A dataset of open infrastructure

Summary: This map shows the locations of critical services and lifeline using infrastructure data from OpenStreetMap for Abaco Islands and part of Grand Bahama Island in the Bahamas.Suggested Use:This product can be used to highlight and identify certain critical infrastructure that was exposed to hazard from Hurricane Dorian. This data is best used with additional data that shows where flooding, power outages, or other hazards have occurred. The infrastructure data is derived from pre-event imagery, so may not reflect current situations.Credits: OpenStreetMap Contributors; data available under Open Database Licence (www.openstreetmap.org/copyright)

Agricultural infrastructure maps provide information on water facilities, reservoirs, etc. The OGC (Open Geospatial Consortium) standard API is an international standard developed for sharing and interoperability of spatial data, enabling efficient provision and use of various geographic information such as maps, features, and rasters on the web. The latest OGC API adopts a RESTful structure to enhance development convenience and expandability, and inherits existing standards such as WMS and WFS in a modern way.

You can use this page to explore Green Infrastructure (GI) practices throughout the District of Columbia. Use the filters to search for GI installed through specific DOEE programs or to search for GI of a specific type. You can also download GI data from the District's publicly-available layer of Best Management Practice (BMP) data.

Infrastructure projects are compiled from the capital improvement plans for Transportation, Airport & Ferry, Surface Water Management, and Sewers. Programs (chipseal, paving, guardrail) are not displayed. Project location, scope, and schedule are subject to change. Please read metadata for additional information(https://matterhorn.piercecountywa.gov/GISmetadata/pdbpubw_improvement_project_points.html). Any data download constitutes acceptance of the Terms of Use (https://matterhorn.piercecountywa.gov/Disclaimer/PierceCountyGISDataTermsofUse.pdf).

Accurate land use land cover (LULC) maps that delineate built infrastructure are useful for numerous applications, from urban planning, humanitarian response, disaster management, to informing decision making for reducing human exposure to natural hazards, such as wildfire. Existing products lack sufficient spatial, temporal, and thematic resolution, omitting critical information needed to capture LULC trends accurately over time. Advancements in remote sensing imagery, open-source software and cloud computing offer opportunities to address these challenges. Using Google Earth Engine, we developed a novel built infrastructure detection method in semi-arid systems by applying a random forest classifier to a fusion of Sentinel-1 and Sentinel-2 time series. Our classifier performed well, differentiating three built environment types: residential, infrastructure, and paved, with overall accuracies ranging from 90 to 96%. Producer accuracies were highest for the infrastructure class (98–99%), followed by the residential class (91–96%). Sentinel-1 variables were important for differentiating built classes. We illustrated the utility of our mapped products by generating a time-series of change across southern Idaho spanning 2015 to 2024 and comparing this with publicly available products: National Land Cover Database (NLCD), Microsoft Building Footprints (MBF) and the global Dynamic World (DW). For 2024, our product estimated 5.88% of the study area as built, aligning closely with NLCD (6%) and DW (4.64%). Our mapped built infrastructure products offer enhancements over NLCD spatially and temporally, over DW thematically, and over MBF both temporally and thematically. We demonstrate the potential of fusing data sources to improve LULC mapping and present a case for regionally parameterized models that can more accurately capture built infrastructure change over time. We used open-source approaches for built infrastructure detection, aiming for broader adoption of this workflow across other ecosystems and environments to support decision-making.

This dataset holds the metadata for the organisations uploaded/mapped on http://map.aginfra.eu/

1:1,000,000 raster map showing the Infrastructure of Northern Ireland. A raster map is a static image displayed on screen which is suitable as background mapping. 1:1 000,000 Raster is smallest scale OSNI raster product giving an excellent overview of Northern Ireland. Published here for OpenData. By download or use of this dataset you agree to abide by the Open Government Data Licence.

We provide background maps, image maps, and hybrid maps provided by Open Platform. You can use it by adding the request URL to the user client as a Javascript source with the authenticated key value. Supports OpenLayers 2.7 ~ 2.13. If you want to use the latest version of Openlayers, you can use the WMTS API. For related inquiries, please link to the relevant site and contact the relevant customer center and we will respond.

This submission offers a link to a web mapping application hosted instance of the Global Oil & Gas Features Database (GOGI), via EDX Spatial. This offers users with the ability to visualize, interact, and create maps with data of their choice, as well as download specific attributes or fields of view from the database. This data can also be downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database. Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.

The National Transit Map - Stops dataset was compiled on September 09, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Transit Map (NTM) is a nationwide catalog of fixed-guideway and fixed-route transit service in America. It is compiled using General Transit Feed Specification (GTFS) Schedule data. The NTM Stops dataset shows stops where vehicles pick up or drop off riders. This dataset uses the GTFS stops.txt file. To improve the spatial accuracy of the NTM Stops, the Bureau of Transportation Statistics (BTS) adjusts transit stops using context from the submitted GTFS source data and/or from other publicly available information about the transit service.

Static Map API is an API that enables location display and information sharing using open platform background maps, image maps, and hybrid maps without relying on JavaScript. There is currently no limit to the number of requests, but it may vary depending on API usage and server overload. When called with RestAPI, the result is returned as an image.

