Cloud-Based Mapping Service Market Outlook

The global cloud-based mapping service market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 8.9 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 11.2% during the forecast period. This remarkable growth is primarily driven by the increasing demand for real-time data access and navigation services across various sectors. Businesses and governments worldwide are increasingly leveraging cloud-based mapping services to optimize operations, improve customer experience, and enhance decision-making processes. The seamless integration of advanced technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) in mapping services is further boosting this market's expansion.

The integration of AI with cloud-based mapping services is one of the key growth factors for this market. AI technologies enhance the capabilities of cloud-based mapping services by providing intelligent insights and predictive analytics. For instance, AI can analyze traffic patterns and predict congestion, offering alternative routes and optimal travel paths. This is particularly beneficial for the transportation and logistics sectors, where time is of the essence. Furthermore, AI-driven mapping services can assist businesses in understanding consumer behavior and preferences, allowing for targeted marketing strategies and improved customer engagement. The ability of AI to process massive datasets quickly and accurately makes it a valuable tool in the cloud-based mapping service industry.

Another significant factor contributing to market growth is the rising adoption of IoT devices. IoT devices generate a vast amount of location-based data that can be effectively managed and utilized through cloud-based mapping services. These services enable businesses to track and monitor assets, vehicles, and personnel in real-time, leading to improved operational efficiency and reduced costs. For example, in the logistics sector, companies can use cloud-based mapping services to optimize delivery routes and monitor vehicle conditions, thereby minimizing fuel consumption and enhancing customer satisfaction. The continuous evolution and proliferation of IoT devices are expected to drive further demand for cloud-based mapping services in the coming years.

The increasing reliance on mobile devices and the proliferation of high-speed internet connectivity are also significant growth drivers for the cloud-based mapping service market. With the widespread use of smartphones and tablets, consumers and businesses alike are accessing mapping services on-the-go, necessitating reliable cloud-based solutions. The availability of high-speed internet ensures seamless connectivity and real-time updates, enhancing user experience. This trend is particularly prominent in urban areas, where demand for navigation and location-based services is high. As mobile technology continues to evolve and internet infrastructure improves worldwide, the cloud-based mapping service market is poised for substantial growth.

The rise of URL Shortening Services has become increasingly relevant in the context of cloud-based mapping services. These services allow users to condense lengthy URLs into shorter, more manageable links, which is particularly useful for sharing location-based information. In industries such as logistics and transportation, where quick access to precise location data is crucial, URL shortening can streamline communication and improve efficiency. By integrating URL shortening with mapping services, businesses can enhance their digital marketing strategies and facilitate easier sharing of maps and navigation routes. This integration not only improves user experience but also supports the growing demand for seamless digital interactions in the mapping service market.

Service Type Analysis

The cloud-based mapping service market is segmented into several service types, each offering unique features and benefits to users. Mapping and navigation services are perhaps the most widely recognized and utilized among these. They provide users with detailed maps, directions, and navigation assistance, which are crucial for both consumers and businesses. These services cater to a wide array of applications, from personal navigation to complex logistics operations. As the demand for precise, real-time navigation grows, mapping and navigation services continue to be at the forefront of the cloud-based mapping industry. Their integrat

Work in progress: data might be changed The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies. Vehicle Detections Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock. The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides. Figure 1: Overview map of detected vehicles Parking Areas

