This product contains file names and URLs to files containing antenna phase map data used in the real-time GPS POD processing. In particular, the IGS ANTEX file name used for the processing is provided. Additional meta data items may include information that identifies the real-time filter source populating the GPS real-time POD products (see "pos", "quat", and "tdp" products). The product is generated at JPL's Global Differential GPS Operations Centers.

The global Navigation Electronic Map market is poised for substantial expansion, projected to reach an impressive USD 3,615 million by 2025, demonstrating a robust Compound Annual Growth Rate (CAGR) of 26.6% throughout the forecast period of 2025-2033. This dynamic growth is fueled by a confluence of escalating demand from both personal and commercial sectors, driven by the pervasive adoption of smartphones, in-car navigation systems, and the burgeoning interest in location-based services. The increasing sophistication of mapping technologies, encompassing the transition from 2D to more immersive and detailed 3D navigation electronic maps, is a significant evolutionary trend. Furthermore, the integration of AI and machine learning for real-time traffic updates, personalized routing, and enhanced predictive analytics is further propelling market penetration. The military application segment, while smaller, also contributes to market expansion through its reliance on precise and reliable geospatial data for strategic operations and intelligence gathering. The market's trajectory is not without its challenges. Restraints such as data privacy concerns, the high cost of developing and maintaining accurate and comprehensive map data, and the need for robust cybersecurity measures to protect sensitive location information could temper the growth. However, these are being actively addressed through advancements in data anonymization techniques, collaborative mapping initiatives, and the development of more efficient data collection and processing methodologies. The competitive landscape is characterized by the presence of established technology giants alongside specialized mapping companies, all vying for market share through innovation and strategic partnerships. The widespread availability of open-source mapping platforms and increasing investments in R&D are expected to foster a more dynamic and competitive environment, ultimately benefiting end-users with more advanced and accessible navigation solutions across diverse applications and regions. This in-depth report delves into the dynamic global Navigation Electronic Map market, offering a comprehensive analysis of its trajectory from the historical period of 2019-2024 through to the forecast period of 2025-2033, with a base year of 2025. The report leverages extensive research to provide actionable insights, market sizing in millions, and strategic recommendations. It meticulously examines key market drivers, challenges, emerging trends, and leading players, equipping stakeholders with the knowledge to navigate this evolving landscape. The report’s detailed segmentation and forward-looking analysis are designed to assist businesses in identifying growth opportunities and formulating robust strategies within the multi-billion dollar navigation electronic map industry.

This product contains file names and URLs to files containing antenna phase map data used in the real-time Galileo POD processing. In particular, the IGS ANTEX file name used for the processing is provided. Additional meta data items may include information that identifies the real-time filter source populating the Galileo real-time POD products (see "pos", "quat", and "tdp" products). The product is generated at JPL's Global Differential GPS Operations Centers.

Introduction

Indonesia, located near the magnetic equator in Southeast and East Asia, is essential for studying ionospheric phenomena, particularly equatorial plasma bubbles (EPBs). The Indonesian Geospatial Information Agency (BIG) has deployed a network of Global Navigation Satellite System (GNSS) receivers as part of the Indonesia Continuously Operating Reference Stations (Ina-CORS) across this country. This network has enabled the creation of detailed ionospheric irregularities maps based on the Rate of Total Electron Content (TEC) Change Index (ROTI). These maps are crucial for understanding EPBs in Southeast and East Asia.

GNSS network and ROTI map generation

The Ina-CORS consists of over 300 GNSS receivers strategically placed across Indonesia (see attached figure "geographic map_receivers.jpg"), spanning from 95°E to 140°E and from 5°N to 10°S. These receivers continuously gather GNSS observable data with a time resolution of 30 seconds in a Receiver Independent Exchange (RINEX) file. This data is then processed to generate the ROTI maps from the magnetic equator to the southern low-latitude region in Southeast/East Asia.

The GNSS data in the RINEX file is processed to calculate the Total Electron Content (TEC) using the open software developed by Seemala (2023). The software can be found at https://seemala.blogspot.com/2020/12/gps-tec-program-version-3-for-rinex-3.html. ROTI is derived by measuring the standard deviation of the rate of change of TEC over a 5-minute interval (Pi et al., 1997). This index is a critical indicator of ionospheric irregularities with kilometers of spatial scales inside the EPBs.
Using ROTI data plotted at the Ionospheric Pierce Point (IPP) altitude of 350 km, 2-dimensional (2D) latitude-longitude ROTI maps are generated. The grid size of the ROTI map is 0.25° × 0.25°. The ROTI map is smoothed by a boxcar average of 5 × 5 grid data regarding geographic latitude and longitude. In the map, sunset and sunrise terminators at altitudes of 110 km (red curve), 350 km (green curve), and 650 km (black curve) are plotted. The ROTI map is generated at each interval of 10 minutes from 9:00 to 23:50 UT. The name file of the zipped map in one day indicates the year and day of the year. For example, s_2024122_map.rar indicates the maps on day 122 in 2024.

