ISO 15143‑3 AEMP Data Hub Market Outlook

According to our latest research, the Global ISO 15143‑3 AEMP Data Hub market size was valued at $1.2 billion in 2024 and is projected to reach $4.7 billion by 2033, expanding at a robust CAGR of 16.5% during the forecast period of 2025–2033. The major factor propelling the growth of this market globally is the increasing adoption of telematics and connected equipment across heavy industries such as construction, mining, and agriculture. The ISO 15143‑3 (AEMP 2.0) standard has become a pivotal enabler for seamless data integration and interoperability, allowing stakeholders to optimize fleet management, enhance equipment utilization, and drive predictive maintenance initiatives. This rapid digital transformation, coupled with heightened demand for real-time asset tracking and regulatory mandates for equipment data transparency, is reshaping operational paradigms and accelerating market expansion worldwide.

Regional Outlook

North America commands the largest share of the global ISO 15143‑3 AEMP Data Hub market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region’s mature construction and mining sectors, widespread adoption of advanced telematics, and early implementation of the ISO 15143‑3 standard by major OEMs and fleet operators. The United States, in particular, benefits from a robust regulatory framework, strong digital infrastructure, and a high concentration of industry-leading technology providers. These factors have fostered an environment conducive to rapid innovation, integration of cloud-based data hubs, and comprehensive deployment of asset tracking solutions. Moreover, North American enterprises are increasingly leveraging data-driven insights to streamline operations, reduce costs, and comply with stringent safety and emissions regulations, further reinforcing the region’s leadership in the global market.

In contrast, Asia Pacific is emerging as the fastest-growing region in the ISO 15143‑3 AEMP Data Hub market, projected to register a remarkable CAGR of 20.3% from 2025 to 2033. The surge in infrastructure development, urbanization, and government-led digitalization initiatives across China, India, Japan, and Southeast Asia is driving the adoption of connected equipment and telematics platforms. Investment in smart construction, mining automation, and precision agriculture is accelerating demand for interoperable data hubs compliant with ISO 15143‑3 standards. Additionally, the influx of global OEMs, rising construction equipment sales, and supportive policy frameworks are catalyzing technological upgrades and cloud deployments in the region. As Asia Pacific economies continue to modernize their industrial base, the appetite for real-time equipment monitoring and predictive maintenance solutions is expected to fuel sustained market growth.

Meanwhile, Latin America and the Middle East & Africa represent promising but comparatively nascent markets for ISO 15143‑3 AEMP Data Hubs. These regions face unique challenges such as limited digital infrastructure, fragmented equipment ownership, and varying regulatory landscapes. However, localized demand for fleet management and asset tracking is gradually increasing, particularly in sectors like oil & gas and agriculture. Governments and private operators are beginning to recognize the value of standardized data integration for enhancing operational efficiency and safety. Targeted investments in connectivity, workforce training, and policy harmonization are expected to bridge adoption gaps over the forecast period, positioning these emerging economies as significant contributors to future market expansion.

Report Scope

Public facing app to be rebuilt soon-ish. 12/21/23

Checkbook NYC 2.0 provides unprecedented access to view and track how New York City government spends its nearly $70 billion annual budget.

This repository includes the data displayed in the LULUCF Data Hub, an online platform hosted in the EU Forest Observatory compiling various datasets of country-level CO₂ emissions and removals from land use, land-use change and forestry (LULUCF). The LULUCF Data Hub is described by Melo et al. (in preparation).

Version 2.0 (NGHGI 2024 release) includes:

Update of version 1.0 of the NGHGI LULUCF database compiled by the European Commission (EC) Joint Research Centre (JRC) and first described by Grassi et al. 2022. The database includes CO₂ fluxes from LULUCF sourced from the National Greenhouse Gas Inventories (NGHGIs) submitted to the United Nations Framework Convention on Climate Change (UNFCCC), using methodologies established by the Intergovernmental Panel on Climate Change (IPCC) (timeseries_NGHGI_2.0.csv). CO₂ fluxes are allocated to the classes Forest (excl. organic soils, excl. HWP), Deforestation (conversion of forest land to other land uses), Other non-forest land uses, Organic soils, and Harvested wood products (HWP), with gaps filled without altering the levels and trends of the reported data.

Version 2.0 of the NGHGI database includes data from country reporting to the UNFCCC under the Paris Agreement reporting rules (or “Paris GHG reporting”, noting that reporting to the Paris Agreement started in 2024). Data is extracted from Common Reporting Tables (CRT) related to the first Biennial Transparency Report (BTR) for 78 countries (we include data submitted until April 2025). These data are complemented by pre-Paris GHG reporting data from V1.0 for the remaining 107 countries with LULUCF data. Of the 198 Parties to the UNFCCC, 12 countries do not report emissions and removals for the LULUCF sector. Information on data sources by country is available in the file text_box_iso3_2.0.csv.

