Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Geospatial raster data and vector data created in the frame of the study "Mapping Arctic Lake Ice Backscatter Anomalies using Sentinel-1 Time Series on Google Earth Engine" submitted to the journal "Remote Sensing" and Python code to reproduce the results.

In addition to the full repository (Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies.zip), two reduced alternatives of this repository are available due to large file size of the full repository:

Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies_without_IW_result_data.zip contains the same data and Python scripts as the full repository, but results based on IW data and tiled EW delta sigma0 images directly exported from Google Earth Engine have been removed. The merged data (from tiled EW delta sigma0 images) and all other results deduced thereof are included.

Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies_scripts_and_reference_data_only.zip contains only the Python scripts and reference data. The directory structure was retained for better reproducibility.

Please see the associated README-files for details.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the …

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 =

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. This dataset includes all 50 variables as available on CDS. ERA5-Land data is available from 1950 to three months from real-time. Please consult the ERA5-Land "Known Issues" section. In particular, note that three components of the total evapotranspiration have values swapped as follows: variable "Evaporation from bare soil" (mars parameter code 228101 (evabs)) has the values corresponding to the "Evaporation from vegetation transpiration" (mars parameter 228103 (evavt)), variable "Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the "Evaporation from bare soil" (mars parameter code 228101 (evabs)), variable "Evaporation from vegetation transpiration" (mars parameter code 228103 (evavt)) has the values corresponding to the "Evaporation from open water surfaces excluding oceans" (mars parameter code 228102 (evaow)). The asset is a daily aggregate of ECMWF ERA5 Land hourly assets which includes both flow and non-flow bands. Flow bands are formed by collecting the first hour's data of the following day which holds aggregated sum of previous day and while the non-flow bands are created by averaging all hourly data of the day. The flow bands are labeled with the "_sum" identifier, which approach is different from the daily data produced by Copernicus Climate Data Store, where flow bands are averaged too. Daily aggregates have been pre-calculated to facilitate many applications requiring easy and fast access to the data. Precipitation and other flow (accumulated) bands might occasionally have negative values, which doesn't make physical sense. At other times their values might be excessively high. This problem is due to how the GRIB format saves data: it simplifies or "packs" the data into smaller, less precise numbers, which can introduce errors. These errors get worse when the data varies a lot. Because of this, when we look at the data for a whole day to compute daily totals, sometimes the highest amount of rainfall recorded at one time can seem larger than the total rainfall measured for the entire day. To learn more, Please see: "Why are there sometimes small negative precipitation accumulations"

