This feature layer shows the sale of restricted food permits in Hong Kong. It is created by Food and Environmental Hygiene Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is in CSV format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of CSDI Portal at https://portal.csdi.gov.hk.

Hand drawn Utah state border in geojson featurecollection format Projection: WGS84 (EPSG: 4326). This resource was created to test NWM viewer app.

The United States Census Bureau TIGER dataset contains the 2018 boundaries for the primary governmental divisions of the United States. In addition to the fifty states, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the island areas (American Samoa, the Commonwealth of the Northern Mariana …

A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz, G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065. https://doi.org/10.5194/nhess-19-2053-2019 Cohen, S., G. R. Brakenridge, A. Kettner, B. Bates, J. Nelson, R. McDonald, Y. Huang, D. Munasinghe, and J. Zhang (2018), Estimating Floodwater Depths from Flood Inundation Maps and Topography, Journal of the American Water Resources Association, 54 (4), 847–858. https://doi.org/10.1111/1752-1688.12609 Sample products and data availability: https://sdml.ua.edu/models/fwdet/ https://sdml.ua.edu/michigan-flood-may-2020/ https://cartoscience.users.earthengine.app/view/fwdet-gee-mi https://alabama.app.box.com/s/31p8pdh6ngwqnbcgzlhyk2gkbsd2elq0 GEE implementation output: fwdet_gee_brazos.tif ArcMap implementation output (see Cohen et al. 2019): fwdet_v2_brazos.tif iRIC validation layer (see Nelson et al. 2010): iric_brazos_hydraulic_model_validation.tif Brazos River inundation polygon access in GEE: var brazos = ee.FeatureCollection('users/cartoscience/FwDET-GEE-Public/Brazos_River_Inundation_2016') Nelson, J.M., Shimizu, Y., Takebayashi, H. and McDonald, R.R., 2010. The international river interface cooperative: public domain software for river modeling. In 2nd Joint Federal Interagency Conference, Las Vegas, June (Vol. 27). Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # FwDET-GEE calculates floodwater depth from a floodwater extent layer and a DEM Authors: Brad G. Peter, Sagy Cohen, Ronan Lucey, Dinuke Munasinghe, Austin Raney Emails: bpeter@ua.edu, sagy.cohen@ua.edu, ronan.m.lucey@nasa.gov, dsmunasinghe@crimson.ua.edu, aaraney@crimson.ua.edu Organizations: BP, SC, DM, AR - University of Alabama; RL - University of Alabama in Huntsville Last Modified: 10/08/2020 To cite this code use: Peter, Brad; Cohen, Sagy; Lucey, Ronan; Munasinghe, Dinuke; Raney, Austin, 2020, "A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET-GEE)", https://doi.org/10.7910/DVN/JQ4BCN, Harvard Dataverse, V2 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDETv2.0) [1] developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet ------------------------------------------------------------------------------------------------------------------------- How to run this code with your flood extent GEE asset: User of this script will need to update path to flood extent (line 32 or 33) and select from the processing options. Available DEM options (1) are USGS/NED (U.S.) and USGS/SRTMGL1_003 (global). Other options include (2) running the elevation outlier filtering algorithm, (3) adding water body data to the inundation extent, (4) add a water body data layer uploaded by the user rather than using the JRC global surface water data, (5) masking out regular water body data, (6) masking out 0 m depths, (7) choosing whether or not to export, (8) exporting additional data layers, and (9) setting an export file name. The simpleVis option (10) bypasses the time consuming processes and is meant for visualization only; set this option to false to complete the entire process and enable exporting. ------------------------------------------------------------------------------------------------------------------------- ••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• Load flood extent layer | Flood extent layer must be uploaded to GEE first as an asset. If the flood extent is a shapefile, upload as a FeatureCollection; otherwise, if the flood extent layer is a raster, upload it as an image. A raster layer may be required if the flood extent is a highly complex geometry -------------------------------------- */ var flood = ee.FeatureCollection('users/username/folder/flood_extent') // comment out this line if using an Image // var flood = ee.Image('users/username/folder/flood_extent') // comment out this line if using a FeatureCollection var waterExtent = ee.FeatureCollection('users/username/folder/water_extent') // OPTIONAL comment out this line if using an Image // var waterExtent = ee.Image('users/username/folder/water_extent') // OPTIONAL comment out this line if using a FeatureCollection // Processing options - refer to the directions above /*1*/ var demSource = 'USGS/NED' // 'USGS/NED' or 'USGS/SRTMGL1_003' /*2*/ var outlierTest = 'TRUE' // 'TRUE' (default) or 'FALSE' /*3*/ var addWater = 'TRUE' // 'TRUE' (default) or 'FALSE' /*4*/ var userWater = 'FALSE' // 'TRUE' or 'FALSE' (default) /*5*/ var maskWater = 'FALSE' // 'TRUE' or 'FALSE' (default) /*6*/ var maskZero = 'FALSE' // 'TRUE' or 'FALSE' (default) /*7*/ var exportLayer = 'TRUE' // 'TRUE' (default) or 'FALSE' /*8*/ var exportAll = 'FALSE' // 'TRUE' or 'FALSE' (default) /*9*/ var outputName = 'FwDET_GEE' // text string for naming export file /*10*/ var simpleVis = 'FALSE' // 'TRUE' or 'FALSE' (default) // ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT •••••••••••••••••••••••••••••••••••••••••••••••••••• // Create buffer around flood area to use for clipping other layers var area = flood.geometry().bounds().buffer(1000).bounds() // Load DEM and grab projection info var dem = ee.Image(demSource).select('elevation').clip(area) // [2,3] var projection = dem.projection() var resolution = projection.nominalScale().getInfo() // Load global surface water layer var jrc = ee.Image('JRC/GSW1_1/GlobalSurfaceWater').select('occurrence').clip(area) // [4] var water_image = jrc // User uploaded flood extent layer // Identify if a raster or vector layer is being used and proceed with appropriate process if ( flood.name() == 'FeatureCollection' ) { var addProperty = function(feature) { return feature.set('val',0); }; var flood_image = flood.map(addProperty).reduceToImage(['val'],ee.Reducer.first()) .rename('flood') } else { var flood_image = flood.multiply(0) } // Optional user uploaded water extent layer if ( userWater == 'TRUE' ) { // Identify if a raster or vector layer is being used and proceed with appropriate process if ( waterExtent.name() == 'FeatureCollection' ) { var addProperty = function(feature) { return feature.set('val',0); }; var water_image = waterExtent.map(addProperty).reduceToImage(['val'],ee.Reducer.first()) .rename('flood') } else { var water_image = waterExtent.multiply(0) } } // Add water bodies to flood extent if 'TRUE' is selected if ( addWater == 'TRUE' ) { var w = water_image.reproject(projection) var waterFill = flood_image.mask().where(w.gt(0),1) flood_image = waterFill.updateMask(waterFill.eq(1)).multiply(0) } // Change processing options if 'TRUE' is selected if ( simpleVis == 'FALSE' ) { flood_image = flood_image.reproject(projection) } else { outlierTest = 'FALSE' exportLayer = 'FALSE' } // Run the outlier filtering process if 'TRUE' is selected if ( outlierTest == 'TRUE' ) { // Outlier detection and filling on complete DEM using the modified z-score and a median filter [5] var kernel = ee.Kernel.fixed(3,3,[[1,1,1],[1,1,1],[1,1,1]]) var kernel_weighted = ee.Kernel.fixed(3,3,[[1,1,1],[1,0,1],[1,1,1]]) var median = dem.focal_median({kernel:kernel}).reproject(projection) var median_weighted = dem.focal_median({kernel:kernel_weighted}).reproject(projection) var diff = dem.subtract(median) var mzscore = diff.multiply(0.6745).divide(diff.abs().focal_median({kernel:kernel}).reproject(projection)) var fillDEM = dem.where(mzscore.gt(3.5),median_weighted) // Outlier detection and filling on the flood extent border pixels var expand = flood_image.focal_max({kernel: ee.Kernel.square({ radius: projection.nominalScale(), units: 'meters' })}).reproject(projection) var demMask = fillDEM.updateMask(flood_image.mask().eq(0)) var boundary = demMask.add(expand) var medianBoundary = boundary.focal_median({kernel:kernel}).reproject(projection) var medianWeightedBoundary = boundary.focal_median({kernel:kernel_weighted}).reproject(projection) var diffBoundary = boundary.subtract(medianBoundary) var mzscoreBoundary = diffBoundary.multiply(0.6745).divide(diffBoundary.abs().focal_median({kernel:kernel}).reproject(projection)) var fill =

