NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, and Oceania are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management.

The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule.

Known Issues * Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. * High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs. * The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

World Continents represents the boundaries for the continents of the world.This layer is best viewed out beyond a maximum scale (zoomed in) of 1:3,000,000. The sources of this dataset are Esri, Global Mapping International (GMI), U.S. Central Intelligence Agency (The World Factbook), and Garmin. It is updated as country boundaries coincident to continental boundaries change. To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to World Continents.

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

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

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

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

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

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

References:

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

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

OverviewThis data set delineates peatlands and other organic soils globally using five layers. Miettinen et al. 2016 was used for Indonesia and Malaysia, Hastie et al. 2022 was used in lowland Peru, Crezee et al. 2022 was used in the Congo basin, and Gumbricht et al. 2017 was used for all land between 40 degrees north and 60 degrees south (including areas covered by the aforementioned data sets). Xu et al. 2018 was used for all land above 40 degrees north. Miettinen et al. 2016, Xu et al. 2018 were rasterized to ~30x30 m resolution while Gumbricht et al. 2017, Crezee et al. 2022, and Hastie et al. 2022 were resampled from their native resolutions to ~30x30 m resolution in order to align with the Global Forest Change maps from Hansen et al. 2013. All layers were combined, i.e. Gumbricht et al. 2017 was also used in Indonesia/Malaysia, the Peruvian Amazon, and the Congo basin. All data sources have different methods for peatland delineation, which are described in their original publications. Crezee, B. et al. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. Nature Geoscience 15: 639-644 (2022). https://www.nature.com/articles/s41561-022-00966-7. Data downloaded from https://congopeat.net/maps/, using classes 4 and 5 only (peat classes). Gumbricht, T. et al. An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Global Change Biology 23, 3581–3599 (2017). https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.13689 Hastie, A. et al. Risks to carbon storage from land-use change revealed by peat thickness maps of Peru. Nature Geoscience 15: 369-374 (2022). https://www.nature.com/articles/s41561-022-00923-4 Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Global Ecological Conservation. 6, 67– 78 (2016). https://www.sciencedirect.com/science/article/pii/S2351989415300470 Xu et al. PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. CATENA 160: 134-140 (2018). https://www.sciencedirect.com/science/article/pii/S0341816217303004 Resolution: 30 x 30mGeographic Coverage: GlobalFrequency of Updates: SporadicDate of Content: Range of yearsSources (by region):Crezee et al. 2022 (Congo basin) Gumbricht et al. 2017 (between 40 deg N and rest of southern hemisphere) Hastie et al. 2022 (Amazonian lowland Peru) Miettinen et al. 2016 (Indonesia and Malaysia) Xu et al. 2018 (temperate/boreal, north of 40 deg N)CautionsThis is a composite layer comprised of five data sets, each with their own methods and strengths and weaknesses. Refer to the original publications for each data set to learn more about specific cautions for each. All input layers have been converted from vector data or resampled from coarser raster dataLicenseCC-by-4.0

Timely and accurate monitoring of impervious surface areas (ISA) is crucial for effective urban planning and sustainable development. Recent advances in remote sensing technologies have enabled global ISA mapping at fine spatial resolution (<30 m) over long time spans (>30 years), offering the opportunity to track global ISA dynamics. However, existing 30 m global long-term ISA datasets suffer from omission and commission issues, affecting their accuracy in practical applications. To address these challenges, we proposed a novel global longterm ISA mapping method and generated a new 30 m global ISA dataset from 1985 to 2021, namely GISA-new. Specifically, to reduce ISA omissions, a multi-temporal Continuous Change Detection and Classification (CCDC) algorithm that accounts for newly added ISA regions (NA-CCDC) was proposed to enhance the diversity and representativeness of the training samples. Meanwhile, a multi-scale iterative (MIA) method was proposed to automatically remove global commissions of various sizes and types. Finally, we collected two independent test datasets with over 100,000 test samples globally for accuracy assessment. Results showed that GISA-new out performed other existing global ISA datasets, such as GISA, WSF-evo, GAIA, and GAUD, achieving the highest overall accuracy (93.12 %), the lowest omission errors (10.50 %), and the lowest commission errors (3.52 %). Furthermore, the spatial distribution of global ISA omissions and commissions was analyzed, revealing more mapping uncertainties in the Northern Hemisphere. In general, the proposed method in this study effectively addressed global ISA omissions and removed commissions at different scales. The generated high-quality GISAnew can serve as a fundamental parameter for a more comprehensive understanding of global urbanization.