GEE-TED: A tsetse ecological distribution model for Google Earth Engine Associated publication forthcoming: Fox, L., Peter, B. G., Frake, A. N., and Messina, J. P. (Forthcoming). A Bayesian maximum entropy model for predicting tsetse ecological distributions. Journal TBD. Description GEE-TED is a Google Earth Engine (GEE; Gorelick et al. 2017) adaptation of a tsetse ecological distribution (TED) model developed by DeVisser et al. (2010), which was designed for use in ESRI's ArcGIS. TED uses time-series climate and land-use/land-cover (LULC) data to predict the probability of tsetse presence across space based on species habitat preferences (in this case Glossina Morsitans). Model parameterization includes (1) day and night temperatures (MODIS Land Surface Temperature; MOD11A2), (2) available moisture/humidity using a vegetation index as a proxry (MODIS NDVI; MOD13Q1), (3) LULC (MODIS Land Cover Type 1; MCD12Q1), (4) year selections, and (5) fly movement rate (meters/16-days). TED has also been used as a basis for the development of an agent-based model by Lin et al. (2015) and in a cost-benefit analysis of tsetse control in Tanzania by Yang et al. (2017). Parameterization in Fox et al. (Forthcoming): Suitable LULC types and climate thresholds used here are specific to Glossina Morsitans in Kenya and are based on the parameterization selections in DeVisser et al. (2010) and DeVisser and Messina (2009). Suitable temperatures range from 17–40°C during the day and 10–40°C at night and available moisture is characterized as NDVI > 0.39. Suitable LULC comprises predominantly woody vegetation; a complete list of suitable categories is available in DeVisser and Messina (2009). In the Fox et al. (Forthcoming) publication, two versions of MCD12Q1 were used to assess suitable LULC types: Versions 051 and 006. The GeoTIFF supplied in this dataset entry (GEE-TED_Kenya_2016-2017.tif) uses the aforementioned parameters to show the probable tsetse distribution across Kenya for the years 2016-2017. A static graphic of this GEE-TED output is shown below and an interactive version can be viewed at: https://cartoscience.users.earthengine.app/view/gee-ted. Figure associated with Fox et al. (Forthcoming) GEE code The code supplied below is generalizable across geographies and species; however, it is highly recommended that parameterization is given considerable attention to produce reliable results. Note that output visualization on-the-fly will take some time and it is recommended that results be exported as an asset within GEE or exported as a GeoTIFF. Note: Since completing the Fox et al. (Forthcoming) manuscript, GEE has removed Version 051 per NASA's deprecation of the product. The current release of GEE-TED now uses only MCD12Q1 Version 006; however, alternative LULC data selections can be used with minimal modification to the code. // Input options var tempMin = 10 // Temperature thresholds in degrees Celsius var tempMax = 40 var ndviMin = 0.39 // NDVI thresholds; proxy for available moisture/humidity var ndviMax = 1 var movement = 500 // Fly movement rate in meters/16-days var startYear = 2008 // The first 2 years will be used for model initialization var endYear = 2019 // Computed probability is based on startYear+2 to endYear var country = 'KE' // Country codes - https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var crs = 'EPSG:32737' // See https://epsg.io/ for appropriate country UTM zone var rescale = 250 // Output spatial resolution var labelSuffix = '02052020' // For file export labeling only //[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] MODIS/006/MCD12Q1 var lulcOptions006 = [1,1,1,1,1,1,1,1,1, 0, 1, 0, 0, 0, 0, 0, 0] // 1 = suitable 0 = unsuitable // No more input required ------------------------------ // var region = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") .filterMetadata('country_co', 'equals', country) // Input parameter modifications var tempMinMod = (tempMin+273.15)/0.02 var tempMaxMod = (tempMax+273.15)/0.02 var ndviMinMod = ndviMin*10000 var ndviMaxMod = ndviMax*10000 var ndviResolution = 250 var movementRate = movement+(ndviResolution/2) // Loading image collections var lst = ee.ImageCollection('MODIS/006/MOD11A2').select('LST_Day_1km', 'LST_Night_1km') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select('NDVI') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var lulc006 = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1') // Lulc mode and boolean reclassification var lulcMask = lulc006.mode().remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],lulcOptions006) .eq(1).rename('remapped').clip(region) // Merge NDVI and LST image collections var combined = ndvi.combine(lst, true) var combinedList = combined.toList(10000) // Boolean reclassifications (suitable/... Visit https://dataone.org/datasets/sha256%3A3695619598269618f05611b802adc5f0e04bc7317cfecc7fcd6bc2536f881776 for complete metadata about this dataset.

The MOD13Q1 V6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. Documentation: User's Guide Algorithm Theoretical Basis Document (ATBD) General Documentation

Blue economies are measured by mining national statistics or economic modeling, requiring substantial capability and quality data, both of which are not universally available. The lack of harmonized methods hampers international comparisons and results are usually only attributable at the national scale. An alternative method is described here that leverages an open computing environment and data to quantify blue economies using marine night light producing measurements that are intercomparable and scalable from national to regional to global.

