The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of location-based services (LBS) across various sectors, including transportation, logistics, and e-commerce, is a primary driver. Furthermore, the proliferation of smartphones and connected devices, coupled with advancements in GPS technology and mapping software, continues to fuel market growth. The rising demand for high-resolution, real-time mapping data for autonomous vehicles and smart city initiatives also significantly contributes to market expansion. Competition among established players like Google, TomTom, and ESRI, alongside emerging innovative companies, is fostering continuous improvement in map accuracy, functionality, and data accessibility. This competitive landscape drives innovation and lowers costs, making digital maps increasingly accessible to a broader range of users and applications. However, market growth is not without its challenges. Data security and privacy concerns surrounding the collection and use of location data represent a significant restraint. Ensuring data accuracy and maintaining up-to-date map information in rapidly changing environments also pose operational hurdles. Regulatory compliance with differing data privacy laws across various jurisdictions adds another layer of complexity. Despite these challenges, the long-term outlook for the digital map market remains positive, driven by the relentless integration of location intelligence into nearly every facet of modern life, from personal navigation to complex enterprise logistics solutions. The market's segmentation (although not explicitly provided) likely includes various map types (e.g., road maps, satellite imagery, 3D maps), pricing models (subscriptions, one-time purchases), and industry verticals served. This diversified market structure further underscores its resilience and potential for sustained growth. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

The smart city platforms market analysis report by Future Market Insights reveals that global sales of the smart city platforms market in 2022 were held at US$ 157.6 billion. The projected market growth from 2023 to 2033 is expected to be 11%.

Attributes	Details
Global Smart City Platforms Market Size (2023)	US$ 175 billion
Global Smart City Platforms Market Size (2033)	US$ 496.9 billion
Global Smart City Platforms Market CAGR (2023 to 2033)	11%

Scope of Report

Report Attributes	Details
Growth Rate	CAGR of 11% from 2023 to 2033
Base Year for Estimation	2023
Historical Data	2018 to 2022
Forecast Period	2023 to 2033
Global Smart City Platforms Market Size (2023)	US$ 175 billion
Global Smart City Platforms Market Size (2033)	US$ 496.9 billion
Quantitative Units	Revenue in US$ million and CAGR from 2023 to 2033
Report Coverage	Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis
Segments Covered	Offering Delivery Model Application Region
Regions Covered	North America Latin America Western Europe Eastern Europe Asia Pacific excluding Japan Japan The Middle East and Africa
Key Countries Profiled	The United States Canada Brazil Mexico Germany The United Kingdom France Spain Italy Poland Russia Czech Republic India Bangladesh Australia New Zealand China Japan South Korea GCC Countries South Africa Israel
Customization	Available Upon Request

The study focused on a pilot case in a vulnerable settlement in Mexico City with high levels of growth in the last two decades, as well as projections of migrations due to climate change. The research tested mechanisms for long-term sustainable processes of risk mitigation through engaging communities and organisations in a constructive ‘dialogue of knowledges’. The pilot case focused on the dwelling level, and engaged with local communities in the development of strategies and tools for monitoring, adaptation and communication. Through testing these strategies and technologies, the research explored how multi-level actors engage with climate change-related risks and the associated governance structures, such as the development of policy and norms, studying the interaction between technical, socio-cultural, economic, political and institutional factors. An upcoming special volume of the Proceedings of the British Academy on 'Urban Resilience and Climate Change in Latin America' will detail some of the aspects of this research.

The following methods were used in the selected pilot area: (1) Focus groups with residents in two areas in the neighbourhood exposed to different levels of flooding risk (2) 15 Semi-structured interviews with members of the community, including community leaders; and (3) 12 Semistructured interviews with key stakeholders in the public and third sectors in Mexico City.

Cleaned Swiss smart meter data based on a collection from CKW AG (see opendata.ckw.ch)

Duration: 2 years (1. Jan 2021 - 31. Dec 2022, CET timestamp)

Location: Canton Lucerne, Switzerland

Interval: 15 minutes

Values: Active Energy (kWh)

Meters in each year: 4959 (see filtered_IDs.csv for all IDs)

The original dataset has been filtered based

on missing data

This means, all 4959 meters have consumption and reported values over the full duration of 2 years. Files are available as space-saving parquet files per day in year. Number in filename is number of day within the year.