The first 30 m resolution global land cover data set with 10 classes and for the year 2000 and 2010.

Global Land Cover (GLC) information is fundamental for environmental change studies, land resource management, sustainable development, and many other societal benefits. Although GLC data exists at spatial resolutions of 300 m and 1000 m, a 30 m resolution mapping approach is now a feasible option for the next generation of GLC products. Since most significant human impacts on the land system can be captured at this scale, a number of researchers are focusing on such products. This paper reports the operational approach used in such a project, which aims to deliver reliable data products.

Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed.

Data citation: CHEN Jun et al.: 2015.Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing Volume 103, May 2015, Pages 7–27 http://dx.doi.org/10.1016/j.isprsjprs.2014.09.002

Available at: http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=163

Project Page

https://open-earth-map.org/

Paper

https://arxiv.org/abs/2210.10732

Overview

OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications.

Reference

@inproceedings{xia_2023_openearthmap, title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping}, author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6254-6264} }

License

Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data here. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Note for xBD data

The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from https://xview2.org/dataset and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.

Code

Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available here.

Leaderboard

Performance on the test set can be evaluated on the Codalab webpage.

This dataset was generated by the Remote Sensing Group of the TU Wien Department of Geodesy and Geoinformation, within Framework Contract (No. 939866-IPR-2020) as part of the provision of an automated, global, satellite-based flood monitoring product for the Copernicus Emergency Management Service (CEMS) managed by the European Commission. The Global Flood Monitoring (GFM) product is integrated within the user interface of the Global Flood Awareness System (GloFAS) of the CEMS. Open use of the dataset is granted under the CC BY 4.0 license.

The Copernicus Sentinel-1 constellation is a highly-capable monitoring mission and provides one of the most comprehensive global archives on satellite imagery. The satellite sensors acquire Synthetic Aperture Radar (SAR) images, and as such, they observe regardless of weather conditions and daylight. The regular and systematic observations generate rich information on the global land surface and its dynamics, which is used for---but not limited to---terrestrial applications like e.g. land cover mapping, flood detection, or drought monitoring.

The complete Sentinel-1 time-series dataset is challenging to analyze, primarily due to its sheer data volume of at the (global) scale of Petabytes. As a user-friendly alternative, this dataset provides a Harmonic (Fourier) series model that reduces the SAR backscatter seasonality to a relative small number of GeoTIFF files holding the harmonic coefficient values.

This dataset publication provides a temporal Sentinel-1 model for most of the world's land masses. Seven coefficients computed using (harmonic) least squares regression, along with the standard deviation of residuals and number of observations, comprise the harmonic parameter set. The parameters are being operationally used to determine the expected SAR backscatter signal for any day of the year as part of the TU Wien's method contributing to GFM's ensemble flood monitoring effort (Bauer-Marschallinger et. al, 2022). The Global Harmonic Parameters (HPARs) were derived from the whole Sentinel-1 VV temporal stack for the period 2019-2020 by least squares regression with a harmonic model formulation, running three sinusoidal iterations (k=3).

The model describes the typical seasonal Sentinel-1 backscatter variation on a 20 m pixel level. It was designed as a smoothed time-series approximation, removing short-term perturbations, such as speckle and transient events (like floods for instance). Hence, the model is suited to discern the seasonal changes brought about by varying water content, e.g., inundation or soil moisture, and progression of vegetation structure.