Find alternative fueling stations near an address or ZIP code or along a route in the United States. Enter a state to see a station count. ## Data Collection Methods ## The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities coordinators, an Add a Station form on the Alternative Fuels Data Center (AFDC) website, and through collaborating with infrastructure equipment and fuel providers. NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies and inclusion criteria may result in slight differences between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to maintain the data. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, and propane industry groups to establish best practices for identifying new stations in the most-timely manner possible and to develop a more rigorous network for the future. ## Station Update Schedule ## Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and dispensing the fuel specified. Based on an established data collection schedule, the database is updated once a month with the exception of electric vehicle supply equipment (EVSE) data, which are updated twice a month. Stations that are no longer operational or no longer provide alternative fuel are removed from the database on a monthly basis or as they are identified. ## Mapping and Counting Methods ## Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or charging outlets at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool like Google Earth, Google Maps, or Google StreetView. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application. ## Notes about Specific Station Types ## ### Private Stations ### Stations with an Access of "Private - Fleet customers only" may allow other entities to fuel through a business-to-business arrangement. For more information, fleet customers should refer to the information listed in the details section for that station to contact the station directly. ### Biodiesel Stations ### The Alternative Fueling Station Locator only includes stations offering biodiesel blends of 20% (B20) and above. ### Electric Vehicle Supply Equipment (EVSE) ### An electric charging station, or EVSE, appears as one point on the map, regardless of the number of charging outlets at that location. The number and type of charging outlets available are displayed as additional details when the station location is selected. Each point on the map is counted as one station in the station count. To see a total count of EVSE for all outlets available, go to the Alternative Fueling Station Counts by State table. Residential EVSE locations are not included in the Alternative Fueling Station Locator. ## Liquefied Petroleum Gas (Propane) Stations ### Because many propane stations serve customers other than drivers and fleets, NREL collaborated with the industry to effectively represent the differences. Each propane station is designated as a 'primary' or 'secondary' service type. Both types are able to fuel vehicles. However, locations with a 'primary' designation offer vehicle services and fuel priced specifically for use in vehicles. The details page for each station lists its service designation.

MSZSI: Multi-Scale Zonal Statistics [AgriClimate] Inventory -------------------------------------------------------------------------------------- MSZSI is a data extraction tool for Google Earth Engine that aggregates time-series remote sensing information to multiple administrative levels using the FAO GAUL data layers. The code at the bottom of this page (metadata) can be pasted into the Google Earth Engine JavaScript code editor and ran at https://code.earthengine.google.com/. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 Input options: [1] Country of interest [2] Start and end year [3] Start and end month [4] Option to mask data to a specific land-use/land-cover type [5] Land-use/land-cover type code from CGLS LULC [6] Image collection for data aggregation [7] Desired band from the image collection [8] Statistics type for the zonal aggregations [9] Statistic to use for annual aggregation [10] Scaling options [11] Export folder and label suffix Output: Two CSVs containing zonal statistics for each of the FAO GAUL administrative level boundaries Output fields: system:index, 0-ADM0_CODE, 0-ADM0_NAME, 0-ADM1_CODE, 0-ADM1_NAME, 0-ADMN_CODE, 0-ADMN_NAME, 1-AREA_PERCENT_LULC, 1-AREA_SQM_LULC, 1-AREA_SQM_ZONE, 2-X_2001, 2-X_2002, 2-X_2003, ..., 2-X_2020, .geo PREPROCESSED DATA DOWNLOAD The datasets available for download contain zonal statistics at 2 administrative levels (FAO GAUL levels 1 and 2). Select countries from Southeast Asia and Sub-Saharan Africa (Cambodia, Indonesia, Lao PDR, Myanmar, Philippines, Thailand, Vietnam, Burundi, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia, Zimbabwe) are included in the current version, with plans to extend the dataset to contain global metrics. Each zip file is described below and two example NDVI tables are available for preview. Key: [source, data, units, temporal range, aggregation, masking, zonal statistic, notes] Currently available: MSZSI-V2_V-NDVI-MEAN.tar: [NASA-MODIS, NDVI, index, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_T-LST-DAY-MEAN.tar: [NASA-MODIS, LST Day, °C, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_T-LST-NIGHT-MEAN.tar: [NASA-MODIS, LST Night, °C, 2001–2020, annual mean, agriculture, mean, n/a] MSZSI-V2_R-PRECIP-SUM.tar: [UCSB-CHG-CHIRPS, Precipitation, mm, 2001–2020, annual sum, agriculture, mean, n/a] MSZSI-V2_S-BDENS-MEAN.tar: [OpenLandMap, Bulk density, g/cm3, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-ORGC-MEAN.tar: [OpenLandMap, Organic carbon, g/kg, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-PH-MEAN.tar: [OpenLandMap, pH in H2O, pH, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-WATER-MEAN.tar: [OpenLandMap, Soil water, % at 33kPa, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-SAND-MEAN.tar: [OpenLandMap, Sand, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-SILT-MEAN.tar: [OpenLandMap, Silt, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_S-CLAY-MEAN.tar: [OpenLandMap, Clay, %, static, n/a, agriculture, mean, at depths 0-10-30-60-100-200] MSZSI-V2_E-ELEV-MEAN.tar: [MERIT, [elevation, slope, flowacc, HAND], [m, degrees, km2, m], static, n/a, agriculture, mean, n/a] Coming soon MSZSI-V2_C-STAX-MEAN.tar: [OpenLandMap, Soil taxonomy, category, static, n/a, agriculture, area sum, n/a] MSZSI-V2_C-LULC-MEAN.tar: [CGLS-LC100-V3, LULC, category, 2015–2019, mode, none, area sum, n/a] Data sources: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1 https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_BULKDENS-FINEEARTH_USDA-4A1H_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_ORGANIC-CARBON_USDA-6A1C_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_PH-H2O_USDA-4C1A2A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_WATERCONTENT-33KPA_USDA-4B1C_M_v01 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_CLAY-WFRACTION_USDA-3A1A1A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_SAND-WFRACTION_USDA-3A1A1A_M_v02 https://developers.google.com/earth-engine/datasets/catalog/OpenLandMap_SOL_SOL_GRTGROUP_USDA-SOILTAX_C_v01...