Summary.zip contains summary statistics over all 112148 (unfiltered) meters counting observations (including duplicates) , and aggregating energy data (per month, and or hourly data), see overview.csv within summary.zip

The revenue of the municipal smart city market in Japan was projected to reach about 79.9 billion Japanese yen in fiscal year 2024. The growth of the market has been supported by subsidies of the Digital Garden City Nation initiative since the 2022 fiscal year. The initiative was put forward by the Japanese government to support the digital integration and transformation of rural and urban areas. A part of its subsidies are provided based on the condition that services based on data integration platforms are generated. This led to an increase in deployment of such platforms after the 2022 fiscal year. Since it is expected that more and more municipalities will launch smart city projects at a lower cost compared to earlier projects, the market is forecast to grow steadily in the coming years and reach a size of about 87.5 billion yen by fiscal year 2028.

Built environment and internal trip capture data for 1-mile buffers around Portland MAX Light Rail stations and Washington DC Metro stations. Internal trip capture data is from streetlight and includes counts of trip origins, trip destination, and internal trip count for pedestrian, bicycle, and auto modes for the data period May 1, 2021-April 30, 2022. Data on population from US Census. Data on employment from US Census LODES. Data on intersections and streets are from local GIS files. Some additional variables are from the Environmental Protection Agencies Smart Location Database. GIS data was gathered in 2024 and 2025.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. Countywide Statistical Areas (CSA) are current as of October 2023.

Fields ending in _yr1 were calculated for the original 2021-2022 SBLA report, while fields ending in _yr2 or without a year suffix were calculated for the 2023-2025 version. Eviction Filings per 100 (eviction_filings_per100) and Life Expectancy (life_expectancy) did not have updated data and are the same data shown in the Year 1 report.

Population and demographic data are from US Census American Community Survey (ACS) 5-year estimates, aggregated up from census tract or block group to CSA. Year 1 data are from 2020, year 2 data are from 2022.

Poverty Data (200% FPL) are from LA County ISD-eGIS Demographics. Year 1 data are from 2021, Year 2 are from 2022.

The 2023-2025 report includes several new indicators that are calculated as the percent of countywide population by race that resides in a geographic area of interest. Population for these indicators is estimated based on intersection with census block group centroids. These indicators are:

Indicator

Fields

Source

Health Professional Shortage Areas (HPSA) for Primary Care

hpsa_primary_pct hpsa_primary_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-primary-care/about

Health Professional Shortage Areas (HPSA) for Mental Health

hpsa_mental_pct hpsa_mental_black_pct

LA County DPH https://data.lacounty.gov/datasets/lacounty::health-professional-shortage-area-mental-health/about

Concentrated Disadvantage

cd_pct cd_black_pct

LA County ISD-Enterprise GIS https://egis-lacounty.hub.arcgis.com/datasets/lacounty::concentrated-disadvantage-index-2022/explore

Firearm Dealers

firearm_dl_count (count of dealers in CSA) firearm_dl_per10000 (rate of dealers per 10,000)

LA County DPH Office of Violence Prevention (OVP)

High and Very High Park Need Areas

parks_need_pct parks_need_black_pct

LA County Parks Needs Assessment Plus (PNA+) https://lacounty.maps.arcgis.com/apps/instant/media/index.html?appid=3d0ef36720b447dcade1ab87a2cc80b9

High Quality Transit Areas

hqta_pct hqta_black_pct

SCAG https://lacounty.maps.arcgis.com/home/item.html?id=43e6fef395d041c09deaeb369a513ca1

High Walkability Areas

walk_total_pct walk_black_pct

EPA Walkability Index https://www.epa.gov/smartgrowth/smart-location-mapping#walkability

High Poverty and High Segregation Areas

highpovseg_total_pct highpovseg_black_pct

CTCAC/HCD Opportunity Area Maps https://www.treasurer.ca.gov/ctcac/opportunity.asp

LA County Arts Investments

arts_dollars (total $$ for CSA) arts_dollars_percap (investment dollars per capita)

LA County Department of Arts and Culture https://lacountyartsdata.org/#maps

Strong Start (areas with at least 9 Strong Start indicators)

strongstart_total_pct strongstart_black_pct

CA Strong Start Index https://strongstartindex.org/map

For more information about the purpose of this data, please contact CEO-ARDI.

For more information about the configuration of this data, please contact ISD-Enterprise GIS.

