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.

Geospatial Dataset

## Overview

Geospatial Dataset is a dataset for instance segmentation tasks - it contains Geospatial annotations for 1,048 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Google Earth Pro facilitated the acquisition of satellite imagery to monitor deforestation in Dhaka, Bangladesh. Multiple years of images were systematically captured from specific locations, allowing comprehensive analysis of tree cover reduction. The imagery displays diverse aspect ratios based on satellite perspectives and possesses high resolution, suitable for remote sensing. Each site provided 5 to 35 images annually, accumulating data over a ten-year period. The dataset classifies images into three primary categories: tree cover, deforested regions, and masked images. Organized by year, it comprises both raw and annotated images, each paired with a JSON file containing annotations and segmentation masks. This organization enhances accessibility and temporal analysis. Furthermore, the dataset is conducive to machine learning initiatives, particularly in training models for object detection and segmentation to evaluate environmental alterations.

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"

Spatial prepositions have been studied in some detail from multiple disciplinary perspectives. However, neither the semantic similarity of these prepositions, nor the relationships between the multiple senses of different spatial prepositions, are well understood. In an empirical study of 24 spatial prepositions, we identify the degree and nature of semantic similarity and extract senses for three semantically similar groups of prepositions using t-SNE, DBSCAN clustering, and Venn diagrams. We validate the work by manual annotation with another data set. We find nuances in meaning among proximity and adjacency prepositions, such as the use of close to instead of near for pairs of lines, and the importance of proximity over contact for the next to preposition, in contrast to other adjacency prepositions.

This data contain the minimal annotation for datasets described in the publication:Identifying witness accounts from social media using imagery, (2017). ISPRS International Journal of Geo-Information.

CrisisLandMark is a large-scale, multimodal corpus for Text-to-Remote-Sensing-Image Retrieval (T2RSIR). It contains over 647,000 Sentinel-1 (SAR) and Sentinel-2 (multispectral optical) images enriched with structured textual and geospatial annotations. The dataset is designed to move beyond standard RGB imagery, enabling the development of retrieval systems that can leverage the rich physical information from different satellite sensors for applications in Land Use/Land Cover (LULC) mapping and crisis management.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Annotation created from Indian Lands and Native Entities.

The Geodatabase to Shapefile Warning Tool examines feature classes in input file geodatabases for characteristics and data that would be lost or altered if it were transformed into a shapefile. Checks include:
1) large files (feature classes with more than 255 fields or over 2GB), 2) field names longer than 10 characters
string fields longer than 254 characters, 3) date fields with time values 4) NULL values, 5) BLOB, guid, global id, and raster field types, 6) attribute domains or subtypes, and 7) annotation or topology

The results of this inspection are written to a text file ("warning_report_[geodatabase_name]") in the directory where the geodatabase is located. A section at the top provides a list of feature classes and information about the geodatabase as a whole. The report has a section for each valid feature class that returned a warning, with a summary of possible warnings and then more details about issues found.

The tool can process multiple file geodatabases at once. A separate text file report will be created for each geodatabase. The toolbox was created using ArcGIS Pro 3.7.11.

For more information about this and other related tools, explore the Geospatial Data Curation toolkit

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

Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or ‘label images’) collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and nadir imagery. Images include a diverse range of coastal environments from the U.S. Pacific, Gulf of Mexico, Atlantic, and Great Lakes coastlines, consisting of time-series of high-resolution (≤1m) orthomosaics and satellite image tiles (10–30m). Each image, image annotation, and labelled image is available as a single NPZ zipped file. NPZ files follow the following naming convention: {datasource}_{numberofclasses}_{threedigitdatasetversion}.zip, where {datasource} is the source of the original images (for example, NAIP, Landsat 8, Sentinel 2), {numberofclasses} is the number of classes us ...

This compressed file geodatabase contains the following layers: Legal Subdivisions - Line Legal Subdivisions - Polygon Legal Annotation Cadastral Control Points This dataset is updated on a weekly basis.

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!

Line coverage for geologic lines including isograds, axial traces, cross-section lines, etc.. Includes annotation for geologic names such as plutons and locations for sample sites. The geologic data was mapped during the summer of 1998 at a scale of 1:24,000. The bedrock geology was mapped using standard techniques. Locational information was provided by Rockwell PLGR+96 GPS receivers using the federal precise positioning service. The line and polygon data were compiled on a mylar greenline of the 7.5-minute quadrangle and scanned at 400 dpi on an Anatech Eagle 4080T scanner. The raster files (TIF) were converted to vector files (DXF) using GTX OSR version 2.0 raster-to-vector conversion software. The vector files were imported to Arc/Info version 7.0.4. Point data were collected with GPS and hand held 3COM Palm Pilot III PDA computers. Data from the PDAs were combined in Microsoft Access, and then imported as ASCII text files and (dbf) files into Arc/Info. The Arc/Info coverages were registered and transformed to the New Hampshire State Plane Coordinate system.

The OliveTreeCrown dataset contains high-resolution images divided into various grid configurations: 1×1 (original), 3×3, 6×6, and 9×9. Each segment is thoroughly annotated to ensure accurate object detection, providing precise and detailed labeling of olive tree crowns. In addition to the annotated image data, the dataset contains a point cloud representation, a Digital Elevation Model (DEM), and spatial data in Keyhole Markup Language (KML) format. These components collectively capture the three-dimensional geometry, topographic features, and geospatial characteristics of the study area. The XYZ coordinates in the point cloud data define the precise spatial position of each point, contributing to a comprehensive spatial representation. By integrating 3D data and geospatial attributes, this dataset offers a valuable resource for advanced spatial modeling and analysis. It serves as a solid foundation for applications such as multi-scale analysis, 3D mapping, and precision agriculture, fostering innovation in remote sensing and AI-driven agricultural solutions.

The Ministry of Economic Affairs' Water Resources Agency's Disaster Emergency Response Team, utilizing long-term disaster response experience, further combines real-time data such as rainfall, water levels, and reservoir levels, through computer technology to provide water level alerts to the public and relevant units. This helps people understand the risk of home flooding, prepare early, and reduce the occurrence of disasters. This dataset is linked to a Keyhole Markup Language (KML) file list, which is a markup language based on the eXtensible Markup Language (XML) syntax standard, developed and maintained by Google's Keyhole company for expressing geospatial annotations. Documents written in the KML language are referred to as KML files and are used in Google Earth-related software (Google Earth, Google Map, Google Maps for mobile, etc.) for displaying geospatial data. Many GIS-related systems now also use this format for geospatial data exchange, and the KML of this data uses UTF-8 encoding.