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1lM1sGcDnaH3Yl8kQOsozotvhtKMwVW4VHTjrWL1ysTs/edit?gid=889227542gid=889227542

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for corporate intelligence, market research, and lead generation.

Canaria’s Google Maps Company Profile Data offers highly customizable, on-demand enrichment of company locations worldwide. This versatile dataset can be tailored for existing companies in our database of over 30 million global locations or created from scratch using client-provided company names and locations.

Ideal for businesses needing verified data across multiple branches, our service retrieves enriched details such as precise addresses, geographic coordinates, business categories, contact numbers, operating hours, and ratings. This data layer supports strategic planning, market analysis, and lead generation by enhancing the accuracy of location-based insights.

Key Data Attributes: - Company Name and Address: Standardized and verified addresses, with additional details like floor or building information where available. - Geographic Coordinates: Latitude and longitude data, along with Plus Codes for precise geolocation, suitable for mapping and geospatial analysis. - Business Category: Classification by business type, as returned by Google Maps, providing context on industry and business function. - Contact Information: Includes verified phone numbers, with sources noted for validation, to support sales and customer service outreach. - Operating Hours: Up-to-date hours of operation for each branch, helping businesses tailor strategies based on accessibility and timing. - Ratings and Reviews: Average rating scores and review counts, offering insights into a company’s public perception at each location.

Core Industry Applications - Location-Based Market Research: Gain insights into market density and competitor distribution with detailed branch-level data. - Lead Generation and Sales Insights: Identify high-potential leads by geographic area and business category, supporting targeted outreach. - Geospatial Analysis: Use precise location coordinates and Plus Codes for mapping and territory planning, essential for logistics and expansion strategies. - Account-Based Marketing (ABM): Tailor campaigns by region or business type, enhancing engagement through location-specific insights. - Corporate Development: Support site selection, expansion, and strategic growth by analyzing branch distribution, ratings, and category data.