Snapshot img

Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

The dataset for DATA 2022 paper "Dataset: An Indoor Smart Traffic Dataset and Data Collection System"

This archive contains a traffic light dataset that can be used for traffic light detection/classification. The dataset is collected from an indoor smart traffic testbed. In this testbed, we use fences to simulate the road's boundaries and use movable toy traffic signs and traffic lights to simulate those in real-world traffic scenes. An F1TENTH vehicle drives along the fence autonomously. Two cameras are mounted on both sides of the vehicle, which capture images of traffic lights and traffic signs on both sides of the track.

This dataset contains 3507 images captured by the F1TENTH vehicle. Each image comes with ground truth bounding boxes that enclose the traffic lights and a label indicating the current state of the traffic light, 0 for a green light and 1 for a red light.

Please cite our paper if you are using this dataset.

The Smart City ICT Infrastructure market is experiencing explosive growth, projected to reach $42.01 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 35.55% from 2019 to 2033. This expansion is fueled by several key drivers. Increasing urbanization necessitates efficient and sustainable city management, driving demand for advanced ICT infrastructure solutions. The proliferation of smart devices and the Internet of Things (IoT) generates vast amounts of data, necessitating robust data management and security platforms. Government initiatives promoting smart city development and substantial investments in technological upgrades further contribute to market growth. Key market trends include the growing adoption of cloud-based solutions for scalability and cost-effectiveness, the increasing integration of AI and machine learning for enhanced data analysis and decision-making, and the focus on cybersecurity to protect critical infrastructure from threats. While challenges such as high initial investment costs and concerns around data privacy exist, the long-term benefits of improved city services and operational efficiency outweigh these restraints. The market is segmented by platform type (Connectivity, Integration, Device, Data, and Security Management Platforms), application (Smart Mobility/Transportation, Smart Security, Smart Utilities, Smart Governance, Smart Infrastructure, Smart Healthcare), and deployment (On-premise, Cloud). Major players like Siemens, Schneider Electric, Microsoft, and Huawei are actively shaping the market landscape through innovation and strategic partnerships. The North American market currently holds a significant share, followed by Europe and the Asia-Pacific region, which is projected to witness the fastest growth due to rapid urbanization and substantial government investments. The forecast period (2025-2033) promises continued expansion, with specific growth rates varying across segments and regions. The cloud deployment model is expected to dominate due to its flexibility and cost-efficiency. Smart mobility and smart security applications will likely see the highest growth due to increasing concerns about traffic management and public safety. The competitive landscape will remain dynamic, with existing players consolidating their positions and new entrants emerging with innovative solutions. The market's future success hinges on addressing concerns surrounding data privacy and security, ensuring interoperability between different systems, and fostering collaborative partnerships among stakeholders to maximize the benefits of smart city technologies. Future growth will depend significantly on the continued investment in advanced technologies like 5G and the successful integration of various smart city applications. Recent developments include: May 2022 - Datumate signed a strategic distribution agreement with Hitachi Solutions to expand digital transformation in Japan and globally. The multi-year strategic partnership agreement entails that Hitachi Solutions, Ltd. will integrate Datumate's construction analytics platform into its professional service offerings., March 2022 - Juganu, an Israeli-based LED lighting and communications solutions provider, has partnered with Qualcomm, Nokia, and Abdi to showcase its 5G and smart cities solutions. Juganu and Qualcomm have collaborated in smart city and communication ventures for several years, including in Brazil and the U.S.. Key drivers for this market are: Rising Adoption of Internet and IoT Devices, Governments Increasing Focus on Smart City. Potential restraints include: Rising Adoption of Internet and IoT Devices, Governments Increasing Focus on Smart City. Notable trends are: Smart Governance to be the Major Application Area.

In financial year 2022, the expenditure on the Smart City Mission in India amounted to around ** billion Indian rupees, and it is projected to exceed ** billion rupees in fiscal year 2023. The mission aims at urban infrastructure transformation with a focus on enhancing the quality of life for citizens through the adoption of technology, data-driven solutions, and effective urban planning.