Purpose

Sharing GNSS data in RINEX files from the CORS could be strictly limited. Sharing the ROTI map derived from the CORS of Southeast Asian countries can be an alternative solution. This database aims to store the ROTI maps over Indonesia derived from GNSS data of the Ina-CORS network. The ROTI map database is also freely accessible and can be used for educational and scientific purposes. It is an academic/scientific resource and promotes a deeper understanding of EPB phenomena and their impact on navigation and communication systems. This database enables continuous monitoring and analysis of ionospheric conditions, particularly EPB occurrence. The database supports scientific research, enhances GNSS applications, and contributes to space weather forecasting by providing a high-resolution ROTI map. This ROTI map database has been developed to encourage research collaboration between researchers globally and in Indonesia.

Attribution

Users must appropriately credit the data source in this database in any publications, presentations, or products derived from it. When using the ROTI maps in this database, please cite the database. Users are also encouraged to collaborate with the ionospheric researchers in Indonesia. If the users need the numeric data for the ROTI maps, please get in touch with the email correspondence for this database.

References

Gopi K. Seemala (2023), Chapter 4 - Estimation of ionospheric total electron content (TEC) from GNSS observations, Editor(s): A.K. Singh, S. Tiwari, In Earth Observation, Atmospheric Remote Sensing, Elsevier, 2023, Pages 63 - 84, doi.org/10.1016/B978-0-323-99262-6.00022-5.

Pi, X., Mannucci, A. J., Lindqwister, U. J., and Ho, C. M. (1997), Monitoring of global ionospheric irregularities using the worldwide GPS network, Geophys. Res. Lett., 24, 2283–2286.

More Information
Feel free to reach out via email for more information. Your feedback is invaluable to us, and we encourage users to share their experiences and suggestions for research ideas and further improvements.
Correspondence: P. Abadi, Dr.; Researcher at Research Center for Climate and Atmosphere, BRIN; email: pray001[at]brin.go.id (replace "[at]" with "@").

The China location-based services (LBS) market is experiencing robust growth, projected to reach a substantial market size. Driven by the rapid expansion of smartphone penetration, increasing adoption of IoT devices, and the burgeoning digital economy, the market is poised for significant expansion. Key application areas such as mapping and navigation, business intelligence and analytics, and location-based advertising are fueling this growth. Furthermore, the integration of LBS into various sectors, including transportation and logistics, IT and telecom, and healthcare, is creating new opportunities. The government's investment in digital infrastructure and supportive policies further strengthens the market's trajectory. Competition is fierce, with both domestic and international players vying for market share. Companies like Alibaba Cloud (Alibaba Amap), Tencent, Baidu, and international firms like TomTom and Here Technologies are actively engaged in developing innovative LBS solutions. While data privacy concerns and regulatory hurdles present potential restraints, the overall market outlook remains positive. The continued advancement of technologies like 5G, AI, and big data analytics is expected to significantly impact the market's future. These technologies are enabling the development of more accurate, efficient, and personalized LBS applications. The growing demand for real-time location data across various industries is another crucial factor driving growth. The increasing adoption of LBS in emerging sectors such as smart cities and autonomous vehicles is also anticipated to contribute significantly to the market's expansion over the forecast period. Despite some potential challenges, the positive market dynamics suggest a sustained period of growth for the China LBS market, outpacing global averages. The focus on developing robust and secure LBS technologies while adhering to stringent data privacy regulations will be crucial for sustained growth in the coming years. Recent developments include: August 2023: The Chinese government revealed the "2023 edition of the standard map of China," confirming its territorial claims over disputed regions. Following the release of the standard map for public use, the Ministry of Natural Resources was also expected to release digital maps, navigation, and positioning for use in various fields, such as location-based services, platform economy, precision agriculture, and intelligent connected vehicles., December 2022: According to the Beijing Institute of Space Science and Technology Information, China's BeiDou navigation satellite system (BDS) became one of the key guidance service providers for domestic Gaode Map. The company outpaced GPS in becoming a navigation service provider. Gaode Map used BeiDou satellites to make more than 210 billion positioning calls daily. The combination of BDS and map navigation provides better public services.. Key drivers for this market are: Technological Advancements and Supportive Government Initiatives, Increasing Importance of Location Analytics. Potential restraints include: Technological Advancements and Supportive Government Initiatives, Increasing Importance of Location Analytics. Notable trends are: Rising Adoption of Smartphones.

