Post-processed GPS time series associated with the article "Weeks-long and years-long slow slip and tectonic tremor episodes on the south-central Alaska megathrust". The columns indicate: Decimal time | North (mm) | East (mm) | Up (mm) | sigN (mm) | sigE (mm) | sigU (mm)

Abstract: The Global Navigation Satellite System (GNSS) is used for precise positioning applications, such as surveying and geodesy. The aim of the present work is to evaluate the effectiveness of web-based relative positioning (RP) and precise point positioning (PPP) GNSS post-processing services using measurements of different satellite visibility obstacles. Within this framework, static GNSS observations were conducted at three control benchmarks selected taking the impact of natural and human-made obstacles on satellite signals into consideration. 3 hours of static GNSS observations in Istanbul, Turkey were repeatedly obtained from three control BMs over six days and were evaluated through two RP (AUSPOS, OPUS) and three PPP (CSRS-PPP, Magic-PPP, GAPS-PPP) web-based GNSS post-processing services. The 6-day average of the three control benchmark coordinates computed using the Bernese GPS software v5.0, and were accepted as true results. They were compared to the local coordinates acquired through the RP and PPP web-based GNSS post-processing services. The different satellite visibility conditions were found to have significant effects on the GNSS point positioning solutions. We also found that web-based GNSS post-processing services provide easy and effective solutions for geodetic positioning applications.

This dataset is about: GNSS measurements at ITH_20_GNSS. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.918649 for more information.

Discover the booming GNSS/INS Post-Processing Kinematic (PPK) software market. This in-depth analysis reveals a projected CAGR of 15% reaching $700M by 2033, driven by autonomous vehicles and drone technology. Learn about key players, market trends, and future growth projections.

Abstract: Since the introduction of the Android 7 in August 2016, it has become possible to use raw data collected by GNSS sensors present in some Android smartphones and tablets. Therefore, it became possible, for the first time, to perform the post-processing of the data, which means to obtain coordinates that are more accurate than usual, from meters to decimeters. In addition, among the technological innovations in the context of positioning via smartphones, it is mentioned the use of modern GNSS sensors, like the one used by Xiaomi Mi 8, which was the first smartphone to integrate a dual-frequency GNSS sensor. In this research, data collection campaigns were carried out in static mode to evaluate the quality of geodetic coordinates obtained from Mi 8. Using freely available applications that store raw data in files in RINEX format, the data was post-processed with different positioning methods and with different software, including the freely available IBGE-PPP online service in Brazil. The results of this research show that it is possible to obtain geodetic coordinates with an accuracy at decimeter order, which indicates that the methodology can be used in some engineering applications.

NSF COLDEX GPS/IMU Level 1B Airborne Position and Attitude Solutions These data are results processed using Hexagon | NovAtel's Waypoint Inertial Explorer, a GUI environment for performing joint Inertial Measurement Unit (IMU) / Global Positioning System (GPS) kinematic position and attitude solutions. The raw data used for creating these solutions is at Young et al., 2025 [USAP-DC] . Manual steps included cutting out bad portions of data and removing bad GPS satellite range information. Only the US GPS constellation of satellites was used. Two types of solution are provided. wpt1 solutions are produced by jointly processed IMU rotation rate and acceleration data with GPS data using a Kalman filter to produce an internally consistent position and aircraft attitude solution at the center of the IMU unit at a rate of 50 Hz. Loosely coupled solutions first perform kinematic precise point positioning (PPP) solving the GPS range data for 1 Hz positions, and then fit the IMU data to interpolate positions and find attitude. Tightly coupled solutions incorporate the IMU data into the position solutions. Accuracies are typically on the order of a few cm. wpt2 solutions only have the PPP position solution, and provide redundancy in the case of an IMU issue. These produce data at the rate of the GNSS receiver (typically 1–2 Hz). Files have the following name convention: SEASON_PLATFORM_FLIGHT_PROCESSING.wpt# Here the SEASON is either CXA1 (the 2022–23 NSF COLDEX airborne season) or CXA2 (the 2023–24 NSF COLDEX airborne season); the PLATFORM is the GNSS antenna/receiver combination; the FLIGHT is the flight number within the season; and the PROCESSING is either LCPPP (loosely coupled with PPP), TCPPP (tightly coupled with PPP), or PPP (PPP only). Some flights have multiple files due to system restarts; other files span multiple flights due to short turn around between flights. A file called POS_timelimits.csv contains the start and end time of each file in seconds with respect to the UNIX epoch. The files are in the form of tables with headers and footers delimited with the # character. Column names are internally defined.

