This web map contains reference data points with specific site information on vegetation dominance type and tree size for the existing vegetation type mapping for the Glacier Project Area, Chugach National Forest.Reference data for this project came from three sources including: 1) Forest Service and RedCastle Resources field crews collecting vegetation information specific to this project in 2021-2022 (695 total); 2) legacy survey plots from the Forest Inventory and Analysis (FIA) program (21 total) (this data set does not contain FIA data); and 3) image interpreted sites (229 total).Chugach National Forest and RedCastle personnel collected most of the ground data for this mapping effort using a variety of access means—such as, by helicopter, floatplane, boat, or by foot from existing trail and road infrastructure. The FIA data were cross-referenced with the classification key to label each plot with a vegetation type class. Image interpretation was used to bolster the number of reference sites. Reference data was consolidated into a single database and reviewed within the context of their corresponding mapping segment using high-resolution imagery.For more detailed information on mapping methodology please see the Glacier Existing Vegetation Project Report.

This record presents the data underlying Skills4EOSC Deliverable D6.1 Mapping of existing professional networks and relevant documentation of the search string.

Reference data – video stations are part of marine ground maps in the coastal zone. It is based on video analysis and shows the locations where the mapping project Marine ground maps in the coastal zone has collected video material. The analyses from the video stations are presented in various point data map layers at www.marinegrunnkart.avinet.no

This web map contains reference data points with specific site information on vegetation dominance type and tree size for the Tongass National Forest, Ketchikan Misty Fjords Project Area to provide up-to-date and more complete information about vegetative communities, structure, and patterns across the project area.Reference data for this project came from numerous sources including: 1) Forest Service field crews collecting vegetation information specific to this project in 2019-2021 (582 total); 2) Young Growth Inventory data (1,444 total); 3) legacy data from previous Forest Service survey plots (556 total) and the Forest Inventory and Analysis (FIA) program; and 4) field data from Annette Islands supplied by the Bureau of Indian Affairs (94 total). These data posted here do not contain the FIA data nor the Bureau of Indian Affairs field data.Tongass National Forest personnel collected most of the ground data for this mapping effort using a variety of access means—such as, by helicopter, floatplane, boat, or by foot from existing trail and road infrastructure. The Young Growth Inventory information was leveraged for forests that are currently, or have been, actively managed in the past. The legacy, FIA, and Annette Islands data were all cross-referenced with the classification key to label each plot with a vegetation type class. Reference data was consolidated into a single database and reviewed within the context of their corresponding mapping segment using high-resolution imagery.For more detailed information on mapping methodology please see the Ketchikan Misty Fjords Existing Vegetation Project Report.

Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019.

Number of sample sites: 500

Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos.

Tool for data collection: Geo-Wiki

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

1. INPUT 200 SATELLITE IMAGES

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

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

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

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

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

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

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

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

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

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

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

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

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

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

This is a report of all records used to plot values on an interactive map on NJ Office of State Comptroller website showing expenditures related to Superstorm Sandy.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011 and 2016. U.S. EPA leads the accuracy assessment of NLCD land cover products in coordination with USGS and SUNY-ESF. The posted dataset includes map labels and reference labels for 4629 sample locations (pixels) from the NLCD 2016 database. The sample pixels were selected using a stratified random design based on the dual 2011 – 2016 map labels. The data can be used to re-create the accuracy results reported in a forthcoming paper and in support of other applications. Accuracy results are reported for the 2011 and 2016 land cover products in the NLCD2016 database and for selected 2011-2006 changes.

This is a report of the column descriptions of the columns in the Detail Report.

Introducing the “**Countries by Region**” dataset, a simple yet handy reference for quickly associating countries with their respective market regions. This dataset is ideal for straightforward lookups of market names based on country information. It simplifies your data tasks when you need a quick mapping between countries and their regional markets.

Examples of Use:

•  Data Lookup: Easily find the market region for any country in your dataset.

Example: Need to know the market region for “Canada”? This dataset has you covered. • Data Integration: Seamlessly integrate this dataset into your projects where country-to-market region mapping is required. Example: Merge this dataset with your sales data to categorise sales by market region. • Data Validation: Ensure the accuracy of market region data in your records by cross-referencing with this dataset. Example: Check if the market region for “Brazil” in your database matches the entry here.

Simplify your data work with the “Countries by Region” dataset. It’s your quick and reliable table for country-to-market region associations.