With flexible delivery options and on-demand updates, Canaria’s Google Maps Company Profile Data provides a dynamic resource for companies needing real-time, location-based enrichment for strategic and operational planning. Let us know your specific needs, and we’ll configure the ideal solution to keep your data accurate and actionable.

GEODATA TOPO 250K Series 3 is a vector representation of the major features appearing on 1:250,000 scale NATMAP topographic maps and is supplied in various formats over a defined area. It is primarily designed to provide high quality data for mapping and GIS professionals. Data includes powerlines and pipelines and is supplied for commercial GIS and public use in the formats described below. GEODATA TOPO 250K Series 3 is available as a packaged product in Personal Geodatabase, ArcView Shapefile or MapInfo TAB file formats. Each package includes data arranged in ten main themes - cartography, elevation, framework, habitation, hydrography, infrastructure, terrain, transport, utility and vegetation. Data is also available as GEODATA TOPO 250K Series 3 for Google Earth in .kml format for use on Google Earth TM Mapping Service. All data is based on GDA94 coordinate system, however .kml format data has been converted to WGS84. Use of GEODATA TOPO 250K Series 3 is subject to a licence, the full terms of which are contained within the package. Interactive Maps, Geoscience Australia`s on-line map download system, delivering free download of seamless data. MapConnect will allow you to select a specific area (subject to parameters) and themes or select individual tiles for download. Data will be available in GML and Shape file formats. Customised 250K GEODATA is available where requirements are not met by the packaged or Interactive Maps options. The price will be determined after assessing your needs - contact the Geoscience Australia Sales Centre. Alternately, we may refer you to a third party supplier. Packaged product formats - Personal geodatabase (Geocat # 63999), Shapefiles (Geocat # 64058), TAB files (Geocat # 64059), KML files for use with Google Earth (Geocat # 65137). Product Specifications Themes: Cartography, Elevation, Framework, Habitation, Hydrography, Infrastructure, Terrain, Transport, Utility and Vegetation Coverage: National (Powerlines not available in South Australia) Currency: Data has a currency of less than five years for any location Coordinates: Geographical Datum: Geocentric Datum of Australia (GDA94) Formats: Personal Geodatabase, ArcView Shapefile and MapInfo TAB Medium: Packaged DVD ROM ($99 per package) or online via Interactive Maps Previous Version: Replaces GEODATA TOPO 250K Series 2

📊 LinkedIn Company Data for Company Analysis, Valuation & Portfolio Strategy LinkedIn company data is one of the most powerful forms of alternative data for understanding company behavior, firmographics, business dynamics, and real-time hiring signals. Canaria’s enriched LinkedIn company data provides detailed company profiles, including hiring activity, job postings, employee trends, headquarters and branch locations, and verified metadata from Google Maps. This LinkedIn corporate data is updated weekly and optimized for use in company analysis, startup scouting, private company valuation, and investment monitoring. It supports BI dashboards, risk models, CRM enrichment, and portfolio strategy.

🧠 Use Cases: What Problems This LinkedIn Data Solves Our LinkedIn company insights transform opaque business landscapes into structured, analyzable data. Whether you’re conducting M&A due diligence, tracking high-growth companies, or benchmarking performance, this dataset empowers fast, confident decisions.

🔍 Company Analysis • Identify a company’s size, industry classification, and headcount signals using LinkedIn firmographic data • Analyze social presence through LinkedIn follower metrics and employee engagement • Understand geographic expansion through branch locations and hiring distribution • Benchmark companies using LinkedIn profile activity and job posting history • Monitor business changes with real-time LinkedIn updates

📈 Company Valuation & Financial Benchmarking • Feed LinkedIn-based firmographics into comps and financial models • Use hiring velocity from LinkedIn job data as a proxy for business growth • Strengthen private market intelligence with verified non-financial signals • Validate scale, structure, and presence via LinkedIn and Google Maps footprint

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📌 Summary Canaria’s LinkedIn company data delivers high-frequency, high-quality insights into U.S. companies, combining job posting trends, location data, and firmographic intelligence. With real-time updates and structured delivery formats, this alternative dataset enables powerful workflows across company analysis, financial modeling, investment research, market segmentation, and business strategy.