The global Big Data in Smart Cities market size was valued at USD 30.6 billion in 2020 and is projected to reach USD 292.4 billion by 2030, growing at a CAGR of 28.9% from 2021 to 2030. The market growth is attributed to the increasing urbanization, rising demand for real-time data analytics, and government initiatives to develop smart cities. The key drivers of the market are the increasing urbanization and population growth, which is leading to a rise in demand for efficient and sustainable city management solutions. Additionally, the growing adoption of IoT devices and sensors is generating vast amounts of data that can be analyzed to improve city operations and services. Furthermore, governments worldwide are investing in smart city initiatives, which is creating a favorable environment for the adoption of Big Data solutions. Market Size: USD 40 billion in 2022, projected to grow to USD 300 billion by 2030 Growth Rate: 25-30% CAGR

The global Smart Cities market, valued at $1.36 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 23.21% from 2025 to 2033. This rapid expansion is driven by several key factors. Increasing urbanization necessitates efficient resource management and improved infrastructure, fueling demand for smart solutions across various sectors. Governments worldwide are actively investing in smart city initiatives to enhance public safety, improve healthcare services, optimize energy consumption, and create sustainable urban environments. Technological advancements, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data analytics, are further accelerating market growth by enabling the development of sophisticated smart city applications and services. The integration of these technologies allows for real-time data analysis, predictive modeling, and improved decision-making, leading to more efficient and responsive city management. Furthermore, growing awareness of environmental sustainability and the need to mitigate climate change is driving the adoption of smart city technologies aimed at reducing carbon footprints and promoting resource conservation. The Smart Cities market is segmented by solution type, with Smart Mobility Management, Smart Public Safety, Smart Healthcare, and Smart Building applications leading the growth. North America and Europe currently hold significant market share, driven by early adoption of smart city technologies and robust technological infrastructure. However, Asia Pacific is expected to witness rapid growth in the coming years due to increasing urbanization and substantial government investments in infrastructure development. While the market faces challenges such as high initial investment costs, data security concerns, and the need for interoperability between different systems, the long-term benefits of improved efficiency, enhanced public services, and sustainable development are expected to outweigh these challenges. Competitive landscape analysis reveals key players such as ABB Ltd, Cisco Systems Inc, and IBM Corporation actively shaping market dynamics through innovation and strategic partnerships. Recent developments include: June 2023: TIM, a telecom operator, and Ericsson announced an increase in their investments in private networks and IoT projects in Brazil. TIM has announced various new projects, particularly in smart cities/public lighting. In the previous month, the company announced a smart lighting project in Porto Alegre, Rio Grande do Sul state, using 4G NB-IoT technology for IPSul, the concessionaire responsible for public lighting in the city., November 2022: Emerson Electric Co. announced the completion of the USD 3-billion sale of its InSinkErator business to Whirlpool Corporation. InSinkErator was founded in 1938 and acquired by Emerson in 1968. It manufactured food waste disposers and instant hot water dispensers for home and commercial use. Whirlpool Corporation, a home appliance industry leader, is well-positioned to capitalize on InSinkErator's substantial legacy and performance to achieve long-term growth and success.. Key drivers for this market are: Rising Adoption of AI and IoT, Growth in the need for public safety and security. Potential restraints include: Rising Adoption of AI and IoT, Growth in the need for public safety and security. Notable trends are: Adoption of AI and IoT to be Major Drivers for the Market.

This data was collected by Google and Dublin City Council as part of Project Air View Dublin. Google's first electric Street View car equipped with Aclima’s mobile air sensing platform drove through the roads of Dublin City measuring street by street air quality. Driving predominantly took place Monday–Friday between 9:00 am and 5:00 pm from May 2021 through August 2022, so the dataset primarily represents typical daytime, weekday air quality. The car measured pollution on each street and highway at 1-second intervals, driving with the flow of traffic at normal speeds. The pollutants determined are: Carbon Monoxide(CO), Carbon Dioxide(CO2), Nitrogen Dioxide (NO2), NO (nitric oxide), Ozone (O3), and Particulate Matter PM2.5 (including size resolved particle counts from 0.3 - 2.5 μm). Airview_DublinCity_Measurements is the 1-second intervals data captured during the period. AirView_Dublin_City_RoadData is the 1-second data points aggregated in approximately 50m road segments. For more information about the project, methodology and maps, visit Google EIE Labs. Citing this data: Feel free to include the data in other analysis, materials, reports, and communications with the following data attribution: Aclima & Google 2022 via Dublinked

Rated Projects in S.M.A.R.T. Housing Developments in Fiscal Year 2022

Overview

The Songdo Traffic dataset delivers precisely georeferenced vehicle trajectories captured through high-altitude bird's-eye view (BeV) drone footage over Songdo International Business District, South Korea. Comprising approximately 700,000 unique trajectories, this resource represents one of the most extensive aerial traffic datasets publicly available, distinguishing itself through exceptional temporal resolution that captures vehicle movements at 29.97 points per second, enabling unprecedented granularity for advanced urban mobility analysis.