Dataset used in the publication "Multi-source mapping of peatland types using Sentinel-1, Sentinel-2 and terrain derivatives – A comparison between five high-latitude landscapes". The dataset includes preprocessed predictor variables in image format (geoTIFF) from Sentinel-1, Sentinel-2 and Copernicus DEM for the five sites, including North Slope (Alaska), Yukon (Canada), Great Slave Lake (Canada), Hudson Bay Lowlands (Canada) and northern Sweden (Scandinavia). It also includes reference data (shape files) used for training and validation of classification models.

The dataset includes preprocessed predictor variables in image format (geoTIFF) from Sentinel-1, Sentinel-2 and Copernicus DEM for the five sites, including North Slope (Alaska), Yukon (Canada), Great Slave Lake (Canada), Hudson Bay Lowlands (Canada) and northern Sweden (Scandinavia). It also includes reference data (shape files) used for training and validation of classification models.

The dataset was originally published in DiVA and moved to SND in 2024.

Our ability to completely and repeatedly map natural environments at a global scale have increased significantly over the past decade. These advances are from delivery of a range of on-line global satellite image archives and global-scale processing capabilities, along with improved spatial and temporal resolution satellite imagery. The ability to accurately train and validate these global scale-mapping programs from what we will call “reference data sets” is challenging due to a lack of coordinated financial and personnel resourcing, and standardized methods to collate reference datasets at global spatial extents. Here, we present an expert-driven approach for generating training and validation data on a global scale, with the view to mapping the world’s coral reefs. Global reefs were first stratified into approximate biogeographic regions, then per region reference data sets were compiled that include existing point data or maps at various levels of accuracy. These reference data sets were compiled from new field surveys, literature review of published surveys, and from individually sourced contributions from the coral reef monitoring and management agencies. Reference data were overlaid on high spatial resolution satellite image mosaics (3.7 m × 3.7 m pixels; Planet Dove) for each region. Additionally, thirty to forty satellite image tiles; 20 km × 20 km) were selected for which reference data and/or expert knowledge was available and which covered a representative range of habitats. The satellite image tiles were segmented into interpretable groups of pixels which were manually labeled with a mapping category via expert interpretation. The labeled segments were used to generate points to train the mapping models, and to validate or assess accuracy. The workflow for desktop reference data creation that we present expands and up-scales traditional approaches of expert-driven interpretation for both manual habitat mapping and map training/validation. We apply the reference data creation methods in the context of global coral reef mapping, though our approach is broadly applicable to any environment. Transparent processes for training and validation are critical for usability as big data provide more opportunities for managers and scientists to use global mapping products for science and conservation of vulnerable and rapidly changing ecosystems.

This dataset is a mapping between MEANS-InOut input data and Life Cycle Inventories from reference databases (Agribalyse, ecoinvent). The MEANS-InOut input data are agricultural production system inputs (fertilisers, plant protection products, agricultural operations, livestock feed, ingredients to be incorporated into livestock feed, etc.). Each input is associated with one or more LCI, which represent(s) the impacts of the production of this input, and the database from which the LCI(s) is from. This version of the dataset corresponds to the following versions of the databases: Agribalyse v3.1.1 and ecoinvent v3.9. The correspondence file (named mapping_data.tab) is associated with : a document describing the input types in the MEANS-InOut software (file: Input_type_description.pdf), a document describing how the value of the input flow of a LCI for an agricultural system studied in MEANS-InOut is obtained from the value taken by this input in MEANS-InOut. (file: LCI_value_construction.pdf) Ce jeu de données établit la correspondance entre les référentiels de MEANS-InOut et des Inventaires de Cycle de Vie de base de données de référence (Agribalyse, ecoinvent). Les référentiels de MEANS-InOut sont des intrants des systèmes de production agricole (engrais, produits phytosanitaires, opérations agricoles, aliments du bétail, ingrédients à incorporer dans les aliments composés...). A chaque intrant est associé un ou plusieurs ICV, qui représentent les impacts de la production de cet intrant, et la base de données dont le ou les ICV sont issus. Cette version du jeu de données fait la correspondance avec les versions suivantes des bases de données : Agribalyse v3.1.1 et ecoinvent v3.9. Au fichier de correspondances (fichier : mapping_data.tab), sont associés : un document qui décrit les types d'intrants du logiciel MEANS-InOut (fichier : Input_type_description.pdf), un document qui décrit comment est obtenue la valeur du flux des intrants d'un ICV d'un système agricole étudié dans MEANS-InOut à partir de la valeur prise par cet un intrant dans MEANS-InOut. (fichier : LCI_value_construction.pdf)

Flown in March 2023. The ground sampling distance (imagery resolution) is 3 inch. Data compiled to meet or exceed a horizontal accuracy of +/- 1.5 feet (46 cm) RMSE. Imagery provided by Nearmap. Access the Data:Access the REST Service from https://ags.roseville.ca.us/arcgis/rest/services/PublicServices/. View the data in our Historical Imagery Collection.Add data to ArcMap or ArcPro by clicking on “View Metadata” and selecting “Open in ArcGIS Desktop”.

