The documents included in this dataset provide information on:a) personal questions given to survey participants (DemographicsQuestionnaire.pdf)b) spatial questions given to participants (SpatialQuestions.pdf)c) the adapted SUS questionnaire (MapUsabilityScale.pdf)d) The dataset of collected participants responses, in the form of a zip archive (3D_printed_map.7z). e) a document with brief guidelines for conducting the survey (Guidelines.docx).f) Finally, the R script (experiment.R) to run the statistical analysis detailed in the paper and to generate Tables 1-4 and the contents of Figure 9 are also included. The R script needs calling the above-mentioned dataset of participants' responses (d), to run effectively.

Revenue for the Surveying and Mapping Services industry has been volatile in the years since the pandemic. As the economy emerged from a short-lived downturn, surveyors were buoyed by strong residential construction resulting from record-low interest rates. Investment from the commercial sector also expanded as corporate profit soared. However, as the Federal Reserve raised the cost of borrowing to combat high inflation, homebuying and existing home improvements declined, severely inhibiting the residential sector and prompting a multi-year revenue decline for the industry. While interest rates have remained elevated, the 2021 Bipartisan Infrastructure Law has pumped millions of dollars into highway construction, civil engineering, mineral surveying and geospatial data processing, rewarding select surveying and mapping companies with hefty contracts. Thus, industry revenue is anticipated to grow at a CAGR of 2.0% through 2025, even as interest rates remain elevated. In 2025, the industry is projected to grow 1.8% with revenue totalling $11.5 billion.Advances in technology are revolutionizing surveying by enabling faster, more accurate data collection and processing. Mobile mapping tools, UAVs, 3D laser scanning and AI-driven analytics are streamlining workflows, reducing field time and expanding the range of services companies offer. These innovations are supporting complex projects in construction, infrastructure and smart city planning, while cloud-based GIS and automation are improving productivity. As these tools are becoming industry standards, companies that have been quick to adopt them have gained a competitive edge. This increased competition has left laggards behind, making innovation incumbent to sustaining profitability.The industry will continue to see modest expansion as steady economic growth will increase demand from the nonresidential sector. However, economic uncertainty and the expectation of conservative monetary policy by the Federal Reserve will continue to keep interest rates elevated, tempering the residential housing market. Still, surveyors will benefit from new home construction that is expected to rise above historical averages, especially in regions where job growth will support relocation. Through 2030, industry revenue is forecast to expand at a CAGR of 1.1% to reach $12.2 billion.

Map exhibiting the property of the U.S. in the vicinity of the Capitol : colored red, with the manner in which it is proposed to lay off the same in building lots, as described in the report to the Sup't of the city to which this is annexed / BHLatrobe, one of the surveyors of the city of Washington, Dec. 3d 1815.

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

Electrical resistivity results from four regional airborne electromagnetic (AEM) surveys (Burton et al. 2024, Hoogenboom et al. 2023, Minsley et al. 2021, Burton et al. 2021) over the Mississippi Alluvial Plain (MAP) were combined by the U.S. Geological Survey (USGS) to produce three-dimensional (3D) gridded models and derivative hydrogeologic products. The 3D models and products were first published using data from the earlier two AEM regional surveys, labeled with the year “2020” (Minsley et al. 2021, Burton et al. 2021). The 3D resistivity models and select derivative products were later updated by incorporating additional data from the two later AEM surveys, labeled with the year “2022” (Burton et al. 2024, Hoogenboom et al. 2023). In both 2020 and 2022 versions, grids were discretized in the horizontal dimension to align with the 1 kilometer (km) x 1 km National Hydrogeologic Grid (NHG; Clark et al. 2018), and vertically discretized into both 5 meter (m) depth slices and 5 m ...

