewed Native Distribution Map for Gadus morhua (Atlantic cod). www.aquamaps.org, version 10/2019 (accessed 01 Oct 2019).

Cite biodiversity maps as, e.g., Shark and Ray Biodiversity Map. www.aquamaps.org, version 10/2019 (accessed 01 Oct 2019).

Cite the environmental dataset as, e.g., Kesner-Reyes, K., Segschneider, J., Garilao, C., Schneider, B., Rius-Barile, J., Kaschner, K., and Froese, R.(editors). AquaMaps Environmental Dataset: Half-Degree Cells Authority File (HCAF). World Wide Web electronic publication, www.aquamaps.org/main/envt_main.php, ver. 7, 10/2019.

Using Full or Large Sets of AquaMaps Data We encourage partnering with the AquaMaps team for larger research projects or publications that would make intensive use of AquaMaps to ensure that you have access to the latest version and/or reviewed maps, the limitations of the data set are clearly understood and addressed, and that critical maps and/or unlikely results are recognized as such and double-checked for correctness prior to drawing conclusions and/or subsequent publication.

The AquaMaps team can be contacted through Rainer Froese (rfroese@geomar.de) or Kristin Kaschner (Kristin.Kaschner@biologie.uni-freiburg.de).

Privacy Policy AquaMaps uses log data generate usage statistics. Like most websites, AquMaps gathers information about internet protocol (IP) addresses, browser, referring pages, operating system, date/time, clicks, and visited pages, and store it in log files. This information is used to find errors in our website, analyze trends, and determine country of origin of our users. The log files are stored indefinitely. Only the administrators of the AquaMaps server has direct access to the log files. The information is used to inform further development of AquaMaps. Usage statistics may be shared with third parties for non-commercial purposes.

Disclaimer AquaMaps generates standardized computer-generated and fairly reliable large scale predictions of marine and freshwater species. Although the AquaMaps team and their collaborators have obtained data from sources believed to be reliable and have made every reasonable effort to ensure its accuracy, many maps have not yet been verified by experts and we strongly suggest you verify species occurrences with independent sources before usage. We will not be held responsible for any consequence from the use or misuse of these data and/or maps by any organization or individual.

Copyright This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License (CC-BY-NC). You are welcome to include text, numbers and maps from AquaMaps in your own web sites for non-commercial use, given that such inserts are clearly identified as coming from AquaMaps, with a backward link to the respective source page. Note that although species photos and drawings draw mainly from FishBase and SeaLifeBase, they belong to the indicated persons or organizations and have their own copyright statements.

1. OC Waze Partner Hub GeoRSS Cumulative Alert Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals. OC Waze Partner Hub data provide information about traffic jams and events that affect road conditions, either from drivers using Waze, a.k.a. Wazers, or from external sources. Wazers may issue reports from the location at which they are currently located or, if no longer at the location, within 30 minutes after the event occurred. We are also able to provide automatic alerts for what we call Unusual Traffic (or Irregularities) - incidents that affect a large number of users and fall outside the normal traffic patterns for a given day and time.Waze generates traffic jam information by processing the following data sources:Data: includes all traffic data reported by Waze users through the Waze mobile application. Reliability: Each alert gets a reliability score based on other user reactions (‘Thumbs up’, ‘Not there’ etc.) and the level of the reporter (Wazers gain levels by contributing to the map, starting at level 1 and reaching up to level 6. The higher the level, the more experienced and trustworthy the Wazer.) The score (0-10) indicates how reliable the report is. Confidence: Each alert gets a confidence score based on other user reactions (‘Thumbs up’, ‘Not there’). The score ranges between 0 and 10. A higher score indicates more positive feedback from Waze users.2. OC Waze Partner Hub GeoRSS Cumulative Traffic Jam Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals.The traffic jams feed includes data gathered in real time about traffic slowdowns on specific road segments. Waze generates traffic jam information by processing the following data sources: GPS location-points sent from user phones (users who drive while using the app) and calculations of the current average speed vs. free-flow speed (maximum speed measured on the road-segment). For Unusual traffic (irregularities) Waze uses historic average speeds (on 30 minute time-slots). User generated reports - reports shared by Waze users who encounter traffic jams. These appear as regular alerts, and also affect the way we identify and present traffic jams.Original data provided by Waze App. Learn more at Waze.com.