⚠️ Important: If you use this dataset in your work, please cite the following reference [1]:

Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery, arXiv preprint arXiv:2411.02136.

(Note: This manuscript shall be replaced by the published version once available.)

Dataset Composition

The dataset consists of four primary components:

Trajectory Data: 80 ZIP archives containing high-resolution vehicle trajectories with georeferenced positions, speeds and acceleration profiles, and other metadata.

Orthophoto Cut-Outs: High-resolution (8000×8000 pixel) orthophoto images for each monitored intersection, used for georeferencing and visualization.

Road and Lane Segmentations: CSV files defining lane polygons within road sections, facilitating mapping of vehicle positions to road segments and lanes.

Sample Videos: A selection of 4K UHD drone video samples capturing intersection footage during the experiment.

Data Collection

The dataset was collected as part of a collaborative multi-drone experiment conducted by KAIST and EPFL in Songdo, South Korea, from October 4–7, 2022.

A fleet of 10 drones monitored 20 busy intersections, executing advanced flight plans to optimize coverage.

4K (3840×2160) RGB video footage was recorded at 29.97 FPS from altitudes of 140–150 meters.

Each drone flew 10 sessions per day, covering peak morning and afternoon periods.

The experiment resulted in 12TB of 4K raw video data.

More details on the experimental setup and data processing pipeline are available in [1].

Data Processing

The trajectories were extracted using geo-trax, an advanced deep learning framework designed for high-altitude UAV-based traffic monitoring. This state-of-the-art pipeline integrates vehicle detection, tracking, trajectory stabilization, and georeferencing to extract high-accuracy traffic data from drone footage.

Key Processing Steps:

Vehicle Detection & Tracking: Vehicles were detected and tracked across frames using a deep learning-based detector and motion-model-based tracking algorithm.

Trajectory Stabilization: A novel track stabilization method was applied using detected vehicle bounding boxes as exclusion masks in image registration.

Georeferencing & Coordinate Transformation: Each trajectory was transformed into global (WGS84), local Cartesian, and orthophoto coordinate systems.

Vehicle Metadata Estimation: In addition to time-stamped vehicle trajectories, various metadata attributes were also extracted, including vehicle dimensions and type, speed, acceleration, class, lane number, road section, and visibility status.

More details on the extraction methodology are available in [1].

File Structure & Formats

1. Trajectory Data (Daily Intersection ZIPs, 16.2 MB ~ 360.2 MB)

The trajectory data is organized into 80 ZIP files, each containing traffic data for a specific intersection and day of the experiment.

File Naming Convention:

YYYY-MM-DD_intersectionID.zip

YYYY-MM-DD represents the date of data collection (2022-10-04 to 2022-10-07).

intersectionID is a unique identifier for one of the 20 intersections where data was collected (A, B, C, E, …, U). The letter D is reserved to denote "Drone".

Each ZIP file contains 10 CSV files, each corresponding to an individual flight session:

YYYY-MM-DD_intersectionID.zip │── YYYY-MM-DD_intersectionID_AM1.csv ├── … │── YYYY-MM-DD_intersectionID_AM5.csv │── YYYY-MM-DD_intersectionID_PM1.csv ├── … └── YYYY-MM-DD_intersectionID_PM5.csv

Here, AM1-AM5 and PM1-PM5 denote morning and afternoon flight sessions, respectively. For example, 2022-10-04_S_AM1.csv contains all extracted trajectories from the first morning session of the first day at the intersection 'S'.

CSV File Example Structure:

Each CSV file contains high-frequency trajectory data, formatted as follows (d.p. = decimal place):

Dataset Column Name Format / Units Data Type Explanation

Vehicle_ID 1, 2, … Integer Unique vehicle identifier within each CSV file

Local_Time hh:mm:ss.sss String Local Korean time (GMT+9) in ISO 8601 format

Drone_ID 1, 2, …, 10 Integer Unique identifier for the drone capturing the data

Ortho_X, Ortho_Y px (1 d.p.) Float Vehicle center coordinates in the orthophoto cut-out image

Local_X, Local_Y m (2 d.p.) Float KGD2002 / Central Belt 2010 planar coordinates (EPSG:5186)

Latitude, Longitude ° DD (7 d.p.) Float WGS84 geographic coordinates in decimal degrees (EPSG:4326)