BOEM's deepwater Gulf of Mexico bathymetry grid was created by mosaicing over 100 3D seismic surveys. XY grid size is 40ft and depth is in feet. Depth is accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200ft) and maximum (-11,000ft) depth extents, due to the nature of the depth transformation method used. The Bureau of Ocean Energy Management makes publically available a new deepwater bathymetry grid of the northern Gulf of Mexico, created by utilizing 3D seismic data which covers more than 90,000 square miles. The grid provides enhanced resolution compared to existing public bathymetry maps over the region, delivering 10 to 50 times increased horizontal resolution of the salt mini-basin province, abyssal plain, Mississippi Fan, and the Florida Shelf/Escarpment. To create the grid the seafloor was interpreted on over one-hundred 3D seismic time-migrated surveys, then mosaicked together and converted to depth in feet. The grid consists of 1.4 billion, 40-by-40 ft defined cells covering water depths –130 to –11,087 ft (–40 to –3,379 m). The average error is calculated to be 1.3 percent of water depth.BOEM has the responsibility of issuing permits for the acquisition of geophysical data in U.S. Federal waters as designated under the Outer Continental Shelf (OCS) Lands Act. Regulations at 30 CFR 551 allow BOEM to obtain a digital version of any post-processed, post-migrated two-dimensional (2D) and three-dimensional (3D) seismic survey acquired within the OCS. BOEM now maintains a confidential library of approximately 1,700 time and depth 2D/3D seismic surveys for the Gulf of Mexico (GOM), with survey vintages dating back to the early 1980s. These data provide geoscientists a world-class repository of subsurface digital data to interpret and utilize in achieving our regulatory missions.Since 1998, BOEM has used the largest, highest quality 3D time surveys to interpret the seafloor. Time surveys were used because the primary objective was not bathymetry but to identify seafloor acoustic amplitude anomalies indicative of authigenic carbonate hardgrounds and natural hydrocarbon seepage; those areas which may be suitable habitat for communities of chemosynthetic, coral, and other benthic organisms [Roberts, 1996, Roberts et al., 1992 and 2000]. The acoustic amplitude response of the seafloor is better resolved in time-migrated surveys rather than depth-migrated, allowing for increased accuracy in the identification of potential benthic habitats and seeps. While this new bathymetry grid does not include acoustic amplitude data for the seafloor, BOEM does publish polygon shapefiles which outline areas of anomalously high and low seafloor acoustic reflectivity, which can be downloaded at www.boem.gov/Seismic-Water-Bottom-Anomalies-Map-Gallery.Roberts, H.H., (1996), Surface amplitude data: 3D-Seismic for interpretation of seafloor geology (Louisiana slope): Gulf Coast Association of Geological Societies Transactions, v. 46, p. 353–362.Roberts, H.H., D.J. Cook, and M.K. Sheedlo, (1992), Hydrocarbon seeps of the Louisiana continental slope: Seismic amplitude signature and seafloor response: Gulf Coast Association of Geological Societies Transactions v. 42, p. 349–362.Roberts, H.H., J. Coleman, J. Hunt Jr., and W.W. Shedd, (2000), Surface amplitude mapping of 3D-seismic for improved interpretations of seafloor geology and biology from remotely sensed data, Gulf Coast Association of Geological Societies Transactions, v. 50, p. 495–503.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

The Digital Elevation Model (DEM) market is experiencing robust growth, driven by increasing demand across various sectors. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $2.8 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of advanced surveying techniques, such as LiDAR and photogrammetry, is providing higher-resolution and more accurate DEMs, leading to wider application in diverse fields. Secondly, the increasing availability of high-resolution satellite imagery and improved processing capabilities are lowering the cost and increasing the accessibility of DEM data. Thirdly, government initiatives promoting spatial data infrastructure and the growing focus on smart city development are further driving market growth. Key applications include urban planning, infrastructure development, environmental monitoring, precision agriculture, and disaster management. The market is segmented by data resolution, acquisition method, application, and geography. Despite the positive outlook, challenges remain. Data accuracy and consistency, especially across different sources and regions, are ongoing concerns. Data integration and interoperability issues also need to be addressed for seamless data utilization across various applications. The high initial investment in specialized equipment and software can be a barrier for smaller companies entering the market. Furthermore, ensuring the privacy and security of geospatial data is crucial, particularly in light of increased regulatory scrutiny. The competitive landscape comprises both established players like Harris MapMart and National Map, alongside emerging companies offering innovative solutions. Companies are increasingly focusing on developing cloud-based platforms and integrating AI/ML algorithms to enhance data processing and analysis capabilities, fueling market innovation and growth.