PDF Map of FCC Form 477 provider reported maximum download speeds by census block for January - June 2020. This map seeks to highlight areas that are undeserved by terrestrial broadband (fiber/cable/dsl on the ground), with "underserved" defined as down/up speeds less than 25/3 Mbps.These data represent a static snapshot of provider reported coverage between January 2020 and June 2020. Maps also depict the locations of federally recognized tribes, Alaskan communities, ANCSA and borough boundaries.Broadband coverage is represented using provider reported speeds under the FCC Form 477 the amalgamated broadband speed measurement category based on Form 477 "All Terrestrial Broadband" as a proxy for coverage. This field is unique to the NBAM platform. These maps do not include satellite internet coverage (and may not include microwave coverage through the TERRA network for all connected areas).This map was produced by DCRA using data provided by NTIA through the NBAM platform as part of a joint data sharing agreement undertaken in the year 2021. Maps were produced using the feature layer "NBAM Data by Census Geography v4": https://maps.ntia.gov/arcgis/home/item.html?id=8068e420210542ba8d2b02c1c971fb20Coverage is symbolized using the following legend:No data avalible or no terrestrial coverage: Grey or transparent< 10 Mbps Maximum Reported Download: Red10-25 Mbps Maximum Reported Download: Orange25-50 Mbps Maximum Reported Download: Yellow50-100 Mbps Maximum Reported Download: Light Blue100-1000 Mbps Maximum Reported Download: Dark Blue_Description from layer "NBAM Data by Census Geography v4":This layer is a composite of seven sublayers with adjacent scale ranges: States, Counties, Census Tracts, Census Block Groups, Census Blocks, 100m Hexbins and 500m Hexbins. Each type of geometry contains demographic and internet usage data taken from the following sources: US Census Bureau 2010 Census data (2010) USDA Non-Rural Areas (2013) FCC Form 477 Fixed Broadband Deployment Data (Jan - Jun 2020) Ookla Consumer-Initiated Fixed Wi-Fi Speed Test Results (Jan - Jun 2020) FCC Population, Housing Unit, and Household Estimates (2019). Note that these are derived from Census and other data. BroadbandNow Average Minimum Terrestrial Broadband Plan Prices (2020) M-Lab (Jan - Jun 2020)Some data values are unique to the NBAM platform: US Census and USDA Rurality values. For units larger than blocks, block count (urban/rural) was used to determine this. Some tracts and block groups have an equal number of urban and rural blocks—so a new coded value was introduced: S (split). All blocks are either U or R, while tracts and block groups can be U, R, or S. Amalgamated broadband speed measurement categories based on Form 477. These include: 99: All Terrestrial Broadband Plus Satellite 98: All Terrestrial Broadband 97: Cable Modem 96: DSL 95: All Other (Electric Power Line, Other Copper Wireline, Other) Computed differences between FCC Form 477 and Ookla values for each area. These are reflected by six fields containing the difference of maximum, median, and minimum upload and download speed values.The FCC Speed Values method is applied to all speeds from all data sources within the custom-configured Omnibus service pop-up. This includes: Geography: State, County, Tract, Block Group, Block, Hex Bins geographies Data source: all data within the Omnibus, i.e. FCC, Ookla, M-Lab Representation: comparison tables and single speed values

Lane-Level Mapmaking Services Market Outlook

According to our latest research, the global lane-level mapmaking services market size reached USD 3.2 billion in 2024, reflecting robust expansion due to the surge in advanced mobility solutions. The market is projected to grow at a CAGR of 15.8% from 2025 to 2033, with a forecasted market size of USD 12.7 billion by 2033. This dynamic growth is primarily driven by the increasing integration of autonomous vehicles and advanced driver-assistance systems (ADAS) across the automotive and smart infrastructure sectors, which demand highly detailed and real-time mapping solutions for precise navigation and safety.

The primary growth factor fueling the lane-level mapmaking services market is the rapid advancement and deployment of autonomous vehicles worldwide. As automotive manufacturers intensify their efforts to commercialize self-driving cars, the need for high-definition, real-time, and highly accurate lane-level maps has become paramount. These sophisticated maps enable vehicles to interpret road environments, recognize lane boundaries, and make critical driving decisions. The proliferation of electric vehicles and the adoption of connected vehicle technologies further amplify the demand for continuous map updates and validation services, ensuring the reliability and safety of autonomous mobility solutions. Additionally, regulatory bodies across major economies are mandating stricter safety and navigation protocols, compelling OEMs and technology providers to invest heavily in state-of-the-art lane-level mapping platforms.