🏢 About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, and Glassdoor salary analytics. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our AI-powered pipeline is developed by a seasoned team of machine learning experts from Google, Meta, and Amazon, and by alumni of S...

Abstract: We present a detailed geomorphological map (1:5000-scale) of a middle mountainous area in Jena, Germany. To overcome limitations associated with traditional field-based approaches and to extend the possibility of manually digital mapping in a structural way, we propose an approach using geographic information systems (GIS) and high-resolution digital data. The geomorphological map features were extracted by manually interpreting and analyzing the combination of different data sources using light detection and ranging (LiDAR) data. A combination of topographic and geological maps, digital orthophotos (DOPs), Google Earth images, field investigations, and derivatives from digital terrain models (DTMs) revealed that it is possible to generate and present the geomorphologic features involved in classical mapping approaches. We found that LiDAR-DTM and land surface parameters (LSPs) can provide better results when incorporating the visual interpretation of multidirectional hillshade and LSP composite maps. The genesis of landforms can be readily identified, and findings enabled us to systematically delineate landforms and geomorphological process domains. Although our approach provides a cost effective, objective, and reproducible alternative for the classical approach, we suggest that further use of digital data should be undertaken to support analysis and applications.

Auburn Maine's parcel Inquiry map with optional zoning and high-resolution aerial photography. Optional zoning layers. Map provides detailed assessing data for each parcel as well as links to WebPro assessing records and Google Street View. Users can search for parcels using parcel ID, location, or owner name. Advanced search options provide ability to select and buffer parcels with an optional export to csv file.