This research paper presents the design and development of an indigenous low cost Mobile Mapping System (MMS) for urban surveying applications. The MMS is comprised of economical Hokuyo-30LX 2D laser scanners, vision sensors, Global Positioning System (GPS) and various odometric sensors that can be installed on car like moving platform. The run time sensorial data is interfaced, processed and recorded using Robot Operating System (ROS). The live laser scan is utilized for the pose estimation using Simultaneous Localization and Mapping (SLAM) technique. In absence of valid SLAM estimation and frequent GPS outages, a multimodal sensor fusion framework for the enhanced pose correction has been developed using Kalman Filter (KF) by incorporating the Inertial Measurement Unit (IMU) and wheel odometric data along with SLAM and GPS data. The corrected pose is utilized for the 3D point cloud mapping by incorporating laser scans perceived periodically from various 2D laser scanners mounted on the MMS. The custom-made installation scheme has been followed for mounting three 2D laser scanners at horizontal, vertical and inclined orientations. The efficacy of the developed map has employed for extraction of road edges and associated road assets by establishing the lucrative classification technique of the point cloud using Split and Merge segmentation and Hough transformation. The surveying to map development time has significantly reduced and the mapping results have found quite accurate when matched with the ground truths. Furthermore, the comparison of the developed maps with ground truths and GIS tools reveals the highly acceptable accuracy of the generated results which have found very nearly aligned with the actual urban environment features. In comparison to the existing global MMS variants, the presented MMS is quite affordable solution for limited financial resourced business entities.

High spatial resolution topographic data is essential in assisting volcanic field work, for volcano morphology analyses, and for hazard modeling of volcanic flow processes. The stereoscopic capability of ASTER data provides the opportunity to derive DEMs at a spatial resolution of 15m for the many regions lacking accurate topographic maps. For visualization, a three dimensional model DEM of the geothermal field with the directional wells and other geographical phenomena were incorporated into the GIS program. The three dimensional model viewers were used with directional geothermal well featuring in the Greater Olkaria geothermal field. The three dimensional modeling of geothermal wells was done using the deviation surveys data by linear referencing. The tool was developed in an Excel-based program developed using a Visual Basic for Application (VBA) procedures (macros) which is the Excel visual Basic Editor (VBE). Geographic Information System (GIS) technology was used to model geothermal well directional survey data. This aids in visualizations suggesting inter-relationships between well bore productivity. By running the data through a model in ArcGIS, 3D maps can be created showing where well bores corkscrew their way down through the earths crust. This phenomenon is difficult to see on a two-dimensional map or cross-sectional view. The 3D map is more intuitive to the KenGen team in exploration and production who are accustomed to thinking in three dimensions. This model is of interest to potential drillers because it shows the way through which a well bore goes down. With respect to this model, geological rock units can be incorporated in the model to show the rock unit penetrated by a particular geothermal well bore.

pone.0318710.t005 - Enhanced vehicle localization with low-cost sensor fusion for urban 3D mapping

S1 Data - Enhanced vehicle localization with low-cost sensor fusion for urban 3D mapping

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 (NAD83). All bare earth elevation values are in meters and are referenced to the North American Vertical Datum of 1988 (NAVD88). Each tile is distributed in the UTM Zone in which it lies. If a tile crosses two UTM zones, it is delivered in both zones. The one-meter DEM is the highest resolution standard DEM offered in the 3DEP product suite. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