We encourage developers from the broader user community to exploit this extensive and functional data resource. In particular, we promote the use of these Sentinel-1 HPARs in models for various applications dealing with land cover, seasonal water mapping, or vegetation phenology.

For the datasets' theoretical formulation and primary use case as a non-flooded backscatter reference model, please refer to our peer-reviewed article. Additionally, the software used, computation process, and outlook are discussed in this conference paper.

Data record

The parameter sets are provided per Sentinel-1's relative orbit to account for geometric effects. The parameter files are sampled at 20 m pixel spacing, georeferenced to the Equi7Grid, and divided into six continental zones (Africa, Asia, Europe, North America, Oceania, and South America. For portability and easier downloads, further sub-divisions into continental parts are done, resulting in 12 compressed bundles (please refer to coverage map).

The parameter sets are provided per Sentinel-1's relative to account for geometric effects. The parameter files are sampled at 20 m pixel spacing, georeferenced to the Equi7Grid, and divided into six continental zones (Africa, Asia, Europe, North America, Oceania, and South America);. For portability and easier downloads, further sub-divisions into continental parts are done, resulting in 12 compressed bundles (please refer to coverage map).

The data itself is organised as square tiles of 300 km extent ("T3"-tiles). Note that the parameters are generated for each orbit, resulting in several orbit-sets per tile. Given this structure, a total of 98910 files for the 10990 tiled orbit-sets, comprising overall a compressed disk size of 3.7 TB.

The datasets follow the Yeoda filenaming convention (documentation here) where the core meta information is embedded. Notably, the file name is prefaced by the product name 'SIG0-HPAR-' and the particular parameter codes:

• M0 - effective mean of the time series stacks, also called the harmonic residual in other literature.
• Cn - cosine component coefficients, where n = 1, 2, or 3.
• Sn - sine component coefficients, where n = 1, 2, or 3.
• STD - standard deviation of residuals, a proxy for model goodness of fit.
• NOBS - number of observations used for the least squares regression, also
  an indicator of solution quality.

Orbit sets are distinguishable by orbit direction, i.e. (A - ascending and D - descending) and relative orbit number, for example: 'A175', 'D080'.

File naming scheme is as follows:

SIG0-HPAR-NNN_YYYYMMDD1_YYYYMMDD2_VV_OOOO_TTTTTTTTTT_GGGG_V02R01_S1IWGRDH.tif

*bold faced items are fixed for this product version.

• NNN - product name which indicates parameter code.
• YYYYMMDD1 - start date of time series processed.
• YYYYMMDD2 - end date of time series processed.
• VV - polarization of product.
• OOOO - relative orbit.
• TTTTTTTTTT - Equi7grid tile name.
• GGGG - Subgrid. Contains continent code and sampling size.
• V02R01 - Product version.
• S1IWGRDH - Sensor type.

For example:
'SIG0-HPAR-STD_20190101_20210101_VV_D111_E102N066T3_SA020M_V02R01_S1IWGRDH.tif'

The parameters' file format is an LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems such as QGIS or ArcGIS, and geodata libraries as GDAL is given.

This repository provides all parameter sets per orbit for each tile and is organized in a folder structure per (sub-)continent. With this, twelve zipped dataset collections per (sub-)continent are available for download.

Code Availability

We suggest users to use the open-source Python package yeoda, a datacube storage access layer that offers functions to read, write, search, filter, split and load data from this repository as an HPAR datacube. The yeoda package is openly accessible on GitHub at https://github.com/TUW-GEO/yeoda.

Furthermore, for the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in this publication .

A day-of-year estimate reader tool based on the packages above is likewise available on GitHub at https://github.com/TUW-GEO/hpar-reader.

Acknowledgements

The authors would like to thank our colleagues: Thomas Melzer of TU Wien for his invaluable insights on the parameter formulation, and Senmao Cao of Earth Observation Data Centre GmbH (EODC) for his contributions to the code base used to process dataset.

