Enhance map accuracy with geospatial data annotation. Mark, classify, and refine geographical data for clearer, more detailed, reliable maps.

The data labeling market is experiencing robust growth, projected to reach $3.84 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 28.13% from 2025 to 2033. This expansion is fueled by the increasing demand for high-quality training data across various sectors, including healthcare, automotive, and finance, which heavily rely on machine learning and artificial intelligence (AI). The surge in AI adoption, particularly in areas like autonomous vehicles, medical image analysis, and fraud detection, necessitates vast quantities of accurately labeled data. The market is segmented by sourcing type (in-house vs. outsourced), data type (text, image, audio), labeling method (manual, automatic, semi-supervised), and end-user industry. Outsourcing is expected to dominate the sourcing segment due to cost-effectiveness and access to specialized expertise. Similarly, image data labeling is likely to hold a significant share, given the visual nature of many AI applications. The shift towards automation and semi-supervised techniques aims to improve efficiency and reduce labeling costs, though manual labeling will remain crucial for tasks requiring high accuracy and nuanced understanding. Geographical distribution shows strong potential across North America and Europe, with Asia-Pacific emerging as a key growth region driven by increasing technological advancements and digital transformation. Competition in the data labeling market is intense, with a mix of established players like Amazon Mechanical Turk and Appen, alongside emerging specialized companies. The market's future trajectory will likely be shaped by advancements in automation technologies, the development of more efficient labeling techniques, and the increasing need for specialized data labeling services catering to niche applications. Companies are focusing on improving the accuracy and speed of data labeling through innovations in AI-powered tools and techniques. Furthermore, the rise of synthetic data generation offers a promising avenue for supplementing real-world data, potentially addressing data scarcity challenges and reducing labeling costs in certain applications. This will, however, require careful attention to ensure that the synthetic data generated is representative of real-world data to maintain model accuracy. This comprehensive report provides an in-depth analysis of the global data labeling market, offering invaluable insights for businesses, investors, and researchers. The study period covers 2019-2033, with 2025 as the base and estimated year, and a forecast period of 2025-2033. We delve into market size, segmentation, growth drivers, challenges, and emerging trends, examining the impact of technological advancements and regulatory changes on this rapidly evolving sector. The market is projected to reach multi-billion dollar valuations by 2033, fueled by the increasing demand for high-quality data to train sophisticated machine learning models. Recent developments include: September 2024: The National Geospatial-Intelligence Agency (NGA) is poised to invest heavily in artificial intelligence, earmarking up to USD 700 million for data labeling services over the next five years. This initiative aims to enhance NGA's machine-learning capabilities, particularly in analyzing satellite imagery and other geospatial data. The agency has opted for a multi-vendor indefinite-delivery/indefinite-quantity (IDIQ) contract, emphasizing the importance of annotating raw data be it images or videos—to render it understandable for machine learning models. For instance, when dealing with satellite imagery, the focus could be on labeling distinct entities such as buildings, roads, or patches of vegetation.October 2023: Refuel.ai unveiled a new platform, Refuel Cloud, and a specialized large language model (LLM) for data labeling. Refuel Cloud harnesses advanced LLMs, including its proprietary model, to automate data cleaning, labeling, and enrichment at scale, catering to diverse industry use cases. Recognizing that clean data underpins modern AI and data-centric software, Refuel Cloud addresses the historical challenge of human labor bottlenecks in data production. With Refuel Cloud, enterprises can swiftly generate the expansive, precise datasets they require in mere minutes, a task that traditionally spanned weeks.. Key drivers for this market are: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Potential restraints include: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Notable trends are: Healthcare is Expected to Witness Remarkable Growth.

Annotation feature class that provides labels for property boundary lengths and acreage of parcels in Chatham County, NC. This service also provides annotation for easements in the Chatham County parlines feature class.

The annotation feature class is maintained by the Chatham County GIS & Tax departments and is updated on a daily basis.Chatham GIS SOP: "MAPSERV-163"

Annotation created from Indian Lands and Native Entities.

Annotation for the Assessor's GIS data. This service is used in the OpenWeb and Opendoor application's.

This tile cache service shows the locations of both in service and out of service submarine telecommunication cables from the North American Submarine Cable Association (NASCA) in coastal and offshore waters within the Exclusive Economic Zone (EEZ). Submarine cable data were originally received from NASCA as Route Position Lists (RPLs), and geospatial products were later received from Pacific Marine Systems, which had contracted with NASCA to produce datasets using the same RPLs. The geospatial data from Pacific Marine Systems were compared against the RPLs and subsequently used in tile cache creation. Submarine cable locations were screened out within 100 meters of landfall, in addition to cable segments that extend beyond the EEZ and do not reenter U.S. maritime waters. Cables which exit and reenter U.S. waters remained intact. Cables are visible from a scale range of 1:18,489,298 to 1:36,112. Each cable contains annotation which references cable name, segment (if applicable), and ownership. Annotation is available at scales from 1:577,791 to 1:72,224. Visual representation of cables and annotation used published NASCA charts on NASCA's website (http://www.n-a-s-c-a.org/cable-maps) as a guide.

This dataset provides pixel-wise annotations of photovoltaic (PV) systems at a spatial resolution of 0.2 m, distinguishing between rooftop PV (RTPV) and ground-mounted PV (GMPV). Two types of labels are included: (1) automatic annotations, available only for the state of Hesse, derived from geospatial sources; and (2) manually curated annotations for selected areas in Hesse and North Rhine-Westphalia (NRW), produced using high-resolution orthoimagery from Hesse and openNRW. The label data are provided without imagery, enabling integration with arbitrary remote sensing sources for applications such as PV detection and segmentation.

The automatic annotations were generated using geospatial sources including OpenStreetMap (OSM), the Official Topographic-Cartographic Information System (ATKIS), and data from the Leibniz Institute of Ecological Urban and Regional Development (IOER) in Dresden. After merging these sources, GMPV installations were supplemented with data from Manske, D. (2025), “Geo-locations and System Data of Renewable Energy Installations in Germany,” Zenodo. doi:10.5281/zenodo.14627853.