Vehicle_Length*, Vehicle_Width* m (2 d.p.) Float Estimated physical dimensions of the vehicle

Vehicle_Class Categorical (0–3) Integer Vehicle type: 0 (car/van), 1 (bus), 2 (truck), 3 (motorcycle)

Vehicle_Speed* km/h (1 d.p.) Float Estimated speed computed from trajectory data using Gaussian smoothing

Vehicle_Acceleration* m/s² (2 d.p.) Float Estimated acceleration derived from smoothed speed values

Road_Section* N_G String Road section identifier (N = node, G = lane group)

Lane_Number* 1, 2, … Integer Lane position (1 = leftmost lane in the direction of travel)

Visibility 0/1 Boolean 1 = fully visible, 0 = partially visible in the camera frame

• These columns may be empty under certain conditions, see [1] for more details.

1. Orthophoto Cut-Outs (orthophotos.zip, 1.8 GB)

For each intersection, we provide the high-resolution orthophoto cut-outs that were used for georeferencing. These 8000×8000 pixel PNG images cover specific areas, allowing users to overlay orthophoto trajectories within the road network.

orthophotos/ │── A.png │── B.png │── … └── U.png

For more details on the orthophoto generation process, refer to [1].

1. Orthophoto Segmentations (segmentations.zip, 24.9 KB)

We provide the road and lane segmentations for each orthophoto cut-out, stored as CSV files where each row defines a lane polygon within a road section.

Each section (N_G) groups lanes moving in the same direction, with lanes numbered sequentially from the innermost outward. The CSV files are structured as follows:

segmentations/ │── A.csv │── B.csv │── … └── U.csv

Each file contains the following columns:

Section: Road section ID (N_G format).

Lane: Lane number within the section.

tlx, tly, blx, bly, brx, bry, trx, try: Polygon corner coordinates.

These segmentations enabled trajectory points to be mapped to specific lanes and sections in our trajectory dataset. Vehicles outside segmented areas (e.g., intersection centers) remain unlabeled. Perspective distortions may also cause misalignments for taller vehicles.

1. Sample Videos (sample_videos.zip, 26.8 GB)

The dataset includes 29 video samples, each capturing the first 60 seconds of drone hovering over its designated intersection during the final session (PM5) on October 7, 2022. These high-resolution 4K videos provide additional context for trajectory analysis and visualization, complementing the orthophoto cut-outs and segmentations.

sample_videos/ │── A_D1_2022-10-07_PM5_60s.mp4 │── A_D2_2022-10-07_PM5_60s.mp4 │── B_D1_2022-10-07_PM5_60s.mp4 │── … └── U_D10_2022-10-07_PM5_60s.mp4

Additional Files

README.md – Dataset documentation (this file)

LICENSE.txt – Creative Commons Attribution 4.0 License

Known Dataset Artifacts and Limitations

While this dataset is designed for high accuracy, users should be aware of the following known artifacts and limitations:

Trajectory Fragmentation: Trajectories may be fragmented for motorcycles in complex road infrastructure scenarios (pedestrian crossings, bicycle lanes, traffic signals) and for certain underrepresented truck variants. Additional fragmentations occurred when drones experienced technical issues during hovering, necessitating mid-recording splits that naturally resulted in divided trajectories.

Vehicle ID Ambiguities: The largest Vehicle_ID in a CSV file does not necessarily indicate the total number of unique vehicles.

Kinematic Estimation Limitations: Speed and acceleration values are derived from raw tracking data and may be affected by minor errors due to detection inaccuracies, stabilization artifacts, and applied interpolation and smoothing techniques.

Vehicle Dimension Estimation: Estimates may be unreliable for stationary or non-axially moving vehicles and can be affected by bounding box overestimations capturing protruding vehicle parts or shadows.

Lane and Section Assignment Inaccuracies: Perspective effects may cause vehicles with significant heights, such as trucks or buses, to be misassigned to incorrect lanes or sections in the orthophoto.

Occasional pedestrian pair misclassifications: Rarely, two pedestrians walking side by side may be briefly mistaken for a motorcycle, but such instances are short-lived and typically removed by the short trajectory filter.

For a comprehensive discussion of dataset limitations and validation procedures, refer to [1].