Another significant driver is the growing implementation of advanced driver-assistance systems (ADAS) in both premium and mass-market vehicles. ADAS requires granular map data to support features such as adaptive cruise control, lane keeping, and automatic emergency braking. The evolution of smart cities and the integration of intelligent transportation systems are also catalyzing market growth, as urban planners and municipalities seek to enhance traffic management, reduce congestion, and improve road safety through digital infrastructure. The rise of fleet management solutions, particularly in logistics and transportation, is further propelling the adoption of real-time mapping services, enabling operators to optimize routes, monitor vehicle performance, and ensure compliance with regulatory requirements.

The lane-level mapmaking services market is also benefitting from technological innovations such as artificial intelligence, machine learning, and edge computing. These technologies are enabling faster, more accurate, and cost-effective map creation and updates. Companies are leveraging crowdsourced data, vehicle sensor inputs, and satellite imagery to enhance map precision and coverage. The integration of 5G connectivity and cloud-based platforms is streamlining the real-time transmission and processing of mapping data, paving the way for scalable and interoperable solutions. However, the market faces challenges related to data privacy, high initial investment costs, and the need for cross-industry collaboration to establish standardized mapping protocols.

Regionally, North America continues to dominate the lane-level mapmaking services market, owing to the high concentration of autonomous vehicle manufacturers, technology innovators, and favorable government policies. Europe follows closely, driven by stringent safety regulations and the rapid deployment of smart mobility projects. The Asia Pacific region is emerging as a lucrative market, supported by massive investments in smart infrastructure, burgeoning automotive production, and the digital transformation of transportation networks. Latin America and the Middle East & Africa are witnessing steady growth, albeit at a slower pace, as they gradually adopt advanced vehicular technologies and modernize road infrastructure.

Service Type Analysis

The lane-level mapmaking services market by service type is segmented into real-time mapping, high-definition map updates, map validation and verification, and custom map creation. Real-time mapping has emerged as the cornerstone of this segment, driven by the imperative need for up-to-the-minute data in autonomous vehicles and advanced fleet management systems. Real-time mapping solutions leverage a blend of sensor fusion, edge computing, and crowdsourced data to capture and process changes in road conditions, lane markings, and traffic patter

Adverse Weather Road Map Layers Market Outlook

According to our latest research, the global Adverse Weather Road Map Layers market size in 2024 stands at USD 2.15 billion, reflecting the growing integration of real-time weather intelligence into mapping and navigation solutions. The market is expected to expand at a robust CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 6.19 billion by 2033. This growth is primarily driven by the increasing demand for advanced road safety features, the proliferation of connected vehicles, and heightened regulatory focus on safe transportation under adverse weather conditions.

One of the key growth factors for the Adverse Weather Road Map Layers market is the rising emphasis on road safety and accident prevention. Governments and regulatory bodies across the globe are mandating the integration of advanced weather data into navigation systems to reduce weather-related accidents. The deployment of intelligent transportation systems (ITS) that leverage real-time weather data is also gaining traction, allowing for dynamic rerouting, timely alerts, and proactive traffic management. As a result, automotive manufacturers, fleet operators, and public safety agencies are increasingly adopting these solutions to enhance situational awareness and minimize the risks associated with adverse weather events such as heavy rain, snow, fog, and ice.

Another significant driver is the rapid advancement in sensor and data acquisition technologies. The integration of radar-based, satellite-based, and sensor-based layers into digital maps allows for highly accurate, granular, and real-time weather updates. These technological advancements are complemented by the growing adoption of cloud-based platforms, which facilitate seamless data aggregation, processing, and distribution. The convergence of Internet of Things (IoT), artificial intelligence (AI), and big data analytics is further empowering stakeholders to derive actionable insights from vast weather datasets, thereby improving the precision and reliability of road map layers. This, in turn, is accelerating the uptake of advanced navigation and fleet management solutions across diverse end-user segments.

Moreover, the exponential growth of connected and autonomous vehicles is creating new opportunities for the Adverse Weather Road Map Layers market. As vehicles become increasingly reliant on real-time data for navigation and decision-making, the integration of weather-sensitive map layers is becoming indispensable. This is especially relevant for logistics and transportation companies seeking to optimize delivery routes, reduce delays, and ensure driver safety under unpredictable weather conditions. The trend is further amplified by the rise of smart cities and the digital transformation of transportation infrastructure, where data-driven road management and emergency response systems are becoming the norm.