The RADARSAT-2 ESA archive collection consists of RADARSAT-2 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Following Beam modes are available: Standard, Wide Swath, Fine Resolution, Extended Low Incidence, Extended High Incidence, ScanSAR Narrow and ScanSAR Wide. Standard Beam Mode allows imaging over a wide range of incidence angles with a set of image quality characteristics which provides a balance between fine resolution and wide coverage, and between spatial and radiometric resolutions. Standard Beam Mode operates with any one of eight beams, referred to as S1 to S8, in single and dual polarisation . The nominal incidence angle range covered by the full set of beams is 20 degrees (at the inner edge of S1) to 52 degrees (at the outer edge of S8). Each individual beam covers a nominal ground swath of 100 km within the total standard beam accessibility swath of more than 500 km. Beam Mode Product Nominal Resolution (metres) Nominal Pixel Spacing Range x Azimuth (metres) Resolution Range x Azimuth (metres) Nominal Scene Size Range x Azimuth (kilometres) Range of Angle of Incidence (degrees) Number of Looks Range x Azimuth Polarisations Options Standard SLC 25 8.0 or 11.8 x 5.1 9.0 or 13.5 x 7.7 100 x 100 20 - 52 1 x 1 Single Pol HH or VV or HV or VH - or - Dual HH + HV or VV + VH SGX 8.0 x 8.0 26.8 - 17.3 x 24.7 1 x 4 SGF 12.5 x 12.5 SSG, SPG Wide Swath Beam Mode allows imaging of wider swaths than Standard Beam Mode, but at the expense of slightly coarser spatial resolution. The three Wide Swath beams, W1, W2 and W3, provide coverage of swaths of approximately 170 km, 150 km and 130 km in width respectively, and collectively span a total incidence angle range from 20 degrees to 45 degrees. Polarisation can be single and dual. Beam Mode Product Nominal Resolution (metres) Nominal Pixel Spacing Range x Azimuth (metres) Resolution Range x Azimuth (metres) Nominal Scene Size Range x Azimuth (kilometres) Range of Angle of Incidence (degrees) Number of Looks Range x Azimuth Polarisations Options Wide SLC 30 11.8 x 5.1 13.5 x 7.7 150 x 150 20 - 45 1 x 1 Single: Pol HH or VV or HV or VH - or - Dual: HH + HV or VV + VH SGX 10 x 10 40.0 - 19.2 x 24.7 1 x 4 SGF 12.5 x 12.5 SSG, SPG Fine Resolution Beam Mode is intended for applications which require finer spatial resolution. Products from this beam mode have a nominal ground swath of 50 km. Nine Fine Resolution physical beams, F23 to F21, and F1 to F6 are available to cover the incidence angle range from 30 to 50 degrees. For each of these beams, the swath can optionally be centred with respect to the physical beam or it can be shifted slightly to the near or far range side. Thanks to these additional swath positioning choices, overlaps of more than 50% are provided between adjacent swaths. RADARSAT-2 can operate in single and dual polarisation for this beam mode. Beam Mode Product Nominal resolution (metres) Nominal Pixel Spacing Range x Azimuth (metres) Resolution Range x Azimuth (metres) Nominal Scene Size Range x Azimuth (kilometres) Range of Angle of Incidence (degrees) Number of Looks Range x Azimuth Polarisations Options Fine SLC 8 4.7 x 5.1 5.2 x 7.7 50 x 50 30 - 50 1 x 1 Single: Pol HH or VV or HV or VH - or - Dual: HH + HV or VV + VH SGX 3.13 x 3.13 10.4 - 6.8 x 7.7 1 x 1 SGF 6.25 x 6.25 SSG, SPG In the Extended Low Incidence Beam Mode, a single Extended Low Incidence Beam, EL1, is provided for imaging in the incidence angle range from 10 to 23 degrees with a nominal ground swath coverage of 170 km. Some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum scan angle range. Only single polarisation is available. Beam Mode Product Nominal resolution (metres) Nominal Pixel Spacing Range x Azimuth (metres) Resolution Range x Azimuth (metres) Nominal Scene Size Range x Azimuth (kilometres) Range of Angle of Incidence (degrees) Number of Looks Range x Azimuth Polarisations Options Extended Low SLC 25 8.0 x 5.1 9.0 x 7.7 170 x 170 10 - 23 1 x 1 Single: HH SGX 10.0 x 10.0 52.7 - 23.3 x 24.7 1 x 4 SGF 12.5 x 12.5 SSG, SPG In the Extended High Incidence Beam Mode, six Extended High Incidence Beams, EH1 to EH6, are available for imaging in the 49 to 60 degree incidence angle range. Since these beams operate outside the optimum scan angle range of the SAR antenna, some degradation of image quality, becoming progressively more severe with increasing incidence angle, can be expected when compared with the Standard Beams. Swath widths are restricted to a nominal 80 km for the inner three beams, and 70 km for the outer beams. Only single polarisation available. Beam Mode Product Nominal resolution (metres) Nominal Pixel Spacing Range x Azimuth (metres) Resolution Range x Azimuth (metres) Nominal Scene Size Range x Azimuth (kilometres) Range of Angle of Incidence (degrees) Num...

GEODATA TOPO 250K Series 3 is a vector representation of the major topographic features appearing on the 1:250,000 scale NATMAPs. The data is supplied in MapInfo Tables format and is designed for use in a range of commercial GIS software. Data is arranged within specific themes. All data is based on the GDA94 coordinate system. GEODATA TOPO 250K Series 3 is also available as a packaged product in Personal Geodatabase, ArcView Shapefile formats. Each package includes data arranged in ten main themes - cartography, elevation, framework, habitation, hydrography, infrastructure, terrain, transport, utility and vegetation. Data is also available as GEODATA TOPO 250K Series 3 for Google Earth in .kml format for use on Google Earth TM Mapping Service. Customised 250K GEODATA is available where requirements are not met by the packaged or MapConnect options. The price will be determined after assessing your needs. Alternately, we may refer you to a third party supplier.

http://www.openstreetmap.org/images/osm_logo.png" alt=""/> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...