This paper presents a custom made small rover based surveying, mapping and building information modeling solution. Majority of the commercially available mobile surveying systems are larger in size which restricts their maneuverability in the targeted indoor vicinities. Furthermore their functional cost is unaffordable for low budget projects belonging to developing markets. Keeping in view these challenges, an economical indigenous rover based scanning and mapping system has developed using orthogonal integration of two low cost RPLidar A1 laser scanners. All the instrumentation of the rover has been interfaced with Robot Operating System (ROS) for online processing and recording of all sensorial data. The ROS based pose and map estimations of the rover have performed using Simultaneous Localization and Mapping (SLAM) technique. The perceived class 1 laser scans data belonging to distinct vicinities with variable reflective properties have been successfully tested and validated for required structural modeling. Systematically the recorded scans have been used in offline mode to generate the 3D point cloud map of the surveyed environment. Later the structural planes extraction from the point cloud data has been done using Random Sampling and Consensus (RANSAC) technique. Finally the 2D floor plan and 3D building model have been developed using point cloud processing in appropriate software. Multiple interiors of existing buildings and under construction indoor sites have been scanned, mapped and modelled as presented in this paper. In addition, the validation of the as-built models have been performed by comparing with the actual architecture design of the surveyed buildings. In comparison to available surveying solutions present in the local market, the developed system has been found faster, accurate and user friendly to produce more enhanced structural results with minute details.

Electrical resistivity results from four regional airborne electromagnetic (AEM) surveys (Burton et al. 2024, Hoogenboom et al. 2023, Minsley et al. 2021, Burton et al. 2021) over the Mississippi Alluvial Plain (MAP) were combined by the U.S. Geological Survey (USGS) to produce three-dimensional (3D) gridded models and derivative hydrogeologic products. The 3D elevation grid was used to quantify across the MAP region 1) the occurrence and thickness of surficial (< 15 meter (m) depth) confining material, 2) the top and bottom elevation corresponding to the surficial confining material, and 3) a metric representing the degree of surface confinement or connectivity that ranges from fully confining conditions to high potential hydrologic connectivity. These products were generated using the updated 12-class facies classifications of the 3D electrical resistivity model. See child item “Mississippi Alluvial Plain (MAP): Electrical Resistivity & Facies Classification Grids” for more details on the facies classes: https://www.sciencebase.gov/catalog/item/5f03a7bc82ce0afb2446e11f. The final surfaces and hydrogeologic metrics were exported as raster images in Georeferenced Tagged Image File Format (GeoTIFF) format. Burton, B.L., Adams, R.F. Adams, Minsley, B.J., Pace, M.D.M., Kress, W.H., Rigby, J.R., and Bussell, A.M., 2024, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, March 2018 and May - August 2021: U.S. Geological Survey data release, https://doi.org/10.5066/P9KPK3UJ. Hoogenboom, B.E., Minsley, B.J., James, S.R., and Pace, M.D., 2023, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, Mississippi Embayment, and Gulf Coastal Plain, September 2021 - January 2022: U.S. Geological Survey data release, https://doi.org/10.5066/P93DO0EO. Burton, B.L., Minsley, B.J., Bloss, B.R., and Kress, W.H., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2018 - February 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9XBBBUU. Minsley, B.J., James, S.R., Bedrosian, P.A., Pace, M.D., Hoogenboom, B.E., and Burton, B.L., 2021, Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9E44CTQ.

URL: https://geoscience.data.qld.gov.au/dataset/ds000022

Version 2 ASTER product created as part of CSIRO's 3D mineral mapping Queensland funded by the Geological Survey of Queensland as part of the Industry Priorities Initiative, under the Future Resources Program. Created by CSIRO; Geological Survey of Queensland.

Statewide AEM Surveys Project Overview

The Department of Water Resources’ (DWR’s) Statewide Airborne Electromagnetic (AEM) Surveys Project is funded through California’s Proposition 68 and the General Fund. The goal of the project is to improve the understanding of groundwater aquifer structure to support the state and local goal of sustainable groundwater management and the implementation of the Sustainable Groundwater Management Act (SGMA).