This data collection consists of benthic and geomorphic reference samples, generated by University of Queensland (UQ) Global Coral Reef Mapping for used in producing benthic and geomorphic maps. The UQ Global Coral Reef Mapping is part of the Allen Coral Atlas (ACA). The atlas includes global benthic and geomorphic maps in coral reef areas derived from classification of satellite imagery driven by inputs of multiple types of data (field data, water depth, slope, wave, and existing maps). The maps are created following a global mapping approach (Lyons et al 2020), using a global classification scheme (Kennedy et al 2020) requiring the creation of reference samples for calibration and validation purposes (Roelfsema et al 2020). It is built by a dedicated team of scientists, technologists, and conservationists. Led and funded by Vulcan Inc. (Paul G. Allen Philanthropies), the partnership also includes Arizona State University’s Centre for Global Discovery and Conservation Science, the National Geographic Society, Planet, and the University of Queensland. Details of the methods developed and used can be found on the ACA website (https://allencoralatlas.org/methods/) and from published papers (Roelfsema et al. 2013, Roelfsema et al. 2018, Roelfsema et al 2020, Kennedy et al 2020, Lyons et al. 2020). The map products are also accessible via the ACA website (https://allencoralatlas.org/atlas/). The Atlas engagement team aims to have these maps used by scientists, academics, policymakers, and protected area managers.

Data for manuscript "Age-related deficits in reference frame switching of map-based navigation ability" - UNDER REVIEW

This Annual NLCD Reference Data Product includes the collection of an independent dataset of 8,360 30-meter by 30-meter samples across the Conterminous United States (CONUS). The Annual NLCD Collection 1 sample design was developed as a two-phase collection by a team of image interpreters as follows: an initial base sample containing 5,000 sample plots chosen purely by simple random selection, following by another collection of 3,360 sample plots (some of which were selected similarly, while others were targeted at particular map-defined strata) upon completion of the map. This approach results in a final stratified reference sample of size 8,360. The Annual NLCD CONUS Reference Data Product collected variables related to primary and alternate land cover and land use, change processes, and other ancillary variables annually across CONUS from 1984–2023. This product contains Annual National Land Cover Database (NLCD) CONUS Reference plot location data, annual land cover, land use, and change process variables for each reference data plot, information on the 'strata' and phase of collection each plot is associated with, and the strata map and overall strata counts for calculating inclusion probabilities of the stratified samples. The Annual NLCD Reference Data Product was utilized for evaluation and validation of the Annual NLCD CONUS Collection 1.0 land cover and land cover change products.

Flown in March/April 2024. The ground sampling distance (imagery resolution) is 3 inch. Data compiled to meet or exceed a horizontal accuracy of +/- 2.5 feet (75 cm) RMSE. Imagery provided by Nearmap. Access the Data:Access the REST Service from https://ags.roseville.ca.us/arcgis/rest/services/PublicServices/. View the data in our Historical Imagery Collection.Add data to ArcMap or ArcPro by clicking on “View Metadata” and selecting “Open in ArcGIS Desktop”.

Within the ESA funded WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.

This publication includes those harmonized data sets of which the original data set was published under the CC-BY-NC license or a license similar to CC-BY-NC. See document "_In-situ-data-World-Cereal - license - CC-BY-NC.pdf" for an overview of the original data sets. Currently this publication only includes a few small data sets for Tanzania originating from a disease monitoring program of the International Maize and Wheat Improvement Center (CIMMYT). CIMMYT made more data available for countries like Kenya, Ethiopia, Rwanda and, Malawi. However due project contraints these data sets were not yet harmonized.

Flown in March 2005. Data compiled to meet or exceed a horizontal accuracy of +/- 2 feet RMSE or 3.46 feet at a 95% confidence level according to the NSSDA standard necessary for 1”=200’ maps.

Access the Data:

Access the REST Service from https://ags.roseville.ca.us/arcgis/rest/services/PublicServices/. View the data in our Historical Imagery Collection.Add data to ArcMap or ArcPro by clicking on “View Metadata” and selecting “Open in ArcGIS Desktop”.