Note: This dataset has been updated with transmission lines for the MENA region. This is the most complete and up-to-date open map of Africa's electricity grid network. This dataset serves as an updated and improved replacement for the Africa Infrastructure Country Diagnostic (AICD) data that was published in 2007. Coverage This dataset includes planned and existing grid lines for all continental African countries and Madagascar, as well as the Middle East region. The lines range in voltage from sub-kV to 700 kV EHV lines, though there is a very large variation in the completeness of data by country. An interactive tool has been created for exploring this data, the Africa Electricity Grids Explorer. Sources The primary sources for this dataset are as follows: Africa Infrastructure Country Diagnostic (AICD) OSM © OpenStreetMap contributors For MENA: Arab Union of Electricity and country utilities. For West Africa: West African Power Pool (WAPP) GIS database World Bank projects archive and IBRD maps There were many additional sources for specific countries and areas. This information is contained in the files of this dataset, and can also be found by browsing the individual country datasets, which contain more extensive information. Limitations Some of the data, notably that from the AICD and from World Bank project archives, may be very out of date. Where possible this has been improved with data from other sources, but in many cases this wasn't possible. This varies significantly from country to country, depending on data availability. Thus, many new lines may exist which aren't shown, and planned lines may have completely changed or already been constructed. The data that comes from World Bank project archives has been digitized from PDF maps. This means that these lines should serve as an indication of extent and general location, but shouldn't be used for precisely location grid lines.

We provide legend images for map data provided by the open platform. If you send the request URL to the server along with the authenticated key value, you can receive a legend in PNG / JPG / XML format. The OGC (Open Geospatial Consortium) standard API is an international standard developed for sharing and interoperability of spatial data, enabling efficient provision and use of various geographic information such as maps, features, and rasters on the web. The latest OGC API adopts a RESTful structure to enhance development convenience and expandability, and provides legend information used in WMS and WFS.

This is a MD iMAP hosted service. Find more information on http://imap.maryland.gov. These data map hub - corridor - and gap elements within the green infrastructure. The Green Infrastructure Assessment was developed to provide decision support for Maryland's Department of Natural Resources land conservation programs. Methods used to identify and rank green infrastructure lands are intended solely for this use. Map Service Link: https://mdgeodata.md.gov/imap/rest/services/Biota/MD_GreenInfrastructure/MapServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

coastal and river flooding (Ward et al, 2020)

extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)

tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

population (Schiavina et al, 2023)

built-up area (Pesaresi et al, 2023)

roads (OpenStreetMap, 2023)

railways (OpenStreetMap, 2023)

power plants (Global Energy Observatory et al, 2018)

power transmission lines (Arderne et al, 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

snkit helps clean network data



nismod-snail is designed to help implement infrastructure
exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the
global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo.
DOI: 10.5281/zenodo.3628142



Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical
cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI:
10.4121/12705164.v3



Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.;
et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI:
10.4121/14510817.v3



Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources
Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine;
resourcewatch.org/



Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries
– Final Report. Available online:
https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries



Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme
climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI:
10.1029/2020EF001616



Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online:
www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details



OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived
from OpenStreetMap. [Dataset] Available at
global.infrastructureresilience.org



Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and
Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID:
data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA



Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived
from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI:
10.5281/zenodo.8147088



Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal
(1975-2030). European Commission, Joint Research Centre (JRC) PID:
data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE



Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020)
Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at:
www.wri.org/publication/aqueduct-floods-methodology.

This dataset holds the metadata for the data points uploaded/mapped on http://map.aginfra.eu/

The project “Interconnectors and Power Transmission Lines” was realised within the framework of the SWP Research Paper “Geopolitik des Stroms – Netz, Raum und Macht” (SWP-Studie 2021/S 14, 07.09.2021) and had the objective of identifying and visualising all interconnecting power lines in Europe, Africa and Asia regardless of their primary source of energy that are of relevance on the transmission grid level. As of 2020, no comprehensive data or database on transmission lines and interconnectors were available. Hence, this dataset contributes to filling this gap. It comprises merged and harmonised data from three different sources, namely from the OpenStreetMap (OSM, https://www.openstreetmap.org), the Open Infrastructure Map (OIM, https://openinframap.org), and the World Bank (WB, https://energydata.info), complemented by further research, including the updating and adding of information.

Policy-makers are looking to promote the uptake of bicycling as a healthy mode of travel that reduces the negative effects of traditional motorised transport (physical inactivity, air pollution, traffic congestion) and achieves sustainability goals. As an active form of mobility, bicycling improves physical and mental health and has long-term public health benefits. However, there are a number of barriers that prevent people from riding a bike, including fears about riding alongside motor vehicle traffic and the lack of safe and appropriate bicycling infrastructure. For the strategic installation of safer bicycling infrastructure or the improvement of existing infrastructure, rigorous evidence-informed scientific studies are necessary, which in turn rely on high-quality bicycling data, which is scarce. In this regard, one of the prerequisites is understanding the different types of bicycling infrastructure that exist in an urban area and create an inventory dataset that can form the basis of future bicycling-related research. OpenStreetMap (OSM) is a valuable open-source map database that contains transport infrastructure data among other things and has spatial coverage for almost the entire planet. Hence, it is used extensively by researchers and planners and it helps develop methods that are transferable and thus can be replicated irrespective of the study area. We, the Sustainable Mobility and Safety Research Group (SMSR) at Monash University, Australia, have developed a classification process to classify existing bicycling infrastructure across Greater Melbourne, Australia. We have derived knowledge from existing studies and calibrated our classification system to suit local tagging practices.

Polygon geometry with attributes displaying the East Baton Rouge Sewerage Commission (EBROSCO) sewer system map key grid in East Baton Rouge Parish, Louisiana.