This dataset is about: GNSS measurements at TVC_24_GNSS. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.918649 for more information.

The Global GNSS Positioning Correction Service market is projected to be valued at $1.2 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12.3%, reaching approximately $3.5 billion by 2034.

Data in this release record ground-surface positions obtained during post-disaster emergency response following the 2014 catastrophic Oso (SR 530) landslide, Snohomish County, Washington. Global Positioning System (GPS) data were collected using three USGS GPS-seismometer spider units deployed adjacent to (OSO1), upslope of (OSO2), and on (OSO3) the landslide (see image for locations) for about five weeks. Details of the post-disaster response as well as the spider units are described in the accompanying publication. Positions were determined in near real-time relative to a base-station GPS receiver (OSO0) located on stable ground less than 2 km from the landslide using static, differential GPS processing techniques. The spider positions over time were computed using GPS L1-only data collected every 15 seconds and processed about every 10 minutes using the previous 3-hour periods of data. Data are presented as latitude, longitude, and ellipsoid height in the World Geodetic System 1984 (WGS 84) coordinate system with times in Coordinated Universal Time (UTC). Horizontal coordinates are also presented as converted into Universal Transverse Mercator (UTM) values. Differences in horizontal and 3D positions over time, relative to the initial spider locations, are included. Time gaps in the data sets indicate that insufficient GPS data were available to obtain fixed ambiguity, high quality, static solutions during the gap time periods. Details of the GPS processing techniques are further described in the Procedure section of the FGDC Metadata. Seismic data from the same spider units are available through IRIS. This data release includes: 1) image showing locations of the three GPS-seismic spider units relative to the Oso landslide, 2) initial positions (WGS 84) of the three spider units and the reference base station, OSO0, and 3) three files containing ground-surface positions and horizontal and 3D movement amounts for OSO1, OSO2, and OSO3 spiders over the monitoring period.

Uncorrected GPS/GNSS observational data collected using an Altus APS3G GNSS receiver on a 2m survey pole by Anne Hellie on the 19th and 20th of January, 2018 (UTC time).

The excel file “Uncorrected_data.xlsx” contains the output of eight different data files collected (SILLS to SILLS 7).
The data has been compiled into the worksheet “Uncorrected_data”.
The other worksheets contain copies of the notes from Carlson Surv PC (the software used to collect the
points), along with a lot more metadata.

Using ArcGIS 10.3 and the XtoolsPro extension, “Uncorrected_data” has been made into an ArcGIS shapefile (*.shp) and GoogleEarth .kml file. Both are named “4318_2018”, with their appropriate extensions.

Note that since these points were collected in autonomous mode (with no RTK or SBAS solutions), these
are uncorrected data points, and have poor accuracy, particularly in the vertical (see the VSDV field for
the vertical standard deviation).

The accuracy of these positions can be substantially improved by differentially correcting/post-processing the data. This can be done with the open source software RTKLIB. Using the Septentrio software (SBF converter), the raw
data files (.sbf files collected by the Septentrio unit) have been converted into Rinex *.obs files to facilitate this process.
The base station files for the appropriate time period can be downloaded from the
Geoscience Australia Geodesy ftp server. The closest base station (CORS) is located at Casey and known as “cas1”.
ftp://ftp.ga.gov.au/geodesy-outgoing/gnss/data/highrate/2018/

GNSS receivers collect the signals from orbiting satellites to determine their location in three dimensions and calculate precise time. GNSS receivers detect, decode, and process both pseudorange (code) and phase transmitted by the GNSS satellites. The satellites transmit the ranging codes on two or more radio-frequency carriers, allowing the locations of GNSS receivers to be determined with varying degrees of accuracy, depending on the receiver and post-processing of the data. The receivers also calculate current local time to high precision facilitating time synchronization applications.