During an AEM survey, a helicopter tows electronic equipment that sends signals into the ground which bounce back. The data collected are used to create continuous images showing the distribution of electrical resistivity values of the subsurface materials that can be interpreted for lithologic properties. The resulting information will provide a standardized, statewide dataset that improves the understanding of large-scale aquifer structures and supports the development or refinement of hydrogeologic conceptual models and can help identify areas for recharging groundwater.

DWR collected AEM data in all of California’s high- and medium-priority groundwater basins, where data collection is feasible. Data were collected in a coarsely spaced grid, with a line spacing of approximately 2-miles by 8-miles. AEM data collection started in 2021 and was completed in 2023. Additional information about the project can be found on the Statewide AEM Survey website. See the publication below for an overview of the project and a preliminary analysis of the AEM data.

California’s Statewide Airborne Electromagnetic Surveys and Preliminary Hydrogeologic Interpretations

Survey Areas

AEM data are being collected in groups of groundwater basins, defined as a Survey Area. See Survey Area Map for groundwater subbasins within a Survey Area:

• Survey Area 1: 180/400 Foot Aquifer (partial), East Side (partial), Upper Valley, Forebay Aquifer, Paso Robles, Atascadero (limited), Adelaida (limited), Cuyama Valley.
• Survey Area 2: Scott River Valley, Shasta Valley, Butte Valley, Tulelake, Fall River Valley (limited), Big Valley (Modoc/Lassen County).
• Survey Area 3: Big Valley (Lake County), Ukiah Valley, Santa Rosa Plain, Petaluma Valley, Sonoma Valley.
• Survey Area 4: White Wolf, Kern County, Tulare Lake, Tule, Kaweah.
• Survey Area 5: Pleasant Valley, Westside, Kings, Madera, Chowchilla, Merced, Turlock, Modesto, Delta-Mendota
• Survey Area 6: Cosumnes, Tracy, Eastern San Joaquin, East Contra Costa, Solano, Livermore, South American, North American, Yolo, Sutter, South Yuba, North Yuba
• Survey Area 7: Colusa, Butte, Wyandotte Creek, Vina, Los Molinos, Corning, Red Bluff, Antelope, Bowman, Bend, Millville, South Battle Creek, Anderson, Enterprise, Eel River, Sierra Valley
• Survey Area 8: Seaside, Monterey, 180/400 (partially surveyed Summer 2021), Eastside (partially surveyed Summer 2021), Langley, Pajaro, Santa Cruz Mid-County, Santa Margarita, San Benito, and Llagas (partial).
• Survey Area 9: Basin Characterization Pilot Study 1 - Madera and Kings.
• Survey Area 10: San Antonio Creek Valley, Arroyo Grande, Santa Maria, San Luis Obispo, Los Osos Area, Warden Creek, Chorro Valley (limited), Morro Valley (limited)
• Survey Area 11: Indian Wells Valley, Rose Valley, Owens Valley, Fish Slough, Indio, Mission Creek, West Salton Sea (limited), East Salton Sea (limited), Ocotillo-Clark Valley (limited), Imperial Valley (limited),Chocolate Valley (limited), Borrego Springs, and San Jacinto

Data Reports

Data reports detail the AEM data collection, processing, inversion, interpretation, and uncertainty analyses methods and procedures. Data reports also describe additional datasets used to support the AEM surveys, including digitized lithology and geophysical logs. Multiple data reports may be provided for a single Survey Area, depending on the Survey Area coverage.

Data Availability and Types

All data collected as a part of the Statewide AEM Surveys will be made publicly available, by survey area, approximately six to twelve months after individual surveys are complete (depending on survey area size). Datasets that will be publicly available include:

AEM Datasets

• Raw AEM Data
• Processed AEM Data
• Inverted AEM Data
• Inverted AEM Data Uncertainty Analysis
• Interpreted AEM Data (for coarse fraction)
• Interpreted AEM Data Uncertainty Analysis

Supporting Datasets

• Flown Survey Lines
• Digitized Lithology Logs
• Digitized Geophysical Logs

AEM Data Viewers

DWR has developed AEM Data Viewers to provides a quick and easy way to visualize the AEM electrical resistivity data and the AEM data interpretations (as texture) in a three-dimensional space. The most recent data available are shown, which my be the provisional data for some areas that are not yet finalized. The Data Viewers can be accessed by direct link, below, or from the Data Viewer Landing Page.