The United States Office of the Geographer provides the Large Scale International Boundary (LSIB) dataset. It is derived from two other datasets: a LSIB line vector file and the World Vector Shorelines (WVS) from the National Geospatial-Intelligence Agency (NGA). The interior boundaries reflect U.S. government policies on boundaries, boundary disputes, and sovereignty. The exterior boundaries are derived from the WVS; however, the WVS coastline data is outdated and generally shifted from between several hundred meters to over a kilometer. Each feature is the polygonal area enclosed by interior boundaries and exterior coastlines where applicable, and many countries consist of multiple features, one per disjoint region. Each of the 180,741 features is a part of the geometry of one of the 284 countries described in this dataset.

Use General Infrastructure to indicate the location of various types of infrastructure.

This is a feature collection created from the geometries of L2B tables in LARSE/GEDI/GEDI02_B_002. Each feature is a polygon footprint of a source table with its asset id and start/end timestamps. Please see User Guide for more information. The Global Ecosystem Dynamics Investigation GEDI mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth's carbon cycle and biodiversity. The GEDI instrument, attached to the International Space Station (ISS), collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of the 3-dimensional structure of the Earth. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. ProductDescriptionL2A VectorLARSE/GEDI/GEDI02_A_002L2A Monthly rasterLARSE/GEDI/GEDI02_A_002_MONTHLYL2A table indexLARSE/GEDI/GEDI02_A_002_INDEXL2B VectorLARSE/GEDI/GEDI02_B_002L2B Monthly rasterLARSE/GEDI/GEDI02_B_002_MONTHLYL2B table indexLARSE/GEDI/GEDI02_B_002_INDEXL4A Biomass VectorLARSE/GEDI/GEDI04_A_002L4A Monthly rasterLARSE/GEDI/GEDI04_A_002_MONTHLYL4A table indexLARSE/GEDI/GEDI04_A_002_INDEXL4B BiomassLARSE/GEDI/GEDI04_B_002

This Feature Collection was converted from a Geocommons dataset on Tue Sep 10 2013 by jcardona_dcdev. The original dataset can be viewed at http://geocommons.com/overlays/355328.View the Feature Collection heresamplepolys2 - This dataset was converted successfullyGeometry Types: Polygons

This is a feature collection created from the geometries of L4A tables in LARSE/GEDI/GEDI04_A_002. Each feature is a polygon footprint of a source table with its asset id and start/end timestamps. Please see User Guide for more information. The Global Ecosystem Dynamics Investigation GEDI mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth's carbon cycle and biodiversity. The GEDI instrument, attached to the International Space Station (ISS), collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of the 3-dimensional structure of the Earth. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. ProductDescriptionL2A VectorLARSE/GEDI/GEDI02_A_002L2A Monthly rasterLARSE/GEDI/GEDI02_A_002_MONTHLYL2A table indexLARSE/GEDI/GEDI02_A_002_INDEXL2B VectorLARSE/GEDI/GEDI02_B_002L2B Monthly rasterLARSE/GEDI/GEDI02_B_002_MONTHLYL2B table indexLARSE/GEDI/GEDI02_B_002_INDEXL4A Biomass VectorLARSE/GEDI/GEDI04_A_002L4A Monthly rasterLARSE/GEDI/GEDI04_A_002_MONTHLYL4A table indexLARSE/GEDI/GEDI04_A_002_INDEXL4B BiomassLARSE/GEDI/GEDI04_B_002

