Homeland Infrastructure Foundation-Level Data (HIFLD) geospatial data sets containing information on US Army Corps of Engineers (USACE) Civil Works Districts.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Infrastructure helps determine the success of manufacturing and agricultural activities. Investments in water, sanitation, energy, housing, and transport also improve lives and help reduce poverty. And new information and communication technologies promote growth, improve delivery of health and other services, expand the reach of education, and support social and cultural advances. Data here are compiled from such sources as the International Road Federation, Containerisation International, the International Civil Aviation Organization, the International Energy Association, and the International Telecommunications Union.

This database categorizes 91 projects using nature-based solutions (NBS) in riverine environments across the United States. These 91 projects were identified in a non-exhaustive search of Federal, State, local, and other publicly available documentation. Eight publicly available reports and websites collectively described 45 projects, while the remaining projects were sourced from individual websites or articles that described one or two projects each. For each project, we identified the following: NBS strategy or strategies implemented, total cost, year implemented, project size, and project city and state. Here, project size refers to the stream length in feet influenced by the project. For some projects, details such as project cost and project size were not recorded in publicly available documents and reports.

This data set is thedata of new plant survey and soil/media analysis for journal article currently titled "Developing multiple lines of evidence to decrease drainage to surface area ratio for effective stormwater control using bioinfiltration". This dataset is associated with the following publication: Oconnor, T. Using multiple metrics to show benefit of increasing watershed to surface area ratio for green infrastructure bioinfiltration design. Journal of Sustainable Water in the Built Environment. American Society of Civil Engineers (ASCE), New York, NY, USA, 9(1): 04022019, (2023).

This dataset features over 600,000 high-quality images of bridges sourced from photographers worldwide. Created to support AI and machine learning applications, it offers a richly annotated and visually diverse collection of bridge structures, environments, and engineering designs.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with object and scene detection metadata, including bridge type, materials, span structure, and environmental context—making it ideal for tasks like classification, detection, and structural analysis. Popularity metrics, based on performance on our proprietary platform, are also included.

1. Unique Sourcing Capabilities: images are collected through a proprietary gamified platform for photographers. Competitions centered on bridge and infrastructure photography ensure high-quality, current content. Custom datasets can be delivered within 72 hours to meet specific criteria such as bridge types (suspension, arch, beam, etc.), geographic regions, or surrounding environments (urban, rural, coastal, etc.).

2. Global Diversity: contributors from over 100 countries have provided imagery of bridges across a wide variety of geographies and engineering styles. The dataset includes historic, modern, pedestrian, rail, and vehicular bridges, captured from multiple angles and in varied lighting and weather conditions.

3. High-Quality Imagery: resolutions range from standard to ultra-high definition, suitable for both large-scale structural analysis and fine-detail inspection. A mix of professional and contextual photography ensures practical utility for real-world AI training and simulation.

4. Popularity Scores: each image is assigned a popularity score derived from its performance in GuruShots competitions. This unique metric can enhance models that factor in visual appeal, user preference, or structural aesthetics.

5. AI-Ready Design: the dataset is optimized for machine learning workflows, ideal for use in bridge classification, structural integrity modeling, environmental context recognition, and generative design training. Compatible with major ML frameworks and geospatial platforms.

6. Licensing & Compliance: all data is compliant with global privacy laws and infrastructure-related content regulations, with clear licensing for commercial and academic use.

Use Cases: 1. Training AI for bridge recognition, type classification, and structural assessment. 2. Supporting infrastructure planning, maintenance prediction, and safety monitoring. 3. Enhancing AR/VR simulations, city modeling, and digital twin applications. 4. Empowering academic research in civil engineering, architecture, and environmental design.

This dataset provides a robust, high-quality resource for AI applications in civil infrastructure, engineering, and urban analytics. Custom configurations are available. Contact us to learn more!

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for civil and infrastructure engineering in the U.S.

Cogs-Excluding-Depreciation-and-Amortization Time Series for Construction Partners Inc. Construction Partners, Inc., a civil infrastructure company, constructs and maintains roadways in Alabama, Florida, Georgia, North Carolina, South Carolina, Tennessee, and Texas. The company provides various products and services to public and private infrastructure projects, such as highways, roads, bridges, airports, and commercial and residential developments. It engages in manufacturing and distributing hot mix asphalt (HMA) for internal use and sales to third parties in connection with construction projects; and paving activities, including the construction of roadway base layers and application of asphalt pavement. In addition, the company is involved in site development, including the installation of utility and drainage systems; mining aggregates, such as sand, gravel, and construction stones that are used as raw materials in the production of HMA; and distributing liquid asphalt cement for internal use and sales to third parties in connection with HMA production. The company was formerly known as SunTx CPI Growth Company, Inc. and changed its name to Construction Partners, Inc. in September 2017. The company was incorporated in 2007 and is headquartered in Dothan, Alabama.