Citation & Attribution

Preferred Citation:

If you use Songdo Traffic for any purpose, whether in academic research, commercial applications, open-source projects, or benchmarking efforts, please cite our accompanying manuscript [1]:

Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Advanced computer vision for extracting georeferenced vehicle

The Smart Government market is experiencing robust growth, driven by increasing government initiatives to enhance citizen services, improve operational efficiency, and foster data-driven decision-making. The period from 2019 to 2024 witnessed significant market expansion, laying a solid foundation for continued growth projected through 2033. Factors such as the rising adoption of cloud computing, big data analytics, and artificial intelligence (AI) are key catalysts. Governments worldwide are increasingly investing in smart city infrastructure, including intelligent transportation systems, smart grids, and public safety solutions, all contributing to market expansion. Furthermore, the growing need for improved transparency and accountability in government operations is driving demand for smart government solutions that enhance citizen engagement and streamline processes. This trend is particularly noticeable in developed economies, but developing nations are also rapidly adopting these technologies to bridge the digital divide and improve public services. Looking ahead, the Smart Government market is poised for continued expansion, driven by sustained technological advancements and increasing government budgets allocated to digital transformation. The focus will likely shift towards more sophisticated solutions integrating various technologies to create holistic ecosystems. Cybersecurity concerns will remain paramount, necessitating robust security measures within smart government platforms. Competition among vendors offering smart city solutions will intensify, leading to increased innovation and competitive pricing. Regional variations in adoption rates will persist, with mature markets in North America and Europe leading the way, while developing economies in Asia and Africa will experience faster growth rates in the coming years. This suggests a substantial opportunity for businesses to capitalize on the evolving technological landscape and address the unique needs of different government sectors. Recent developments include: May 2022 - The Malaysian government has launched MyGovCloud, its cloud computing service, a cloud computing service that serves as an upgrade to the Public Sector Data Centre. All government agencies can use the new service. Moreover, a contract agreement was signed by the government, Cloud Service Provider (CSP), and Managed Service Provider (MSP), a local Cloud Bumiputera service provider appointed by CSP. The Malaysian Administrative Modernisation and Management Planning Unit (MAMPU), the federal agency in charge of the public sector's digitalization agenda, represented Malaysia in the agreement., March 2022 - E-government will be critical to India's infrastructure development. In the most current Union Budget unveiled on February 1, the government significantly increased the sector's funding. In the fiscal year 2022-2023, it is projected to spend more than Rs 10 lakh crore., March 2022 - According to research results published by Amazon Web Services (AWS), the epidemic has increased the need for digital skills training. According to the survey, the number of Indian workers who require digital skills for their jobs will rise by 27.3 million over the next year, accounting for 7% of the country's labor force.. Key drivers for this market are: Government Initiatives for Digital Transformation, Adoption of SMAC (Social, Mobile, Analytics, and Cloud). Potential restraints include: Government Initiatives for Digital Transformation, Adoption of SMAC (Social, Mobile, Analytics, and Cloud). Notable trends are: E-governance Services to Drive Market Growth.

The size of the Africa Geospatial Analytics market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.99% during the forecast period.Geospatial analytics is a tool where the potential of location-based data, which is fast catching up in the African continent, is pooled to utilize the integration of the geographic information systems (GIS), global positioning systems (GPS), and remote sensing technologies. These empower organizations to interpret and make value out of spatial data for analytical purposes. Geospatial analytics analyzes patterns and trends as well as their relationships within geographic contexts that will give a more holistic understanding of complex phenomena.Geospatial analytics alters most aspects of life in these areas in Africa. It is helpful in optimizing crops and resources in precision farming. Farmers learn when to make agriculture decisions through analysis of data on soil quality, weather, and crop health for maximization of its produce. In urban planning, it helps in urban development, infrastructure planning, and disaster management. Mapping the pattern of growth for cities, identification of vulnerable areas, and even the optimization of resource allocation makes cities sustainable and resilient. Geospatial analytics is also important in natural resource management, environmental conservation, and climate change adaptation. Monitoring deforestation, tracking populations of wildlife, and assessing the impact of climate will help policymakers further strategize towards a more effective implementation of conservation. Africa is continuing to step into the technological fray, and the market for geospatial analytics is supposed to grow its presence multifold.From seemingly endless savannahs, countless forests, substantive natural riches, and burgeoning cities, Africa has much to harness from the insights offered by geospatial analytics: Address real challenges, unlock fresh opportunities, and drive sustainable development across the continent. Recent developments include: November 2022: A Memorandum of Understanding (MOU) was signed by SaskTel and Axiom Exploration Group to jointly explore opportunities to assist organizations throughout Saskatchewan in enhancing and modernizing their operations through the gathering and analysis of geospatial and other geophysical data., September 2022: A two-day conference on Data Analytics and visualization was held by Women in GIS Kenya in association with Pathways International, Esri Eastern Africa, Nakala Analytics, and the University of Nairobi, Department of Geospatial and Space Technology.. Key drivers for this market are: Commercialization of spatial data, Increased smart city & infrastructure projects. Potential restraints include: High costs associated with geospatial technologies. Notable trends are: Commercialization of Spatial Data.