The i.c.sens Visual-Inertial-LiDAR Dataset is a data set for the evaluation of dead reckoning or SLAM approaches in the context of mobile robotics. It consists of street-level monocular RGB camera images, a front-facing 180° point cloud, angular velocities, accelerations and an accurate ground truth trajectory. In total, we provide around 77 GB of data resulting from a 15 minutes drive, which is split into 8 rosbags of 2 minutes (10 GB) each. Besides, the intrinsic camera parameters and the extrinsic transformations between all sensor coordinate systems are given. Details on the data and its usage can be found in the provided documentation file. Image credit: Sören Vogel The data set was acquired in the context of the measurement campaign described in Schoen2018. Here, a vehicle, which can be seen below, was equipped with a self-developed sensor platform and a commercially available Riegl VMX-250 Mobile Mapping System. This Mobile Mapping System consists of two laser scanners, a camera system and a localization unit containing a highly accurate GNSS/IMU system. Image credit: Sören Vogel The data acquisition took place in May 2019 during a sunny day in the Nordstadt of Hannover (coordinates: 52.388598, 9.716389). The route we took can be seen below. This route was completed three times in total, which amounts to a total driving time of 15 minutes. The self-developed sensor platform consists of several sensors. This dataset provides data from the following sensors: Velodyne HDL-64 LiDAR LORD MicroStrain 3DM-GQ4-45 GNSS aided IMU Pointgrey GS3-U3-23S6C-C RGB camera To inspect the data, first start a rosmaster and launch rviz using the provided configuration file: roscore & rosrun rviz rviz -d icsens_data.rviz

Description Cellphone tower extract is a copy of the data found at https://ised-isde.canada.ca/site/spectrum-management-system/en/spectrum-management-system-data. Data is organized into a point layer of tower locations grouped by provider. All the providers transmitters are located in a table related by a unique TowerID.Dataset Usage General data layer for use when needed, for example to identify shortfalls in cell service for field work.Data Source Modified version of Innovation, Science and Economic Development Canada datasetData Criticality 1Sensitive Data NoCurator Greg SpiridonovCurator Job Title GIS SpecialistCurator Email greg.spiridonov@grey.caCurator Department IT / GISCurator Responsibilities Maintain,Oversight_Control_AccessMaintenance and Update Frequency MonthlyUpdate History New ZIP downloaded Nov 14 2023Published Map Service(s) https://gis.grey.ca/portal/home/item.html?id=718f08a731924856827f85178bb649cbPublicly Available Publicly availableOpen Data Published to Open DataOffline (sync) Not sure at this timeOther Comments Dataset relates to table GC_CellTower_TransmittersPermissionsAssign permissions to map service if published.Group PermissionsCurator Department ViewGrey County Staff ViewPublic View

These data were automated to provide an accurate high-resolution composite shoreline of GRAND BAY TO PENSACOLA MOBILE BAY, AL suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies. This metadata describes information for both the line and point shapefiles...

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A real-world multi-vehicle multi-modal V2V and V2X dataset Recently published datasets have been increasingly comprehensive with respect to their variety of simultaneously used sensors, traffic scenarios, environmental conditions, and provided annotations. However, these datasets typically only consider data collected by one independent vehicle. Hence, there is currently a lack of comprehensive, real-world, multi-vehicle datasets fostering research on cooperative applications such as object detection, urban navigation, or multi-agent SLAM. In this paper, we aim to fill this gap by introducing the novel LUCOOP dataset, which provides time-synchronized multi-modal data collected by three interacting measurement vehicles. The driving scenario corresponds to a follow-up setup of multiple rounds in an inner city triangular trajectory. Each vehicle was equipped with a broad sensor suite including at least one LiDAR sensor, one GNSS antenna, and up to three IMUs. Additionally, Ultra-Wide-Band (UWB) sensors were mounted on each vehicle, as well as statically placed along the trajectory enabling both V2V and V2X range measurements. Furthermore, a part of the trajectory was monitored by a total station resulting in a highly accurate reference trajectory. The LUCOOP dataset also includes a precise, dense 3D map point cloud, acquired simultaneously by a mobile mapping system, as well as an LOD2 city model of the measurement area. We provide sensor measurements in a multi-vehicle setup for a trajectory of more than 4 km and a time interval of more than 26 minutes, respectively. Overall, our dataset includes more than 54,000 LiDAR frames, approximately 700,000 IMU measurements, and more than 2.5 hours of 10 Hz GNSS raw measurements along with 1 Hz data from a reference station. Furthermore, we provide more than 6,000 total station measurements over a trajectory of more than 1 km and 1,874 V2V and 267 V2X UWB measurements. Additionally, we offer 3D bounding box annotations for evaluating object detection approaches, as well as highly accurate ground truth poses for each vehicle throughout the measurement campaign. Data access Important: Before downloading and using the data, please check the Updates.zip in the "Data and Resources" section at the bottom of this web site. There, you find updated files and annotations as well as update notes. The dataset is available here. Additional information are provided and constantly updated in our README. The corresponding paper is available here. Cite this as: J. Axmann et al., "LUCOOP: Leibniz University Cooperative Perception and Urban Navigation Dataset," 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA, 2023, pp. 1-8, doi: 10.1109/IV55152.2023.10186693.