From a regional perspective, North America currently leads the Adverse Weather Road Map Layers market, accounting for the largest share in 2024, driven by the high penetration of advanced automotive technologies, strong regulatory frameworks, and significant investments in smart transportation infrastructure. Europe follows closely, benefiting from stringent safety standards and the increasing adoption of intelligent mobility solutions. The Asia Pacific region is poised for the fastest growth over the forecast period, fueled by rapid urbanization, expanding automotive markets, and government initiatives aimed at enhancing road safety and disaster management capabilities. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a comparatively slower pace, owing to infrastructural challenges and varying levels of technology integration.

Product Type Analysis

The Adverse Weather Road Map Layers market by product type is segmented into Radar-Based Layers, Satellite-Based Layers, Sensor-Based Layers, and Others.

The USGS Colorado Water Science Center, in cooperation with the Colorado Water Conservation Board, collected hydraulic data for the South Platte River for areas adjacent to Fort Morgan, Colo., based on the USGS streamgage 06759500 South Platte River at Fort Morgan, CO. The hydraulic data include survey point data for 54 cross sections and 10 pressure transducers, which are used to record the river stage beginning at Morgan County Road 16 and extending downstream to Morgan County Road 20.5 near Fort Morgan, Colo. The cross-section and pressure transducer location data were collected using real-time kinematic Global Navigation Satellite Systems by USGS personnel from February 15, 2017, through April 18, 2017. These data can be used to develop inundation maps, which could be available to emergency personnel, public officials, and the general public using an online public mapping application at the USGS Flood Inundation Mapper website (http://water.usgs.gov/osw/flood_inundation/), which contains flood inundation map libraries from throughout the country created by the USGS.

Accounts-Payable Time Series for TomTom NV. TomTom N.V., together with its subsidiaries, develops and sells navigation and location-based products and services in Europe, the Americas, and internationally. The company operates in two segments, Location Technology and Consumer. It offers navigation apps, personal and professional sat navs, in-dash navigation, accessories, maps and service updates for drivers; maps for automation, map maker, maps SDK, map display API, and places API; and routing APIs, automotive APIs and UI, and navigation SDK. The company also provides traffic solutions, such as traffic stats, origin destination analysis, route monitoring, junction analytics, historical traffic volumes, and traffic APIs. In addition, it offers road traffic management, location intelligence, electrification, automated driving, navigation for automotive, safety and regulations, as well as fleet management and logistics, mobility on demand, and public sector solutions. It serves enterprises, automotive, and consumer markets. The company was founded in 1991 and is headquartered in Amsterdam, the Netherlands.

Christmas Island lies about 1600 km north-north west of Australia's Northwest Cape and approximately 350 km south of Java in the northern part of the Wharton basin (IndianOcean). Recently Australia declared a 200 mile Fisheries Zone around the island andAGSO was asked to assess seabed morphology, sediment thickness and offshore mineralresources in this area. In February 1992 RAT "Rig Seismic" carried out a detailed survey ofthe region, providing relevant data for the required assessment. Eight seismic profiles wereacquired on this cruise, totalling about 2000 km, and almost twice as much bathymetricdata was recorded. In conjunction with seismic and bathymetic data collected by otherinstitutions, our data provides a good coverage of the area, which enabled us to compile anew bathymetric map and to produce the first sediment thickness map.

Among the published bathymetric maps only three cover the Christmas Island area: 1)published by Udintsev (Geophysical Atlas of the Indian Ocean, 1975; 1:5,000 000), 2) byMammerickx et al. (1976, 1:5,000 000) and 3) 1:10,000 000 General BathymetricCompilation (GEBCO) map, published by the International Oceanographic Service (1982).All published bathymetric maps were compiled in the end of the 1970s and the beginningof the 1980s, and all of them were based on processing analog records of water depths andwere drafted manually. Moreover, most of the data for map compilation were collectedusing a sextant, and only a very limited using satellite navigation.

The amount and quality of data collected by the end of the 1970s allowed the production ofthe fairly accurate 1:5,000 000 and 1:10,000 000 maps of the Indian Ocean listed above,however a lot of smaller features, such as individual seamounts, are missing on those maps.Insufficient data coverage led to broad extrapolation of bathymetric trends, sometimesderived purely from magnetic lineation pattern (Fig.1). To the east of Christmas Island thelack of information is particularly evident: all the maps differ in their interpretation of this area.

New high quality data collected by "Rig Seismic", and digital water depths obtained fromthe USA National Geophysical Data Bank (NGDC), were used for compilation of a newrevised version of the bathymetric map on the Christmas Island area in a 1:1,000 000 scale.The new map (to be published in AGSO's Offshore Resources Map Series) contains a lotmore detail on the complex bathymetry of the area, and gives a more realistic picture ofseamount distribution and the structure of the Java Trench and Java's outer-arc ridge. Theamount of added information can be clearly deduced from comparison of Fig.1 and Fig.2.The time scale used in this report is that of McDougall (1974) and Fanoon et al. (1993).