These rasters provide the local mean annual extreme low temperature from 1976 to 2005 in an 800m x 800m grid covering the USA (including Puerto Rico) based on interpolation of data from more than a thousand weather stations. Each location's Plant Hardiness Zone is calculated based on classifying that temperature into 5 degree bands. The classified rasters are then used to create print and interactive maps. A complex algorithm was used for this edition of the USDA Plant Hardiness Zone Map (PHZM) to enable more accurate interpolation between weather reporting stations. This new method takes into account factors such as elevation changes and proximity to bodies of water, which enabled mapping of more accurate zones.Temperature station data for this edition of the USDA PHZM came from several different sources. In the eastern and central United States, Puerto Rico, and Hawaii, nearly all the data came from weather stations of the National Weather Service. In the western United States and Alaska, data from stations maintained by USDA Natural Resources Conservation Service, USDA Forest Service, U.S. Department of the Interior (DOI) Bureau of Reclamation, and DOI Bureau of Land Management also helped to better define hardiness zones in mountainous areas. Environment Canada provided data from Canadian stations, and data from Mexican stations came from the Global Historical Climate Network.All of these data were carefully examined to ensure that only the most reliable were used in the mapping. In the end, data from a total of 7,983 stations were incorporated into the maps. The USDA PHZM was produced with the latest version of PRISM, a highly sophisticated climate mapping technology developed at Oregon State University. The map was produced from a digital computer grid, with each cell measuring about a half a mile on a side. PRISM estimated the mean annual extreme minimum temperature for each grid cell (or pixel on the map) by examining data from nearby stations; determining how the temperature changed with elevation; and accounting for possible coastal effects, temperature inversions, and the type of topography (ridge top, hill slope, or valley bottom).Information on PRISM can be obtained from the PRISM Climate Group website (http://prism.oregonstate.edu).Once a draft of the map was completed, it was reviewed by a team of climatologists, agricultural meteorologists, and horticultural experts. If the zone for an area appeared anomalous to these expert reviewers, experts doublechecked for errors or biases.For example, zones along the Canadian border in the Northern Plains initially appeared slightly too warm to several members of the review team who are experts in this region. It was found that there were very few weather reporting stations along the border in the United States in that area. Data from Canadian reporting stations were added, and the zones in that region are now more accurately represented. In another example, a reviewer noted that areas along the relatively mild New Jersey coastline that were distant from observing stations appeared to be too cold. This was remedied by increasing the PRISM algorithm’s sensitivity to coastal proximity, resulting in a mild coastal strip that is more consistently delineated up and down along the shoreline.On the other hand, a reviewer familiar with Maryland’s Eastern Shore thought the zones there seemed too warm. The data were doublechecked and no biases were found; the zone designations remained unchanged.The zones in this edition were calculated based on 1976-2005 temperature data. Each zone represents the average annual extreme minimum temperature for an area, reflecting the temperatures recorded for each of the years 1976-2005. This does not represent the coldest it has ever been or ever will be in an area, but it reflects the average lowest winter temperature for a given geographic area for this time period. This average value became the standard for assigning zones in the 1960s. The previous edition of the USDA Plant Hardiness Zone Map, which was revised and published in 1990, was drawn from weather data from 1974 to 1986.A detailed explanation of the mapmaking process and a discussion of the horticultural applications of the new PHZM are available from the articles listed below.Daly, C., M.P. Widrlechner, M.D. Halbleib, J.I. Smith, and W.P. Gibson. 2012. Development of a new USDA Plant Hardiness Zone Map for the United States. Journal of Applied Meteorology and Climatology, 51: 242-264. Link to articleWidrlechner, M.P., C. Daly, M. Keller, and K. Kaplan. 2012. Horticultural Applications of a Newly Revised USDA Plant Hardiness Zone Map. HortTechnology, 22: 6-19. Link to article