In the LULUCF Data Hub version 2.0, the NGHGI estimates (2000-2023) are compared to results from two key global initiatives:

• Global Carbon Budget (GCB) 2024 data from Friedlingstein et al. (2025), using four bookkeeping models to estimate CO₂ fluxes from Forest, Deforestation and Other transitions, and external datasets to estimate CO₂ emissions from Organic soils, from 2000 onwards. These original results are reclassified to match the NGHGI classes (timeseries_GCB_original_2.0.csv). In addition, forest fluxes from the GCB are adjusted to the NGHGI definition of human-induced CO₂ sink in managed land based on the methodology from Grassi et al. (2023) (timeseries_GCB_translated_2.0.csv). This increases the forest area and adds the indirect anthropogenic effects (e.g., due to increased atmospheric CO₂) compared to the original/reclassified results.
• Global Forest Watch (GFW) 2024 data from Gibbs et al. (2025) includes CO₂ fluxes from forests and deforestation from 2001 onwards, estimated by integrating Earth observation data into a geospatial GHG monitoring framework. Gross fluxes from the model are reclassified to match the NGHGI classes (timeseries_GFW_original_2.0.csv). In addition, forest fluxes from GFW are adjusted to the NGHGI definition of human-induced CO₂ sink in managed land by excluding the area of unmanaged forests (timeseries_GFW_translated_2.0.csv). Thus, in this case, forest area increases compared to the original/reclassified results.

Note there is no update to GCB and GFW data from version 1.0 to version 2.0. Both versions use the 2024 data from these two datasets.

Data is available in Mt CO₂ yr⁻¹. Net CO₂ fluxes include emissions by sources and removals by sinks. Non-CO₂ emissions are not included. Countries are identified by their ISO3 code. In addition, the timeseries files also include aggregated estimates for World (WRD) and the European Union (EU27).

The LULUCF Data Hub was pre-released during the IPCC Expert Meeting on Reconciling Anthropogenic Land-Use Emissions (IPCC, 2024; Grassi et al., 2025). In November 2024, version 1.0 was released, and is here complemented by version 2.0. The current versions available are:

• Version 1.0: NGHGI 2023 (pre-Paris GHG reporting, updated August 2024), GCB 2024, GFW 2024.
• Version 2.0: NGHGI 2024 (Paris GHG reporting, updated April 2025, complemented by pre-Paris GHG reporting from version 1.0), GCB 2024, GFW 2024.

For questions regarding this dataset, please email joana.brandao-de-melo@ec.europa.eu or giacomo.grassi@ec.europa.eu

Citation policy: Users should cite the dataset version they use (this dataset) for the NGHGI file. For the GCB-derived files, please cite Friedlingstein et al. (2025), and for the GFW-derived files, please cite Gibbs et al. (2025). When referring to the dataset as a whole, the Zenodo version DOI or concept DOI should be used. For methodological details and context on the LULUCF Data Hub, please cite the associated data description paper: Melo et al. (in preparation, Earth System Science Data).

This is a graphic representation of the data stewards based on PLSS Townships in PLSS areas. In non-PLSS areas the metadata at a glance is based on a data steward defined polygons such as a city or county or other units. The identification of the data steward is a general indication of the agency that will be responsible for updates and providing the authoritative data sources. In other implementations this may have been termed the alternate source, meaning alternate to the BLM. But in the shared environment of the NSDI the data steward for an area is the primary coordinator or agency responsible for making updates or causing updates to be made. The data stewardship polygons are defined and provided by the data steward.

A collection of 22 data set of 50+ requirements each, expressed as user stories.

The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

Overview of the datasets [data and links added in December 2024]

The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

Public administration and transparency

g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

(Research) data and meta-data management

g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its

The Toxic Substances Control Act Test Submissions 2.0 (TSCATS 2.0) tracks the submissions of health and safety data submitted to the EPA either as required or voluntarily under certain sections of TSCA.

The Youth Risk Behavior Surveillance System (YRBSS) monitors six types of health-risk behaviors that contribute to the leading causes of death and disability among youth and adults. This file contains state-level results for 13 tobacco-use variables by sex and grade for 2013.

Soil organic carbon stock in t/ha for 0-30 cm depth interval. Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global compilation of soil profile data and environmental layers. To visualize these layers please use www.soilgrids.org.

These are areas of water that are defined from meander lines of the PLSS and GLO surveys. These are not the official representations of coast or water lines and are representations of the lines marked by the survey along the boundaries of meandered water at the time of survey

940034 nanopublications. These nanopubs were automatically extracted from the DisGeNET dataset. See also the main DisGeNET data on Datahub.