This work was partly funded by TU Wien, with co-funding from the project "Provision of an Automated, Global, Satellite-based Flood Monitoring Product for the Copernicus Emergency Management Service" (GFM), Contract No. 939866-IPR-2020 for the European Commission's Joint Research Centre (EC-JRC), and the project "Flood Event Monitoring and Documentation enabled by the Austrian Sentinel Data Cube" (ACube4Floods), Contract No. 878946 for the Austrian Research Promotion Agency (FFG, ASAP16).

The computational results presented have been achieved using the Vienna Scientific Cluster (VSC). We further would like to thank our colleagues at TU Wien and EODC for supporting us on technical tasks to cope with such a large and complex dataset.

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.

Culminating more than four years of processing data, NASA and the National Geospatial-Intelligence Agency (NGA) have completed Earth's most extensive global topographic map. The mission is a collaboration among NASA, NGA, and the German and Italian space agencies. For 11 days in February 2000, the space shuttle Endeavour conducted the Shuttle Radar Topography Mission (SRTM) using C-Band and X-Band interferometric synthetic aperture radars to acquire topographic data over 80% of the Earth's land mass, creating the first-ever near-global data set of land elevations. This data was used to produce topographic maps (digital elevation maps) 30 times as precise as the best global maps used today. The SRTM system gathered data at the rate of 40,000 per minute over land. They reveal for the first time large, detailed swaths of Earth's topography previously obscured by persistent cloudiness. The data will benefit scientists, engineers, government agencies and the public with an ever-growing array of uses. The SRTM radar system mapped Earth from 56 degrees south to 60 degrees north of the equator. The resolution of the publicly available data is three arc-seconds (1/1,200th of a degree of latitude and longitude, about 295 feet, at Earth's equator). The final data release covers Australia and New Zealand in unprecedented uniform detail. It also covers more than 1,000 islands comprising much of Polynesia and Melanesia in the South Pacific, as well as islands in the South Indian and Atlantic oceans. SRTM data are being used for applications ranging from land use planning to "virtual" Earth exploration. Currently, the mission's homepage "http://www.jpl.nasa.gov/srtm" provides direct access to recently obtained earth images. The Shuttle Radar Topography Mission C-band data for North America and South America are available to the public. A list of complete public data set is available at "http://www2.jpl.nasa.gov/srtm/dataprod.htm" The data specifications are within the following parameters: 30-meter X 30-meter spatial sampling with 16 meter absolute vertical height accuracy, 10-meter relative vertical height accuracy, and 20-meter absolute horizontal circular accuracy. From the JPL Mission Products Summary, "http://www.jpl.nasa.gov/srtm/dataprelimdescriptions.html". The primary products of the SRTM mission are the digital elevation maps of most of the Earth's surface. Visualized images of these maps are available for viewing online. Below you will find descriptions of the types of images that are being generated:

• Radar Image
• Radar Image with Color as Height
• Radar Image with Color Wrapped Fringes
  -Shaded Relief
• Perspective View with B/W Radar Image Overlaid
• Perspective View with Radar Image Overlaid, Color as Height
• Perspective View of Shaded Relief
• Perspective View with Landsat or other Image Overlaid
• Contour Map - B/W with Contour Lines
• Stereo Pair
• Anaglypgh

The SRTM radar contained two types of antenna panels, C-band and X-band. The near-global topographic maps of Earth called Digital Elevation Models (DEMs) are made from the C-band radar data. These data were processed at the Jet Propulsion Laboratory and are being distributed through the United States Geological Survey's EROS Data Center. Data from the X-band radar are used to create slightly higher resolution DEMs but without the global coverage of the C-band radar. The SRTM X-band radar data are being processed and distributed by the German Aerospace Center, DLR.

Raster files and contours of apparent resistivity collected at nominal electrode array spacings of 500 and 1000 m.