This is a smart city domain dataset, and more specifically a environmental one generated within the framework of the SmartSantander research testbed.

It contains raw temperature measurements reported by SmartSantander sensors deployed in the spanish city of Santander, covering a period of 17 months between January 1st 2021 and July 31st 2022, and comprising more than 24 million data points. The dataset includes not only the temperature dimension but also spatial and temporal information, as well as the specific device identifier and some labels to differentiate between static/mobile and indoor/outdoor devices.

As is common with large-scale sensor deployments, there are occasional sensor malfunctions, which have deliberately not been filtered out of this raw dataset.

The Smart Grid Data Analytics market is experiencing robust growth, projected to reach a significant market size driven by the increasing need for efficient and reliable energy distribution. A Compound Annual Growth Rate (CAGR) of 12.76% from 2019-2033 indicates a substantial expansion, fueled by several key factors. The rising adoption of smart meters, coupled with advancements in data analytics technologies, allows utilities to gain valuable insights into energy consumption patterns. This, in turn, enables proactive grid management, optimized resource allocation, and improved demand-side management strategies such as demand response programs. Furthermore, regulatory mandates pushing for grid modernization and the integration of renewable energy sources are contributing to market growth. The cloud-based deployment model is gaining traction due to its scalability and cost-effectiveness, while solutions focusing on transmission and distribution network optimization, advanced metering infrastructure (AMI) analysis, and customer analytics are leading the market segments. Key players like Siemens, Itron, and General Electric are investing heavily in research and development to enhance their offerings and gain a competitive edge. While data security concerns and the high initial investment costs represent potential restraints, the long-term benefits of improved grid efficiency and reduced operational costs are driving widespread adoption. The geographical distribution of the market reveals strong growth across North America and Europe, driven by mature infrastructure and high levels of technological adoption. However, the Asia-Pacific region is poised for significant expansion, fueled by rapid urbanization and increasing electricity demand. The public sector remains a major end-user, owing to government initiatives promoting smart grid deployments. However, growing participation from the private sector, particularly large enterprises and SMEs, reflects the increasing awareness of the commercial advantages of data-driven grid management. Looking ahead, the integration of artificial intelligence and machine learning into smart grid analytics is expected to further enhance grid stability, improve energy efficiency, and unlock new revenue streams for utilities. The market is expected to witness consolidation through mergers and acquisitions, with larger companies acquiring smaller specialized firms to expand their product portfolios and market reach. Recent developments include: November 2022: Siemens Smart Infrastructure partnered with SEW, a prominent cloud platform provider that specializes in digital customer experiences and workforce experiences for utility providers, to support utilities globally, improve the customer and workforce experiences for utility smart meter users, and facilitate the transition to a world powered entirely by renewable energy sources. The companies say that this move could lead to a long-term partnership that will help move forward the new platform paradigm in energy and utilities and speed up the digital transformation process., September 2022: The R&D Center of Dubai Electricity and Water Authority (DEWA) is evaluating its Smart Grid Analytics project, which utilizes voltage and current data from critical substations to identify and anticipate medium-voltage (MV) disruptions. The R&D Center will also assess the "dInsight" Analytics Platform, which offers complete visual analytics of the grid, loads, and supplies., July 2022: Siemens Smart Infrastructure partnered with Esri, a geographic information systems (GIS) and location intelligence platform, to broaden its ecosystem of partners for its grid software business. In this partnership, Esri's mapping and spatial analytics tools and Siemens' knowledge of electrical topology will be used to help grid operators build, run, and fix power networks better.. Key drivers for this market are: Growing Investments in Smart Grid Projects, Enormous Influx of Data. Potential restraints include: Growing Investments in Smart Grid Projects, Enormous Influx of Data. Notable trends are: Growing Investments in Smart Grid Projects.