This dataset consists of ground-based Global Navigation Satellite System (GNSS) GLONASS Compact Observation Data (30 second sampling, daily, 24 hour files) from the NASA Crustal Dynamics Data Information System (CDDIS). GNSS provide autonomous geo-spatial positioning with global coverage. The GLONASS data sets from ground receivers at the CDDIS consist of observations from the Russian GLObal NAvigation Satellite System (GLONASS); Russia's GLONASS is similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure. The daily GLONASS compact observation data files contain one day of GLONASS observation (30-second sampling) data in RINEX format from a global permanent network of ground-based receivers, one file per site.

The data set consists of an Excel file containing the supporting data for the following publication:

Lambert, A., Huang, J., Courtier, N., & Pavlic, G. (2020). Constraints on secular geocenter velocity from absolute gravity observations in central North America: Implications for global melting rates. J. Geophys. Res., in prep.

The Excel data file comprises four sheets:

Sheet 1: Annual absolute gravity observations at six sites (1995-2010) and two sites (2002-2010) showing site names, observation time in decimal years, gravity values and standard errors in microGal (1 microGal = 10 nm/s2), site reference gravity values, instruments used, observers names and site co-ordinates.

Sheet 2: Daily GPS heights at five sites (1996/7-2010) and one site (2003-2010) showing site names, observation time in decimal years, heights and standard deviations in meters, and site co-ordinates.

Sheet 3: Daily GPS heights at ten sites (2002-2010) used with data from two sites in sheet 2 to calculate vertical velocities at two absolute gravity sites (sheet 4) where no continuous GPS was available. Site co-ordinates are given in sheet 4.

Sheet 4: Long-term height trends (vertical velocities) are estimated for the two sites lacking continuous GPS by using a 2-D adaptive Gaussian interpolation function, with a half-width defined as the distance to the nearest GPS site. The absolute gravity drop data were processed using the Micro-g LaCoste "g8" software.

The GPS data were processed with the NRCan Precise Point Positioning PPP 1.05 software (Héroux and Kouba, 2001). For each site, daily positions were computed using ionosphere-free combinations of un-differenced pseudo-range and phase observations, with satellite orbits and clocks fixed to the International GNSS Service (IGS) precise products, absolute phase-center calibrations for the GPS and satellite antennas (Schmid et al., 2007), gridded Vienna Mapping Functions (VMF1, Boehm et al., 2006) for the troposphere model, and solid earth and ocean tide corrections. The GPS post-processing was originally carried out in support of Mazzotti et al. (2011).References:

Boehm, J., Werl, B., & Schuh, H. (2006). Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Median-Range Weather Forecast operational analysis data. J. Geophys. Res., 111, B02406. https://doi.org/10.1029/2005JB003629

Héroux, P., & Kouba, J. (2001). GPS Precise Point Positioning using IGS orbit products. Phys. Chem. Earth (A), 26, 573-578. https://doi.org/10.1016/S1464-1895(01)00103-X

Mazzotti, S., Lambert, A., Henton, J., James, T.S., & Courtier, N. (2011). Absolute gravity calibration of GPS velocities and glacial isostatic adjustment in mid-continent North America. Geophys. Res. Lett., 38, L24311. https://doi.org/10.1029/2011GL049846

Schmid, R., Steigenberger, P., Gendt, G., Ge, M., & Rothacher, M. (2007). Generation of a consistent absolute phase center correction model for GPS receiver and satellite antennas. J. Geod., 81 (12) 781-798. https://doi.org/10.1007/s00190-007-0148-yData Sources and Open Data Policy

Absolute gravity data source: Geological Survey of Canada.