The National Indicative Aggregated Fire Extent Dataset has been developed rapidly to support the immediate needs of the Department of Climate Change, Energy, the Environment and Water (DCCEEW, previously DAWE) in:quantifying the potential impacts of the 2019/20 bushfires on wildlife, plants and ecological communities; and,identifying appropriate response and recovery actions.The intent was to derive a reliable, agreed, fit for purpose and repeatable national dataset of burnt areas across Australia for the 2019/20 bushfire season.The NIAFED was first published on 13 February 2020 and was updated several times during 2020 to reflect updates to fire extent datasets from state and territory agencies. Most changes across these versions, after February (end of summer), reflect refinements on previous extent mapping, rather than new burnt areas. Fire analyses and decision making within the department after June 2020 has been based on the GEEBAM dataset. The GEEBAM dataset reports on fire severity within the NIAFED v20200225 extent envelope and includes some areas determined to be unburnt within NIAFED areas.NOTE: previous versions of this dataset are available on request to geospatial@dcceew.gov.auThe dataset takes the national Emergency Management Spatial Information Network Australia (EMSINA) data service, which is the official fire extent currently used by the Commonwealth and adds supplementary data from other sources to form a cumulative national view of fire extent. This EMSINA data service shows the current active fire incidents, and the Department map shows the total fire extent from 1 July 2019 to the 22 June 2020.EMSINA have been instrumental in providing advice on access to data and where to make contact in the early stages of developing the National Indicative Aggregated Fire Extent Dataset.This dataset is released on behalf of the Commonwealth Government and endorsed by the National Burnt Area Dataset Working Group, convened by the National Bushfire Recovery Agency.Known Issues:The dataset has a number of known issues, both in its conceptual design and in the quality of its inputs. These are outlined below and should be taken into account in interpreting the data and developing any derived analyses.The list of known issues below is not comprehensive: it is anticipated that further issues will be identified in the future, and the Department welcomes feedback on this. We will seek as far as possible to continuously improve the dataset in future versions.In addition, the 2019/20 bushfire season is ongoing and it can be expected that the fire extent will increase.Future versions of the dataset will therefore document and distinguish between changes arising from methodological improvement, as distinct from changes to the actual fire extent.The dataset draws data together from multiple different sources, including from state and territory agencies responsible for emergency and natural resource management, and from the Northern Australian Fire Information website. The variety of mapping methods means that conceptually the dataset lacks national coherency. The limitations associated with the input datasets are carried through to this dataset. Users are advised to refer to the input datasets’ documentation to better understand limitations.The dataset is intentionally precautionary and the rulesets for its creation elect to accept the risk of overstating the size of particular burnt areas. If and when there are overlapping polygons for an area, the internal boundaries have been dissolved.The dataset shows only the outline of burnt areas and lacks information on fire severity in these areas, which may often include areas within them that are completely unburnt. For the intended purpose this may limit the usability of the data, particularly informing on local environmental impacts and response. This issue will be given priority, either for future versions of the dataset or for development of a separate, but related, fire severity product.This continental dataset includes large burnt areas, particularly in northern Australia, which can be considered part of the natural landscape dynamics. For the intended purpose of informing on fire of potential environmental impact, some interpretation and filtering may be required. There are a variety of ways to do this, including by limiting the analysis to southern Australia, as was done for recent Wildlife and Threatened Species Bushfire Recovery Expert Panel’s preliminary analysis of 13 January 2020. For that preliminary analysis area, boundaries from the Interim Biogeographic Regionalisation of Australia version 7 were used by the Department to delineate an area of southern Australia encompassing the emergency bushfire areas of the southern summer. The Department will work in consultation with the expert panel and other relevant bodies in the future on alternative approaches to defining, spatially or otherwise, fire of potential environmental impact.The dataset cannot be used to reliably recreate what the national burnt area extent was at a given date prior to the date of release. Reasons for this include that information on the date/time on individual fires may or may not have been provided in the input datasets, and then lost as part of the dissolve process discussed in issue 2 above.With fires still burning extents are not yet refined.Fire extents are downloaded daily, and datasets are aggregated. This results in an overlap of polygon extents and raises the issue that refined extents are disregarded at this early stage.The Northern Australian Fire Information (NAFI) dataset is only current to 19 June 2020.

This map was created by the Department of Primary Industries (Office of Water) in 2013 to identify areas in NSW with a reliable surface water supply. Land with reliable surface water was defined as areas:

• buffering all regulated rivers and creeks by 150 metres,
• buffering unregulated rivers and creeks with a 5th stream order or higher, by 150 metres,
• buffering 3rd and 4th stream order unregulated rivers and creeks, by 150 metres

Reliable surface water mapping along with two other datasets (rainfall of 350mm for more per annum - 9 out of 10 years and highly productive groundwater) are used to identify land with access to a reliable water supply, forming part of the regional and site level assessment classification of Biophysical Strategic Agricultural Land (BSAL).

Under the Mining SEPP, all State Significant Development applications require a Site Verification Certificate to determine if their site contains any BSAL and therefore requiring further assessment from the Mining and Petroleum Gateway Panel. This process is managed by Planning and Assessment, Department of Planning, Industry and Environment and are custodian of the dataset.

A pdf map and GIS shapefile of this dataset is accessible from the resources section of the metadata.