The following data set contains service request activity for Miami-Dade County. The data sets include services completed proactively by Miami-Dade County departments and requests submitted by citizens via phone (311), online (miamidade.gov), and other self service channels such as the 311Direct mobile application. With a few exceptions, the dataset does not generally include requests from other cities (City of Miami, Coral Gables, etc.) unless the work is owned by Miami-Dade County staff. Case Owner is (match ANY condition): Animal_Services or Citations_and_Tags or Enforcement_Section-3-36

Geographic information layer available at 1/100 000th produced in the context of the development of the Regional Ecological Coherence Scheme (CESR) 2014, which locates the boundaries of potentially mobile stream portions.

Grade PT2 easements concern easements for the protection of radio transmission and reception centres against obstacles. They are established pursuant to Articles L. 54 to L.56-1 of the Postal and Electronic Communications Code in order to protect radio centres from physical obstacles that may impede the spread of waves. Two schemes should be distinguished: — easements established for the benefit of radio centres concerning national defence or public security (Articles L.54 to L.56 of the Postal and Electronic Communications Code); — easements for radio centres owned by private operators (Article L.56-1 of the Postal and Electronic Communications Code). However, in the absence of a decree implementing Article L.62-1 of the Postal and Electronic Communications Code, operators of electronic communications networks open to the public cannot benefit from radio easements to date. A plan for the establishment of easements approved by decree sets out the areas that are subject to easement. Four types of zone can be created: — primary clearance zones and/or secondary clearance zones around each radio station emitting or receiving radio waves using directional aerials, as well as around radio laboratories and research centres; — special clearance zones between two centres providing a radio wave link greater than 30 megahertz (i.e. with a wavelength of less than 10 metres); — areas of clearance around radio-tracking stations or radionavigation stations of transmission or reception. The consequence of servitude is: — the obligation, in all those areas, for owners to carry out, if necessary, the removal or modification of buildings constituting buildings by nature pursuant to Articles 518 and 519 of the Civil Code. In the absence of an amicable agreement, the administration may expropriate these properties; — the prohibition, in all these areas, of creating fixed or mobile obstacles the highest part of which exceeds the ratings fixed by the easement order without the authorisation of the minister who operates or controls the centre; — prohibition, in the primary clearance zone: * an aeronautical safety station or radiogoniometric centre, to create or retain any fixed or mobile metalwork, bodies of water or liquids of any kind that may interfere with the operation of that facility or station; * an aeronautical safety station, to create or maintain artificial excavations that may interfere with the operation of that station. * the prohibition, in the special clearance zone, of creating structures or obstacles siuted above a straight line 10 metres below that joining the emission and receiving airplanes, but the height limitation imposed on a construction may not be less than 25 metres. This resource describes the surface bases of the PT2 class easements, i.e. the areas (primary, secondary, special) and clearance areas Source: —NR— Vintage: —NR— Dissemination: Restricted