DC resistivity mapping data collected by GNS Science and its predecessor organisations from the mid-1960s until 1997, primarily for geothermal exploration. Apparent resistivity varies by several orders of magnitude in the Taupō Volcanic Zone, where there is a significant resistivity contrast between rocks saturated with hot geothermal fluids and cold, siliceous volcanic rocks. Over 32,000 DC resistivity mapping observations have been made in New Zealand. The greatest number of measurements are in the Taupō Volcanic Zone, with some smaller surveys in the Waihi area and Ngawha in Northland.

All data collected since 1970 have been with the Schlumberger electrode configurations with nominal electrode spacings of AB/2 = 500m and AB/2 = 1000m. Prior to 1970, DC resistivity data were mostly collected using a Wenner array with electrode spacing 1800 ft (549 m) and 3600 ft (1097 m). Data collected in Lake Taupō used two 150 m long dipoles towed behind small boats, each boat separated by 500 m forming an equatorial dipole-dipole array. For more information on the data collection see download resource (below), Bibby 1988, Bennie et al. 1995, Caldwell and Bibby 1992.

Rasters and contours can be accessed via the E Tūhura - Explore Zealandia (TEZ) portal (https://data.gns.cri.nz/tez/).

DOI for this data set is https://doi.org/10.21420/6GE4-A546

Bennie, S. L., Stagpoole, V. M. and Bibby, H. M., 1985. Waterborne resistivity measurements in the Rotorua Lakes area of New Zealand. Geophysics Division Report 206, DSIR, Wellington.

Bibby HM, 1988. Electrical resistivity mapping in the Central Volcanic Region of New Zealand. New Zealand Journal of Geology and Geophysics, Vol. 31: 259-274. https://doi.org/10.1080/00288306.1988.10417776

Caldwell, T. G. and Bibby, H. M. 1992. Geothermal implications of resistivity mapping on Lake Taupō. Proc. 14th N. Z. Geothermal Workshop 1992, pp. 207-212, University of Auckland.

The dataset presents 687 rivers associated to 405 Major River Basins.Data was collected within the framework of the BGR-UNESCO "World-wide Hydrogeological Mapping and Assessment Programme" (WHYMAP): www.whymap.org

ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.htmlUpdate: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

ESA WorldCereal 2021 products v100

The European Space Agency (ESA) WorldCereal 10m 2021 product suite consist of global-scale annual and seasonal crop maps and (where applicable) their related confidence. Every file in this repository contains up to 106 agro-ecological zone (AEZ) products which were all processed with respect to their own regional seasonality and should be considered as independent products.

Naming convention of the ZIP files is as follows:

WorldCereal_{year}_{season}_{product}_{classification|confidence}.zip

The actual AEZ-based GeoTIFF files inside each ZIP are named according to following convention:

{AEZ_id}_{season}_{product}_{startdate}_{enddate}_{classification|confidence}.tif

The seasons are defined in Table 1. Note that cereals as described by WorldCereal include wheat, barley and rye, which belong to the Triticeae tribe. Next to the actual WorldCereal products, this repository contains the files "WorldCereal_AEZ.geojson" that contains the AEZ description and outline, as well as "QGIS_stylefiles.zip" which contains QGIS style files (.qml) for product visualization purposes.

Note: AEZs for which no irrigation product is available were not processed because of the unavailability of thermal Landsat data.

A scientific paper describing the WorldCereal products and the methodology behind them is available through the link below:

Van Tricht, K., Degerickx, J., Gilliams, S., Zanaga, D., Battude, M., Grosu, A., Brombacher, J., Lesiv, M., Bayas, J. C. L., Karanam, S., Fritz, S., Becker-Reshef, I., Franch, B., Mollà-Bononad, B., Boogaard, H., Pratihast, A. K., Koetz, B., and Szantoi, Z.: WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping, Earth Syst. Sci. Data, 15, 5491–5515, https://doi.org/10.5194/essd-15-5491-2023, 2023.

This work was supported by the European Space Agency under contract N°4000130569/20/I-NB.