GPS data sources: Canadian Active Control System (CACS) data from Canadian Geodetic Survey’s Geodetic Data Products web site, NASA Crustal Dynamics Data Information System (CDDIS), and U.S. National Geodetic Survey, Continually Operating Reference Stations (CORS) data download site.

Use of Canadian Geodetic Survey products and data is subject to the

Open Government Licence - Canada

© Her Majesty the Queen in Right of Canada, as represented by the Minister of Natural Resources, 2020

Small Uncrewed Aircraft Systems (sUAS) were used to collect aerial remote sensing data over Sesuit Marsh in Dennis, MA. Raw data from aerial surveys include aerial images from natural color (RGB) and multispectral cameras and raw lidar data. These datasets were processed to produce high resolution digital elevation models (DEM), image mosaics, and lidar point clouds (LPC) to provide local partners with high resolution imagery and elevation data to monitor and identify vegetation cover of a salt marsh on Cape Cod during peak growing season. A YellowScan Mapper Plus lidar system with integrated RGB camera module were mounted to a DJI Matrice 600 to collect the raw lidar data and true-color imagery. A MicaSense Altum-PT was then mounted to the DJI Matrice 600 to collect the multispectral reflectance imagery. Black and white cross-coded ground control points (GCPs) were surveyed using RTK-GPS and RTK-enabled AeroPoints to georeference the model and orthomosaics during post-processing. This project was supported by grant funding from the Commonwealth of Massachusetts' Executive Office of Energy and Environmental Affairs, Division of Conservation, awarded to Rick Rheinhardt in collaboration with the Cape Cod Conservation District.

Developed at JPL, GUARDIAN is a near-real-time (NRT) ionospheric monitoring software (Martire et al.). Its main products are NRT total electronic content (TEC) time series, allowing users to explore ionospheric TEC perturbations due to natural and anthropogenic events on Earth. The NRT GUARDIAN time series are validated against well-established post-processing methods. Currently, time series are computed for more than 90 GNSS ground stations distributed around the Pacific Ring of Fire, which monitor the four main GNSS constellations (GPS, Galileo, BDS, and GLONASS).

GNSS Survey Receiver Market Outlook

As per our latest research, the global GNSS Survey Receiver market size stood at USD 1.87 billion in 2024, reflecting robust demand from infrastructure and geospatial sectors. The market is expected to expand at a CAGR of 7.3% from 2025 to 2033, reaching a projected value of USD 3.53 billion by 2033. This growth is primarily driven by increasing adoption of precise positioning technologies across construction, agriculture, and mining sectors, as well as ongoing advancements in GNSS receiver capabilities and integration with digital mapping and IoT systems.

The GNSS Survey Receiver market is experiencing significant growth due to the rapid expansion of infrastructure development projects worldwide. As urbanization accelerates and governments prioritize smart city initiatives, the demand for high-precision geospatial data collection tools has soared. GNSS survey receivers, which provide centimeter-level accuracy, are now indispensable in the planning, monitoring, and execution of large-scale construction and civil engineering projects. Additionally, the integration of GNSS technology with Building Information Modeling (BIM) and Geographic Information Systems (GIS) has enabled more efficient project management and resource allocation, further fueling market expansion. The proliferation of public-private partnerships and increased investments in transportation, utilities, and urban planning are also pivotal in driving the adoption of advanced GNSS survey solutions.

Technological advancements are another major factor propelling the GNSS Survey Receiver market. The transition from single-frequency to multi-frequency receivers has enhanced the reliability and accuracy of positioning, even in challenging environments such as dense urban areas or under heavy canopy cover. Innovations in receiver design, including miniaturization, improved battery life, and enhanced connectivity options, have broadened their applications across various industries. The integration of GNSS with real-time kinematic (RTK) and post-processing kinematic (PPK) techniques has further improved survey efficiency and data precision. Moreover, the development of multi-constellation receivers capable of leveraging signals from GPS, GLONASS, Galileo, and BeiDou systems ensures greater signal availability and robustness, supporting the market’s upward trajectory.