Web-based GIS for spatiotemporal crop climate niche mapping Interactive Google Earth Engine Application—Version 2, July 2020 https://cropniche.cartoscience.com https://cartoscience.users.earthengine.app/view/crop-niche Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # CropSuit-GEE Authors: Brad G. Peter (bpeter@ua.edu), Joseph P. Messina, and Zihan Lin Organizations: BGP, JPM - University of Alabama; ZL - Michigan State University Last Modified: 06/28/2020 To cite this code use: Peter, B. G.; Messina, J. P.; Lin, Z., 2019, "Web-based GIS for spatiotemporal crop climate niche mapping", https://doi.org/10.7910/DVN/UFC6B5, Harvard Dataverse, V1 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine crop climate suitability geocommunication and map export tool designed to support agronomic development and deployment of improved crop system technologies. This content is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by USAID under Cooperative Agreement No. AID-OAA-L-14-00006. ------------------------------------------------------------------------------------------------------------------------- Summarization of input options: There are 14 user options available. The first is a country of interest selection using a 2-digit FIPS code (link available below). This selection is used to produce a rectangular bounding box for export; however, other geometries can be selected with minimal modification to the code. Options 2 and 3 specify the complete temporal range for aggregation (averaged across seasons; single seasons may also be selected). Options 4–7 specify the growing season for calculating total seasonal rainfall and average season temperatures and NDVI (NDVI is for export only and is not used in suitability determination). Options 8–11 specify the climate parameters for the crop of interest (rainfall and temperature max/min). Option 12 enables masking to agriculture, 13 enables exporting of all data layers, and 14 is a text string for naming export files. ------------------------------------------------------------------------------------------------------------------------- ••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• */ // CHIRPS data availability: https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD // MOD11A2 data availability: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 var country = 'MI' // [1] https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var startRange = 2001 // [2] var endRange = 2017 // [3] var startSeasonMonth = 11 // [4] var startSeasonDay = 1 // [5] var endSeasonMonth = 4 // [6] var endSeasonDay = 30 // [7] var precipMin = 750 // [8] var precipMax = 1200 // [9] var tempMin = 22 // [10] var tempMax = 32 // [11] var maskToAg = 'TRUE' // [12] 'TRUE' (default) or 'FALSE' var exportLayers = 'TRUE' // [13] 'TRUE' (default) or 'FALSE' var exportNameHeader = 'crop_suit_maize' // [14] text string for naming export file // ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT •••••••••••••••••••••••••••••••••••••••••••••••••••• // Access precipitation and temperature ImageCollections and a global countries FeatureCollection var region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017') .filterMetadata('country_co','equals',country) var precip = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD').select('precipitation') var temp = ee.ImageCollection('MODIS/006/MOD11A2').select(['LST_Day_1km','LST_Night_1km']) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select(['NDVI']) // Create layers for masking to agriculture and masking out water bodies var waterMask = ee.Image('UMD/hansen/global_forest_change_2015').select('datamask').eq(1) var agModis = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1').mode() .remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], [0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0]) var agGC = ee.Image('ESA/GLOBCOVER_L4_200901_200912_V2_3').select('landcover') .remap([11,14,20,30,40,50,60,70,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230], [1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) var cropland = ee.Image('USGS/GFSAD1000_V1').neq(0) var agMask = agModis.add(agGC).add(cropland).gt(0).eq(1) // Modify user input options for processing with raw data var years = ee.List.sequence(startRange,endRange) var bounds = region.geometry().bounds() var tMinMod = (tempMin+273.15)/0.02 var tMaxMod = (tempMax+273.15)/0.02 //...

MTUPP Tobacco Retailers and Schools

These are pdf files of the original survey feature/collection forms. Each survey feature was recorded on a form and assigned a unique number, and, if the feature was collected, that number pertains to the surface collection. Some features have more than one collection, in which case additional feature/collection numbers were assigned. Rarely numbers were subsequently de-assigned, and might then be assigned to a different feature/collection later, but occasionally numbers were not used (skipped), especially because distinct blocks of numbers were used in different survey seasons. Some files include sketch or gps maps.

The Global Administrative Unit Layers (GAUL) compiles and disseminates the best available information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. departments), and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth, and lowers levels. The overall methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System, and e) distribute data to the users (see Technical Aspects of the GAUL Distribution Set. Note that some administrative units are multipolygon features.

This dataset contains a sample of 10,000 (3.5%) out of a total of 285,846 text sequences extracted from the 1891–1896 Map of London by the Ordnance Survey (OS).

The methodology used for the automated recognition, linking, and sequencing of the text is detailed in the article Recognizing and Sequencing Multi-word Texts in Maps Using an Attentive Pointer by M. Zou et al., 2025.