• AEM 3D Viewer (Beta) (computer only)

• AEM Profile Viewer

• SGMA Data Viewer (Basin Characterization tab)

AEM Depth Slice and Shallow Subsurface Average Maps

As a part of DWR’s upcoming Basin Characterization Program, DWR will be publishing a series of maps and tools to support advanced data analyses. The first of these maps have now been published and provide analyses of the Statewide AEM Survey data to support the identification of potential recharge areas. The maps are located on the SGMA Data Viewer (under the Hydrogeologic Conceptual Model tab) and show the AEM electrical resistivity and AEM-derived texture data as the following:

• Shallow Subsurface Average: Maps showing the average electrical resistivity and AEM-derived texture in the shallow subsurface (the top approximately 50 feet below ground surface). These maps support identification of potential recharge areas, where the top 50 feet is dominated by high resistivity or coarse-grained materials.

• Depth Slices: Depth slice automations showing changes in electrical resistivity and AEM-derived texture with depth. These maps aid in delineating the geometry of large-scale features (for example, incised valley fills).

Shapefiles for the formatted AEM electrical resistivity data and AEM derived texture data as depth slices and the shallow subsurface average can be downloaded here:

• Electrical Resistivity Depth Slices and Shallow Subsurface Average Maps

• Texture Interpretation (Coarse Fraction) Depth Slices and Shallow Subsurface Average Maps

Technical Memos

Technical memos are developed by DWR's consultant team (Ramboll Consulting) to describe research related to AEM survey planning or data collection. Research described in the technical memos may also be formally published in a journal publication.

• AEM Test Flights to Evaluate the Bias Signal Caused by Vineyard Trellises Containing Metal

• SkyTEM Instrument Comparison for Airborne EM

2018-2020 AEM Pilot Studies

Three pilot studies were conducted in California from 2018-2020 to support the development of the Statewide AEM Survey Project. The AEM Pilot Studies were conducted in the Sacramento Valley in Colusa and Butte county groundwater basins, the Salinas Valley in Paso Robles groundwater basin, and in the Indian Wells Valley groundwater basin.

Provisional Statement

Data Reports and datasets labeled as provisional may be incomplete and are subject to revision until they have been thoroughly reviewed and received final approval. Provisional data and reports may be inaccurate and subsequent review may result in revisions to the data and reports. Data users are cautioned to consider carefully the provisional nature of the information before using it for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences.

1. This map is intended as a current snapshot of information that can be disclosed publicly regarding tax credit seismic surveys.2. Representation on this map does not guarantee public release and is subject to statutory requirements in effect at the time of acquisition and application for tax credit.3. Release is subject to public notice and permission of private oil and gas mineral estate owner where applicable. Some surveys require clipping to mineral ownership boundaries; actual map extents of released datasets may differ from those shown here. 4. Year label "Released" surveys denote actual release year. Year label "Eligible" and "Issued" denote the year in which the data is eligible for release and distribution under AS 43.55.025(f)(2)(c), most tax credit seismic projects are held confidential for 10 years from completion of initial seismic processing. 5. Map does not include surveys whose initial seismic processing was completed less than 10 years ago but prior to legislative adoption of the disclosure clause of AS 43.55.025(f)(5). Seismic surveys acquired with credits under AS 43.55.023 are not subject to disclosure under AS 43.55.025(f)(5), and cannot be represented here until their confidentiality period has expired.6. Additional qualifying surveys will be added to this map as new tax credit certificates are issued or as changes in confidentiality status allows.