The expanding adoption of GNSS survey receivers in precision agriculture and mining is further accelerating market growth. In agriculture, these receivers enable farmers to optimize field mapping, crop planting, and resource management, leading to increased yields and reduced operational costs. In mining, GNSS receivers are critical for site surveying, equipment tracking, and resource extraction planning, ensuring safety and operational efficiency. The growing emphasis on sustainable practices and resource optimization in these industries has made GNSS receivers a vital component of modern workflows. Additionally, the marine sector is increasingly deploying GNSS survey receivers for hydrographic surveying, navigation, and port construction, further diversifying the market’s application base.

From a regional perspective, the Asia Pacific region is emerging as a dominant force in the GNSS Survey Receiver market, driven by large-scale infrastructure development, rapid urbanization, and government initiatives to modernize agriculture and transportation. North America and Europe continue to exhibit steady demand, supported by technological innovation and established construction and mining industries. Meanwhile, Latin America and the Middle East & Africa are witnessing rising adoption rates as infrastructure investment and digital transformation initiatives gain momentum. Each region presents unique opportunities and challenges, shaping the competitive landscape and influencing product development strategies.

Product Type Analysis

The GNSS Survey Receiver market is segmented by product type into Single Frequency GNSS Receivers and Multi-Frequency GNSS Receivers, each catering to distinct user requirements and industry applications. Single frequency receivers, while cost-effective and suitable for basic surveying tasks, are increasingly being overshadowed by their multi-frequency counterparts due to limitations in accuracy, especially in environments with signal obstructions or multipath interference. These devices are typically fav

This data release presents the post-processed Global Navigation Satellite System (GNSS) ground-survey data acquired during the installation of a camera system at Tres Palmas, Rincón, Puerto Rico (PR). The data contains topographic survey data collected during the installation of the camera. Data were collected on foot, by a person equipped with a GNSS antenna affixed to a backpack outfitted for surveying location and elevation data (XYZ). The GNSS measurements were made using Post-Processed Kinematic (PPK) corrections referenced to a temporary base station located approximately 250 meters from the study area.

The objectives of the survey were to provide bathymetric and sidescan sonar data for sediment transport studies and coastal change model development for ongoing studies of nearshore coastal dynamics along Sandwich Town Neck Beach, MA. Data collection equipment used for this investigation are mounted on an unmanned surface vehicle (USV) uniquely adapted from a commercially sold gas-powered kayak and termed the "jetyak". The jetyak design is the result of a collaborative effort between USGS and Woods Hole Oceanographic Institution (WHOI) scientists.

This portion of the data release presents orthomosaic images of the Whale's Tail Marsh region of South San Francisco Bay, CA. The orthomosaics have resolutions of 2.5 centimeters per pixel and were derived from structure-from-motion (SfM) processing of repeat aerial imagery collected from fixed-wing aircraft. The raw imagery used to create these elevation models was acquired from an approximate altitude of 427 meters (1,400 feet) above ground level (AGL), using a Hasselblad A6D-100c camera fitted with an HC 80 lens, resulting in a nominal ground-sample-distance (GSD) of 2.5 centimeters per pixel. The acquisition flight lines were designed to provide approximately 50 percent overlap between adjacent flight lines (sidelap), with approximately 70 percent overlap between sequential images along the flight line (forelap). Survey control was established using an onboard camera-synchronized dual-frequency GPS system as well as ground control points (GCPs) distributed throughout the survey area and measured using survey-grade post-processed kinematic (PPK) GPS. Both the data from the onboard GPS and from the GPS used to measure the GCPs were post-processed using a nearby Continuously Operating Reference Station (CORS) station operated by the National Geodetic Survey (NGS). Structure-from-motion processing of these data was conducted using a "4D" processing workflow in which imagery from each of the different acquisition dates were co-aligned in order to increase relative spatial precision between the final data products. The resulting orthomosaics have been formatted as cloud optimized GeoTIFFs with internal overviews and masks to facilitate cloud-based queries and display.