This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Coastal Services Center's Sea Level Rise and Coastal Flooding Impacts Viewer (www.csc.noaa.gov/slr/viewer). This metadata record describes the DEM for Mobile County in Alabama and Escambia, Santa Rosa, and Okaloosa (southern coastal portion only) Counties in Florida. The DEM includes the best available lidar data known to exist at the time of DEM creation for the coastal areas of Mobile County in Alabama and Escambia, Santa Rosa, and Okaloosa (portion) counties in Florida, that met project specification.This DEM is derived from the USGS National Elevation Dataset (NED), US Army Corps of Engineers (USACE) LiDAR data, as well as LiDAR collected for the Northwest Florida Water Management District (NWFWMD) and the Florida Department of Emergency Management (FDEM). NED and USACE data were used only in Mobile County, AL. NWFWMD or FDEM data were used in all other areas. Hydrographic breaklines used in the creation of the DEM were obtained from FDEM and Southwest Florida Water Management District (SWFWMD). This DEM is hydro flattened such that water elevations are less than or equal to 0 meters.This DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 5 meters. This DEM does not include licensed data (Baldwin County, Alabama) that is unavailable for distribution to the general public. As such, the extent of this DEM is different than that of the DEM used by the NOAA Coastal Services Center in creating the inundation data seen in the Sea Level Rise and Coastal Impacts Viewer (www.csc.noaa.gov/slr/viewer).The NOAA Coastal Services Center has developed high-resolution digital elevation models (DEMs) for use in the Center's Sea Level Rise And Coastal Flooding Impacts internet mapping application. These DEMs serve as source datasets used to derive data to visualize the impacts of inundation resulting from sea level rise along the coastal United States and its territories.The dataset is provided "as is," without warranty to its performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of this dataset is assumed by the user. This dataset should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

Vegetation structure metrics of 127 LA plots, derived from mobile lidar scans (GeoSLAM ZEB Horizon). Metrics cover information on structural complexity, vegetation height, canopy surface, vertical layering, vegetation cover and gaps.

description: Various telecommunication datasets such as cellphone towers and service areas, land mobile station locations, AM, FM, and TV communication, extracted from the FCC Licensing Database, can be individually downloaded from the FCC GIS data site. Addiitonally, a full dataset download of all GIS files is packaged with an ArcExplorer(R) viewing capability for users who do not have full GIS capability.; abstract: Various telecommunication datasets such as cellphone towers and service areas, land mobile station locations, AM, FM, and TV communication, extracted from the FCC Licensing Database, can be individually downloaded from the FCC GIS data site. Addiitonally, a full dataset download of all GIS files is packaged with an ArcExplorer(R) viewing capability for users who do not have full GIS capability.

Mobile air quality mapping has been performed for black carbon (BC), ultrafine particles (UFP) and particulate matter (PM2.5) to assess the spatial pollutant variability in Warsaw, evaluate source impacts and improve commuter exposure assessment. The measurement set-up is based on the mobile monitoring approaches developed within RI-URBANS. Voluntary participants performed bicycle measurements with portable equipment along a dedicated sampling route during morning (8-9h) and evening (17-18h) rush hours. The monitoring campaigns were performed during the warm (40 runs between September 6th-October 9th, 2024) and cold (42 runs between January 22nd - March 5th, 2025) season.Prior to the mobile campaigns, the portable instruments (Aethlabs AE51, Naneos Partector 2.0, OpenSeneca) were co-located with high-end instruments at the WOS site for UFP and BC and the local AQMS for PM. GPS localization was provided by a Garmin Forerunner 55 watch.This dataset includes the raw high-resolution (1-second) data of the portable instruments collected during the mobile monitoring campaigns and merged to seasonal (summer/winter) datafiles based on datetime stamps. This data is currently further processed to construct high-resolution concentration maps as described in the RI-URBANS service tool for urban mapping (ST13).