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

Click here to download the point cloud data for the North Shore coastline

  DATA ACQUISITION


    Airborne Data Acquisition

  An airborne laser scanner survey was conducted over the North Shore, from North Head to Long Bay
    (approximately 22.5 km following the shoreline). Operations were undertaken on 19th June 2019 in good flying
    conditions. Data were acquired using a Riegl VUX-1LR lidar system, mounted on an EC120 helicopter, operated
    by Christchurch Helicopters. The laser survey was based on the following parameters:



      Parameter


      Parameter



      Scanner
      Riegl VUX-1LR
      Pulse Repetition
      820 kHz


      Flying Height
      50-80 m above ground
      Swath Overlap
      75-100%


      Scan Angle
      180 degrees
      Aircraft speed
      45 knots


      Scan Frequency
      170 Lines per second
      Nominal pulse density
      50 pls/m2 (p/flightline)



  The scanner-IMU was mounted on a front facing boom extending below the cockpit with an unobstructed
    240-degree field of view, with a GNSS antenna mounted on the cockpit. 
  Survey operations were conducted from North Shore Aerodrome, with each survey comprising a sequence of short,
    linear flightlines aligned to the coast. Flightlines were acquired north-south, and then south-north, to
    account for the effects of occlusion during a single overpass. Each return sortie too approximately 70 mins
    of flying time (not including travel time to and from a regional base). Following the first sortie, all
    instrumentation was powered down and dismounted, before being remounted and reinitialized. This approach
    mimics exactly the procedure that would be followed between two widely separately surveys in time.
  Global Navigation Satellite Systems (GNSS) Base Station Data
  GNSS observations were recorded at a 3rd order (2V) LINZ geodetic mark (GSAL) to correct the roving
    positional track recorded at the sensor. This is a continuous operating reference station (CORS) operating
    as part of Global Surveys Leica Geosystems SmartFix network, recording observations at 1 s. The details of
    the reference station are as follows:



      LINZ
        Benchmark Code:
      GSAL (Albany Triton)


      Benchmark Position:

      Latitude:
      36° 44' 27.51079" S


      Longitude:
      174° 43' 23.50966" E


      Ellipsoidal height
        (m):
      88.262


      Antenna:

      Leica AS10



  A further ground survey of check point data was acquired using Leica GS15 GNSS systems operating using
    network RTK GNSS based on the Global Survey SmartFix network. >300 observations were acquired from
    across the survey area, classified by land-cover to include hard surfaces (roads); and short grass pasture.
    Note: network RTK GNSS have typical absolute accuracies of 4-6 cm over the baseline lengths used here (15-25
    km).
  Real Time Kinematic GNSS Checkpoint Data
  A distributed network of 351 checkpoints were acquired as checkpoints to evaluate the vertical accuracy and
    precision of the survey data. All points were collected using network-derived RTK GNSS observations based
    on the Leica Geosystems SmartFix network of broadcasting referencing stations. Measurements were acquired
    with a Leica GS16 receiver on the 24th January 2020, and acquisition settings that enforced a 3D standard
    deviation of < 0.025 m for each observation. To capture any broad scale patterns of georeferencing
    error, the checkpoints were collected in four regional surveys at Browns Bay, Mairangi Bay, Milford and
    Narrow Neck, as shown in Figure 6 overleaf.

  DATA PROCESSING
  Trajectory Modelling
  Lidar positioning and orientation (POS) was determined using the roving GNSS/IMU and static GNSS observations
    acquired using Waypoint Inertial Explorer Software. The resulting solution maintained attitude separation
    of less than +-2 arcmin and positional separation of less than +-1 cm. Trajectories were solved
    independently for each of the two surveys.
  Lidar Calibration
  Swath calibration based on overlap analysis was undertaken using the TerraScan and TerraMatch software
    suite. Flightline calibration was undertaken to solve for global and flightline specific deviations and
    fluctuations in attitude and DZ based on over 100,000 tie-lines derived from ground observations. The
    results of the calibration, based on all used tie-lines is shown in Table 2 below:




      Survey
      Initial mean 3D
        mismatch (m)
      Calibrated mean 3D
        mismatch


      1
      0.055
      0.014


      2
      0.044
      0.011



  Point Cloud Classification
  Data were classified using standard routines into ground, above ground and noise.