Series Name: Amount of United States dollars committed to public-private partnerships for infrastructure million USD realSeries Code: GF_COM_PPPI_KDRelease Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 17.17.1: Amount in United States dollars committed to public-private partnerships for infrastructureTarget 17.17: Encourage and promote effective public, public-private and civil society partnerships, building on the experience and resourcing strategies of partnershipsGoal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable DevelopmentFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Table 1. Selected Innovative Rehabilitation Technologies - summarizes the descriptions of the technologies. Table 2. Summary of test data for CIPP liner - gives the values of the lab results. Table 3. Summary of Four Technology Evaluations - summarizes the benefits of technologies. This dataset is associated with the following publication: Selvakumar , A., and J. Matthews. Demonstration and Evaluation of Innovative Rehabilitation Technologies for Water Infrastructure Systems. Journal of Pipeline Systems Engineering and Practice. American Society of Civil Engineers (ASCE), Reston, VA, USA, 8(4): 1949-1204, (2017).

The spreadsheets that provide the unit and regional cost variables and equations for all of the green infrastructure and low impact development controls programmed into the National Stormwater Calculator.

This dataset is associated with the following publications: Rossman, L., and J. Bernagros. NATIONAL STORMWATER CALCULATOR WEB APP USER’S GUIDE – VERSION 3.2.0 - manual. U.S. Environmental Protection Agency, Washington, DC, USA, 2019. Bernagros, J., D. Pankani, S. Struck , and M. Deerhake. Estimating Regionalized Planning Costs of Green Infrastructure and Low-Impact Development Stormwater Management Practices: Updates to the US Environmental Protection Agency’s National Stormwater Calculator. Journal of Sustainable Water in the Built Environment. American Society of Civil Engineers (ASCE), New York, NY, USA, 7(2): 04020021-1, (2021).

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Background: Efforts to support disadvantaged communities have been prioritized through initiatives like Justice40, the Inflation Reduction Act (IRA), and the Bipartisan Infrastructure Law (BIL). Identifying disadvantaged communities involves several datasets with associated variables related to vulnerability indicators and scores. There are three key datasets:

1. Climate and Economic Justice Screening Tool (CEJST) from the White House Council on Environmental Quality (CEQ).
2. Energy Justice Mapping Tool from the Department of Energy (DOE) Office of Economic Impact & Diversity.
3. Environmental Justice Screening Tool from the Environmental Protection Agency (EPA) Office of Environmental Justice and External Civil Rights.

Problem:

1. Each dataset employs similar but distinct criteria and outcomes.
2. The unique IDs or GEOIDs differ across datasets. CEJST uses the 2010 census boundaries, while the Energy Justice Mapping Tool and the Environmental Justice Screening Tool are based on the 2019 and 2021 census boundaries, respectively.

To address these issues, this dataset consolidates information on disadvantaged communities and their associated variables by combining the three distinct datasets:

• CEJST: Provides binary data indicating whether a tract is a disadvantaged community. A community is classified as disadvantaged if it meets any of the following thresholds: 1) one or more indicators within categories such as climate change, energy, health, housing, pollution, transportation, and water & wastewater, coupled with low income; 2) one or more indicators in workforce development category and education; or 3) tribal lands. Environment and pollution indicators come from the EPA, while socio-demographic indicators are from the American Community Survey (ACS) for 2015-2019.

• Energy Justice Mapping Tool: Offers a DAC score, a continuous variable representing the sum of the 36 indicator percentiles. It includes environment, pollution, and socio-demographic indicators from the EPA and ACS (2015-2019).

• Environmental Justice Screening Tool: Includes the 13 Environmental Justice (EJ) Index and Supplemental Index. These continuous variables are weighted with socio-demographic indicators from ACS (2017-2021).

Data Descriptions

• Unit of Analysis: Census tract
• Geometry: 2021 census boundaries
• Columns:
  • Socio-demographic indicators
  • 13 Environmental Justice (EJ) index values
  • 13 Supplemental index values
  • Binary indicator for disadvantaged community classification
  • Disadvantaged Community (DAC) scores
• Datasets:
  • results/DAC.csv: Contains all columns from the three datasets.
  • results/DAC_s.csv: A shorter version, including socio-demographic indicators and EJ and Supplemental indices (Environmental Justice Screening Tool), disadvantaged community classification (CEJST), and DAC scores (Energy Justice Mapping Tool).
• Code:
  • syntax/code.R: This script illustrates the methodology for merging the three datasets, culminating in the creation of the two CSV files located in the results directory.

Purpose:

The dataset aims to help researchers identify overall disadvantaged communities or determine which specific communities are classified as disadvantaged. By consolidating these datasets, researchers can more effectively analyze and compare the various criteria used to define disadvantaged communities, enhancing the comprehensiveness of their studies.

Note

For complete data descriptions and sources, please refer to the original datasets.

• Climate and Economic Justice Screening Tool (CEJST)
• Energy Justice Mapping Tool
• Environmental Justice Screening Tool

Meterology, hydrologic, and water quality data for the inflow and outflow of a rain garden system treating stormwater runoff from a parking lot and wooded hillslope. The data in this file was used to calibrated, validate, and evaluate a Green Infrastructure performance model for hydrology, hydraulics, and water quality. This dataset is associated with the following publication: Alikhani, J., C. Nietch, S. Jacobs, W. Shuster, and A. Massoudieh. Modeling and design scenarios analysis of a long-term monitored rain garden for rainfall-runoff reduction to a combined sewer in Cincinnati, OH. Journal of Sustainable Water in the Built Environment. American Society of Civil Engineers (ASCE), New York, NY, USA, 6(2): 04019016-1, (2020).

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