PT2 class easements concern protection easements of radio transmission and reception centres against obstacles They are instituted pursuant to Articles L. 54 to L.56-1 of the Postal and Electronic Communications Code in order to protect radio centres against physical obstacles that may hinder the spread of waves. Two schemes should be distinguished: — servitudes instituted for the benefit of radio centres concerning national defence or public security (Articles L.54 to L.56 of the Postal and Electronic Communications Code); — easements established for the benefit of radio centres owned by private operators (Article L.56-1 of the Postal and Electronic Communications Code). However, in the absence of a decree implementing Article L.62-1 of the Postal and Electronic Communications Code, operators of electronic communications networks open to the public cannot benefit from radio easements to date. A plan for the establishment of servitudes approved by decree sets out the areas that are subject to servitude. Four types of zone can be created: — primary clearance zones and/or secondary clearance zones around each radio wave transmitting or receiving station using direct air, as well as around radio laboratories and research centres; — special clearance zones between two centres providing a frequency wave radio connection greater than 30 megahertz (i.e. with a wavelength of less than 10 metres); — clearance areas around radio-tracking or radionavigation stations of emission or reception. Servitude has the consequence of: — the obligation, in all those areas, for the owners, if necessary, to remove or modify buildings constituting buildings by nature pursuant to Articles 518 and 519 of the Civil Code. In the absence of an amicable agreement, the administration may expropriate these buildings; — the prohibition, in all these zones, of creating fixed or mobile obstacles, the highest part of which exceeds the ratings fixed by the servitude order without authorisation of the minister operating or controlling the centre; — the prohibition in the primary clearance zone: * an aeronautical safety station or a radiogoniometric centre, to create or retain any fixed or mobile metal work, bodies of water or liquids of any kind that may interfere with the operation of that installation or station; * from an aeronautical safety station, to create or maintain artificial excavations that may interfere with the operation of that station. * the prohibition, in the special clearance zone, of creating structures or obstacles located above a straight line 10 metres below that of the emission and reception airs, but the height limitation imposed on a construction may not be less than 25 metres. This resource describes the surface plates of PT2 easements, i.e. areas (primary, secondary, special) and clearance areas

This data aims to analyze the intellectual structure of mobile money studies by examining bibliographic characteristics. A dataset of 165 documents from the Scopus database was used. This study explored various aspects, including annual publication counts, country coupling, source numbers, primary research areas, co-occurrence of keywords, bibliographic linkages between sources and documents, and co-citation patterns of references. Bibliographic network mapping techniques were applied to analyze the data. The analysis was performed using VOSviewer, a scientific mapping tool. The results showed four main themes of the mobile money dataset: mobile money in Africa, financial inclusion, electronic money, and digital financial services.

This data set contains three-dimensional mobile lidar elevation data for seven villages in American Samoa on the island of Tutuila. The seven villages are: Fagaalu, Fagotogo, Pago Pago, Vatia, Leone, Amanave, and Poloa. The data were collected by Sanborn Map Company on October 27 - 30, 2010

Partners in this effort were the NOAA Pacific Services Center, the American Samoa Department of Commerce...

These data were automated to provide an accurate high-resolution historical shoreline of Mobile Bay, Alabama suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.

This database presents the results of the MOVE Project Survey (Work Package 4) that has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 649263. The consortium of MOVE comprises nine partners in six countries: Luxembourg, Germany, Hungary, Norway, Romania, and Spain. The central aim of MOVE is to provide evidence-based knowledge on mobility of young people in Europe as a prerequisite to improve mobility conditions, and to identify fostering and hindering factors of “beneficial” mobility. This aim is pursued using a multilevel interdisciplinary research approach, aiming at a comprehensive and systematic analysis of the mobility of young people in Europe. Objectives of the Survey: –To find out about the role and value of information and support services for young people and their decision making process to go abroad. –To explore the role of transnational networks for support and as a potential “pull factor” for mobility. –To examine the agency of young people with mobility experience and without it. –To study the formation of social capital and the dimensions of social inequality of mobile young people and their effects on future perspectives as well as the reproduction of social inequalities. –To carry out research on the formation of identity by those mobile young people compared to non- mobile ones. –To examine the career-plans of young people and their personal attachments related to their commitments in their home country (e.g. sending money home, supporting the family, etc.) –To gain insights into the (re)production of social inequality concerning mobility and non- mobility.