    For Survey 1, the point density over the entire area is 97.5 points/m² for all point classes and 44.2
      points/m2 for only ground points.
    For Survey 2, the point density over the entire area is 55.7 points/m² for all point classes and 30.9
      points/m2 for only ground points.

  The difference between the two datasets reflects trimming of Survey 1 to incorporate only the coastal fringe,
    while Survey 2 extends inland by typically 300 m to provide a demonstration of the potential wider coverage
    observable from the flightpath. On the beach areas and along the cliff sections, typical densities are in
    excess of 100 points/m2 in both surveys. The final point cloud classification for each survey is shown in
    Table 3:



      Surface Type
      Classification Code

      Point Class
      Survey 1
        Observations
      Survey 2
        Observations


      Unclassified
      1
      Off-Ground
      204,644,243
      226,749,086


      Ground
      2
      Ground
      143,160,406
      182,111,679


      Total Points


      347,804,649
      408,860,765

Water supply lakes are the primary source of water for many communities in northern and western Missouri. Therefore, accurate and up-to-date estimates of lake capacity are important for managing and predicting adequate water supply. Many of the water supply lakes in Missouri were previously surveyed by the U.S. Geological Survey in the early 2000s (Richards, 2013) and in 2013 (Huizinga, 2014); however, years of potential sedimentation may have resulted in reduced water storage capacity. Periodic bathymetric surveys are useful to update the area/capacity table and to determine changes in the bathymetric surface. In June and July 2020, the U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources and in collaboration with various cities in north- and west-central Missouri, completed bathymetric surveys of 12 lakes using a marine-based mobile mapping unit, which consists of a multibeam echosounder (MBES) and an inertial navigation system (INS) mounted on a marine survey vessel. Bathymetric data were collected as the vessel traversed longitudinal transects to provide nearly complete coverage of the lake. The MBES was electronically tilted in some areas to improve data collection along the shoreline, in coves, and in areas that are shallower than about 2.0 meters deep (the practical limit of reasonable and safe data collection with the MBES). At some lakes, supplemental data were collected in shallow areas using an acoustic Doppler current profiler (ADCP) mounted on a remote-controlled vessel equipped with a differential global positioning system (DGPS). Bathymetric quality-assurance data also were collected at each lake to evaluate the vertical accuracy of the gridded bathymetric point data from the MBES. As part of the survey at each of these lakes, one or more reference marks or temporary bench marks were established to provide a point of known location and elevation from which the water surface could be measured or another survey could be referenced at a later date. In addition, the elevation of a primary spillway or intake was surveyed, when present. These points were surveyed using a real-time kinematic (RTK) Global Navigation Satellite System (GNSS) receiver connected to the Missouri Department of Transportation real-time network (RTN), which provided real-time survey-grade horizontal and vertical positioning, using field procedures as described in Rydlund and Densmore (2012) for a Level II real-time positioning survey. Mozingo Lake and Maryville Reservoir were surveyed in June 2020 as part of the group of lakes surveyed in 2020. However, extraordinary interest in the bathymetry at Mozingo Lake by the city of Maryville necessitated these data being released earlier than the other 2020 lakes (Huizinga and others, 2021, 2022). The MBES data can be combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table for each lake. These data also can be used to compare the current bathymetric surface with any previous bathymetric surface. Data from each of the remaining 10 lakes surveyed in 2020 are provided in ESRI Shapefile format (ESRI, 2021). Each of the lakes surveyed in 2020 except Higginsville has a child page containing the metadata and two zip files, one for the bathymetric data, and the other for the bathymetric quality-assurance data. Data from the surveys at the Upper and Lower Higginsville Reservoirs are in two zip files on a single child page, one for the bathymetric data and one for the bathymetric quality assurance data of both lakes, and a single summary metadata file. The zip files follow the format of "####2020_bathy_pts.zip" or "####2020_QA_raw.zip," where "####" is the lake name. Each of these zip files contains a shapefile with an attribute table. Attribute/column labels of each table are described in the "Entity and attribute" section of the metadata file. The various reference marks and additional points from all the lake surveys are provided in ESRI Shapefile format (ESRI, 2021) with an attribute table on the main landing page. Attribute/column labels of this table are described in the "Entity and attribute" section of the metadata file. References Cited: Environmental Systems Research Institute, 2021, ArcGIS: accessed May 20, 2021, at https://www.esri.com/en-us/arcgis/about-arcgis/overview Huizinga, R.J., 2014, Bathymetric surveys and area/capacity tables of water-supply reservoirs for the city of Cameron, Missouri, July 2013: U.S. Geological Survey Open-File Report 2014–1005, 15 p., https://doi.org/10.3133/ofr20141005. Huizinga, R.J., Oyler, L.D., and Rivers, B.C., 2022, Bathymetric contour maps, surface area and capacity tables, and bathymetric change maps for selected water-supply lakes in northwestern Missouri, 2019 and 2020: U.S. Geological Survey Scientific Investigations Map 3486, 12 sheets, includes 21-p. pamphlet, https://doi.org/10.3133/sim3486. Huizinga R.J., Rivers, B.C., and Oyler, L.D., 2021, Bathymetric and supporting data for various water supply lakes in northwestern Missouri, 2019 and 2020 (ver. 1.1, September 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P92M53NJ. Richards, J.M., 2013, Bathymetric surveys of selected lakes in Missouri—2000–2008: U.S. Geological Survey Open-File Report 2013–1101, 9 p. with appendix, https://pubs.usgs.gov/of/2013/1101. Rydlund, P.H., Jr., and Densmore, B.K., 2012, Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D1, 102 p. with appendixes, https://doi.org/10.3133/tm11D1.