Description of the content

The map is drawn at a scale of five-feet to the mile (c.a. 1:1,056). The text on the map is an invaluable source of information about the Greater London in the late Victorian period. It includes the names of streets, squares, parks, watercourses and even some estates ('Poplars', 'The Grange', 'Arbutus Lodge'). In addition, the map contains many details of the function of buildings and economic activity, such as factories ('Sweet Factory', 'Crown Linoleum Works', 'Imperial Flour Mills', 'Lion Brewery'), warehouses or commercial infrastructure ('Warehouse', 'Jamaica Wharf', 'Rag Store'), offices ('Offices'), etc. The map also mentions public buildings such as schools ('School Boys, Girls & Infants', 'Sunday School'), hospitals or clinics ('St. Saviour's Union Infirmary', 'Beulah Spa Hydropathic Establishment', 'South Western Fever Hospital'), railway stations ('Clapham Station'), post offices, banks, police stations, etc. Other social venues are also mentioned, such as public houses, i.e. pubs ('P.H.'), clubs, casinos, and recreational areas (e.g. 'Cricket Ground'). Special attention is given to churches, with a regular count of the number of seats (e.g. 'Baptist Chapel Seats for 600').

In addition, the map provides details that can be of great interest in the study of everyday life in London at the end of the 19th century. For example, there are numerous mentions of 'Stables', 'Drinking Fountain'[s] (or simply 'Fn.') or 'Urinal'[s]. Fire protection infrastructure is highlighted, e.g. fire plugs ('F.P.') and fire alarms ('F.A.'). The map also includes information on elevation (e.g. '11·6') and flood levels (e.g. 'High Water Mark of Ordinary Tides').

A list of abbreviations used in the Ordnance Survey maps, created by Richard Oliver [1], is made available by the National Library of Scotland (link).

Organization of the data

The data in 10k_text_london_OS_1890s.geojson is organized as a regular geojson file.

Example structure

{
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"geometry": {
"type": "MultiPolygon",
"coordinates": [[[ [x1, y1], [x2, y2], ...]]]
},
"properties": {
"label": "Oxford Circus",
}
},

... # Further text sequences


]
}

Image documents

The original map document consists of 729 separate sheets, digitized, georeferenced, and served as geographic tiles by the National Library of Scotland [2].

Descriptive statistics

Total Number of text sequences: 285,846
Sample size: 10,000
Total Area covered: 450 square km

Use and Citation

For any mention of this dataset, please cite :

@misc{text_london_OS_1890s,
author = {Zou, Mengjie and Petitpierre, R{\'{e}}mi and di Lenardo, Isabella},
title = {{London 1890s Ordnance Survey Text Layer}},
year = {2025},
publisher = {Zenodo},
url = {https://doi.org/10.5281/zenodo.14982946}}

@article{recognizing_sequencing_2025,
author = {Zou, Mengjie and Dai, Tianhao and Petitpierre, R{\'{e}}mi and Vaienti, Beatrice and di Lenardo, Isabella},
title = {{Recognizing and Sequencing Multi-word Texts in Maps Using an Attentive Pointer}},
year = {2025}}

Corresponding author

Rémi PETITPIERRE - remi.petitpierre@epfl.ch - ORCID - Github - Scholar - ResearchGate

License

This project is licensed under the CC BY 4.0 License.

Liability

We do not assume any liability for the use of this dataset.

References

1. Oliver R. (2013). Ordnance Survey maps: A concise guide for historians. The Charles Close Society. London, UK. 3rd Ed. 320 pages
2. Ordnance Survey, London, five feet to the mile, 1893-1896 (1896), https://maps.nls.uk/os/townplans-england/london-1056-1890s.html, digitized by the National Library of Scotland (NLS)

Use Oil & Gas Exploration for the inspection and inventory of oil and gas production facilities.

The contain data generated by the recogito software (https://recogito.pelagios.org/) in processing the text of Peter Graves' translation. of Linnaeus's Laplandfic Journey (Edinburgh Universioty Press, 1995). Recogito allows to tag place names with UUIDs and correlate these with geolocation data (taken from geonames). Locations can then be displayed on a map that links them with correspondiong sections of the text. Files include a .csv file correlating UUIDs, place names and geonames URI, a GeoJSON FeatureCollection with confirmed geo-located places in the document, and the annotated text in a basic TEI/XML serialization.