Download the content of this set of nanopublications from the server network using nanopub-java:

$ np get -c -o nanopubs.trig RAXy332hxqHPKpmvPc-wqJA7kgWiWa-QA0DIpr29LIG0Q

Korean War Era Veterans by Year, 2000-2040. This chart supports the Korean War data story at: https://www.datahub.va.gov/stories/s/7wja-85c3

This file is a digital geospatial Environmental Systems Research Institute (ESRI) ArcGIS File Geodatabase Polygon Feature Class representing land use in the seven northeastern Illinois counties (Cook, DuPage, Kane, Kendall, Lake, McHenry and Will). Land use is identified to 60 categories, and was created using county parcel GIS boundaries and Assessor data, along with color orthorectificed aerial photography captured in April, 2010. Land uses were assigned to parcels using a combination of automated and manual techniques, using a variety of reference data sets for land use identification and validation. Parcels were then dissolved on common land uses (to the limits of PLS sections or assessor blocks); polygons were generated for “non-parcel” (water, right-of-way) areas and classified using automated processes, and extnesive topological cleaning was necessary to minimize gap/overlap issues.In addition to this metadata record, additional information can be found in the following documents:2010 Land Use Inventory Classification Scheme2010 Land Use Inventory Geodatabase Schema2010 Land Use Inventory MetadataLANDUSE lookup .csv table to support shapefile downloadsProcess Narrative: Creating the 2010 Land Use Inventory for Northeastern IllinoisProcess Narrative Addendum: Creating Version 2.0Comparison Guide: Differences between the 2010 and 2005 Land Use InventoriesNOTE: Land use polygons are based on county parcel boundaries; special care must be exercised when comparing these data to earlier (2005, 2001, 1990) Inventories, which relied on manual drafting of land use boundaries that would extend to road centerlines. Additionally, the classification scheme was rewritten to accommodate the parcel approach; see the "comparison guide" link above for inter-inventory analysis guidance.

This data set contains calibrated images and calibration data from the Stardust Navigation Camera (NAVCAM) observations of comet 81P/Wild 2. 206 calibrated images were obtained over the 5-day period 2003-12-29 to 2004-01-02.

The aerosol optical depth processing includes the spectral de-convolution algorithm (SDA) described in O'Neill et al. (2003). This algorithm yields fine (sub-micron) and coarse (super-micron) aerosol optical depths at a standard wavelength of 500 nm (from which FMF*, the fraction of fine mode to total aerosol optical depth can be computed).

This data set contains the data originally archived in TRIAD and reported in Morrison & Zellner, Asteroids (T. Gehrels, Ed., The University of Arizona Press, 1979) [MORRISON&ZELLNER1979]. It tabulates the reduced values of the polarization curves of the asteroids, namely minimum polarization, inversion angle, and polarimetric slope.

(File Size – 375 MB) Click on the title for more details and to download the file.

This data set contains the Zappala et al. (1995) [ZAPPALAETAL1995] asteroid dynamical families classifications, based on the hierarchical clustering method applied to the proper elements of Milani & Knezevic (1994) [MILANI&KNEZEVIC1994]. It includes 12,487 numbered and unnumbered asteroids.

Streamflow discharge time series metadata service encoded in WFS. The WaterMLURI field in the metadata contains a URI that could be WSDL, SOS, Kisters Query Service (KiQS), USGS WaterML Service, or other web service endpoint for stream discharge content in WaterML 1.x or WaterML 2.0 encoding. CAVEAT: This service registration is a proof of concept only. The University of Texas Center for Research in Water Resources is not an official map service for the national hydrologic surveys of the countries represented in this catalog.

Ensemble de fonctions en services web WPS, permettant d'accéder aux horaires, passages, tronçons, lieux public, etc... issus de la base de données SIG de Bordeaux Métropole.Nous mettons à votre disposition un démonstrateur web qui permet d'illustrer par des exemples concrets et didactiques l'utilisation de nos Webservices ainsi notre Api cartographique.WebService WPSFonctionnementLe WPS est un service d'accès temps réel. Pour plus de détails, reportez-vous à la FAQ.URL du WebService : https://data.bordeaux-metropole.fr/wps?key=[VOTRECLE]&SERVICE=WPS&VERSION=2.0.0&REQUEST=EXECUTE&IDENTIFIER=[NOMCOUCHE]Remplacez [VOTRECLE] par la clé logicielle qui vous a été fournie par Bordeaux Métropole. Pour en commander une (valide pour les WebServices WMS, WFS, WPS et API CUB), cliquez-iciSi vous souhaitez consulter plus d'information sur les webservices OGC cliquez sur ce lienNom de la coucheNom de la couche à fournir à votre client : Nom coucheDescriptionsaeiv_arret_horairesRetourne les horaires sur un arrêt à un jour donnésaeiv_arret_passagesRetourne tous les passages à venir sur un arret TBMsaeiv_arrets_cheminRetourne les arrêts ordonnés d'un chemin de ligne, incluant les arrêts des déviationssaeiv_arrets_sensRetourne les arrêts d'un sens de lignesaeiv_troncons_sensRetourne tous les tronçons d'un sens de ligne (ne retourne pas les déviations)saeiv_troncons_cheminRetourne tous les tronçons d'un chemin de ligne, incluant les déviations pour une date donnéesaeiv_arrets_flexoRetourne les arrêts d'une zone Flexosaeiv_lipub_arretRetourne tous les lieux publics en relation avec un arrêtsaeiv_correspondancesRetourne les correspondances TBM autour d'un arrêt donné