Other-Appropriated-Reserves Time Series for TomTom NV. TomTom N.V., together with its subsidiaries, develops and sells navigation and location-based products and services in Europe, the Americas, and internationally. The company operates in two segments, Location Technology and Consumer. It offers navigation apps, personal and professional sat navs, in-dash navigation, accessories, maps and service updates for drivers; maps for automation, map maker, maps SDK, map display API, and places API; and routing APIs, automotive APIs and UI, and navigation SDK. The company also provides traffic solutions, such as traffic stats, origin destination analysis, route monitoring, junction analytics, historical traffic volumes, and traffic APIs. In addition, it offers road traffic management, location intelligence, electrification, automated driving, navigation for automotive, safety and regulations, as well as fleet management and logistics, mobility on demand, and public sector solutions. It serves enterprises, automotive, and consumer markets. The company was founded in 1991 and is headquartered in Amsterdam, the Netherlands.

This dataset contains a map of ground movements covering the Afar Rift Zone in Ethiopia, Eritrea, and Djibouti for the time period between October 2014 and August 2019. The Afar region is located where three tectonic plates are pulling apart, creating rift segments which are 50-100 km long. Surface deformation on these segments is not constant in time, with episodes of rifting occurring periodically and magma intrusions causing sudden ground movements. We use frequent Sentinel-1 satellite Interferometric Synthetic Aperture Radar (InSAR) observations to measure surface displacements through time across the whole region. We relate these to ground based Global Navigation Satellite Systems (GNSS) observations and combine data from different satellite tracks to produce maps of the average surface velocity in three directions (perpendicular to the rift zone, parallel to the rift zone, and vertical). The continued observation of these time-varying ground movements is important for understanding how continents break up, with data here providing evidence of how tightly focussed extension is around the rift segments and of the subsurface magma movement at several volcanic centres.

Other-Non-Cash-Items Time Series for TomTom NV. TomTom N.V., together with its subsidiaries, develops and sells navigation and location-based products and services in Europe, the Americas, and internationally. The company operates in two segments, Location Technology and Consumer. It offers navigation apps, personal and professional sat navs, in-dash navigation, accessories, maps and service updates for drivers; maps for automation, map maker, maps SDK, map display API, and places API; and routing APIs, automotive APIs and UI, and navigation SDK. The company also provides traffic solutions, such as traffic stats, origin destination analysis, route monitoring, junction analytics, historical traffic volumes, and traffic APIs. In addition, it offers road traffic management, location intelligence, electrification, automated driving, navigation for automotive, safety and regulations, as well as fleet management and logistics, mobility on demand, and public sector solutions. It serves enterprises, automotive, and consumer markets. The company was founded in 1991 and is headquartered in Amsterdam, the Netherlands.

Depreciation Time Series for TomTom NV. TomTom N.V., together with its subsidiaries, develops and sells navigation and location-based products and services in Europe, the Americas, and internationally. The company operates in two segments, Location Technology and Consumer. It offers navigation apps, personal and professional sat navs, in-dash navigation, accessories, maps and service updates for drivers; maps for automation, map maker, maps SDK, map display API, and places API; and routing APIs, automotive APIs and UI, and navigation SDK. The company also provides traffic solutions, such as traffic stats, origin destination analysis, route monitoring, junction analytics, historical traffic volumes, and traffic APIs. In addition, it offers road traffic management, location intelligence, electrification, automated driving, navigation for automotive, safety and regulations, as well as fleet management and logistics, mobility on demand, and public sector solutions. It serves enterprises, automotive, and consumer markets. The company was founded in 1991 and is headquartered in Amsterdam, the Netherlands.