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

Dropbox link to download the Triangulated Irregular Network (TIN) derived from Network Mapping Ltd. LiDAR data collection, flown September 4, 5 2011.

A digital elevation model (DEM) of a portion of the National Park Service Southeast Coast Network's Cape Hatteras National Seashore in North Carolina, post-Nor'Ida (November 2009 nor'easter), was produced from remotely sensed, geographically referenced elevation measurements cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over the area using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 50 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of +/-15 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When subsequent elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

For more information on Lidar science and the Experimental Advanced Airborne Research Lidar (EAARL) system and surveys, see http://ngom.usgs.gov/dsp/overview/index.php and http://ngom.usgs.gov/dsp/tech/eaarl/index.php .

Components: Hardware: Includes mobile mapping systems, sensors, and other equipment Software: Includes software for data collection, processing, and visualization Services: Includes data collection, processing, and analysis servicesSolutions: Location-based: Provides location-based information and services Indoor mapping: Creates maps of indoor spaces Asset management: Helps manage assets and track their location 3D mapping: Creates 3D models of buildings and infrastructureApplications: Land surveys: Used for surveying land and creating maps Aerial surveys: Used for surveying areas from the air Real estate & construction: Used for planning and designing buildings and infrastructure IT & telecom: Used for network planning and management Recent developments include: One of the pioneers in wearable mobile mapping technology, NavVis, revealed the NavVis VLX 3, their newest generation of wearable technology. As the name suggests, this is the third version of their wearable VLX system; the NavVis VLX 2 was released in July of 2021, which is over two years ago. In their news release, NavVis emphasises the NavVis VLX 3's improved accuracy in point clouds by highlighting the two brand-new, 32-layer lidars that have been "meticulously designed and crafted" to minimise noise and drift in point clouds while delivering "high detail at range.", According to the North American Mach9 Software Platform, mobile Lidar will produce 2D and 3D maps 30 times faster than current systems by 2023., Even though this is Mach9's first product launch, the business has already begun laying the groundwork for future expansion by updating its website, adding important engineering and sales professionals, relocating to new headquarters in Pittsburgh's Bloomfield area, and forging ties in Silicon Valley., In order to make search more accessible to more users in more useful ways, Google has unveiled a tonne of new search capabilities for 2022 spanning Google Search, Google Lens, Shopping, and Maps. These enhancements apply to Google Maps, Google Shopping, Google Leons, and Multisearch., A multi-year partnership to supply Velodyne Lidar, Inc.'s lidar sensors to GreenValley International for handheld, mobile, and unmanned aerial vehicle (UAV) 3D mapping solutions, especially in GPS-denied situations, was announced in 2022. GreenValley is already receiving sensors from Velodyne., The acquisition of UK-based GeoSLAM, a leading provider of mobile scanning solutions with exclusive high-productivity simultaneous localization and mapping (SLAM) programmes to create 3D models for use in Digital Twin applications, is expected to close in 2022 and be completed by FARO® Technologies, Inc., a global leader in 4D digital reality solutions., November 2022: Topcon donated to TU Dublin as part of their investment in the future of construction. Students learning experiences will be improved by instruction in the most cutting-edge digital building techniques at Ireland's first technical university., October 2022: Javad GNSS Inc has released numerous cutting-edge GNSS solutions for geospatial applications. The TRIUMPH-1M Plus and T3-NR smart antennas, which employ upgraded Wi-Fi, Bluetooth, UHF, and power management modules and integrate the most recent satellite tracking technology into the geospatial portfolio, are two examples of important items.. Key drivers for this market are: Improvements in GPS, LiDAR, and camera technologies have significantly enhanced the accuracy and efficiency of mobile mapping systems. Potential restraints include: The initial investment required for mobile mapping equipment, including sensors and software, can be a barrier for small and medium-sized businesses.. Notable trends are: Mobile mapping systems are increasingly integrated with cloud platforms and AI technologies to process and analyze large datasets, enabling more intelligent mapping and predictive analytics.

High Resolution LiDAR elevation and nearshore bathymetry data for Hog Island, Northampton County, VA, collected on October 11, 2011 on behalf of the USACE Engineer Research and Development Center using the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. RGB air photo imagery is provided as a separate VCRLTER dataset. Two data entities are included here: (1) the LiDAR point cloud (approximate point density of 100 points per square meter [denser over structures and dense vegetation], average point spacing of 0.48 m.) contained in a mosaic of 211 LAS files (standard LiDAR LAS file format); and (2) a polygon INDEX shapefile showing the footprint of each LAS file and containing a summary description of each LAS file in the attribute table (LAS file name, point count, point spacing, and minimum and maximum elevation). Note that the two co-collected 2011 USACE datasets (LiDAR and RGB ) are in different coordinate systems: (A) the horizontal and vertical units of the LiDAR data are in US feet, not meters. (B) the horizontal units of the associated RGB mosaic images are in [standard] meters. Also Note that THESE ARE VERY LARGE DATASETS and download should not be attempted unless you have a fast network connection and plenty of disk space. The LAS file data collection is 7.3 GB compressed (11.7 GB uncompressed). The RGB imagery mosaic data collection is 12.9 GB compressed (30.8 GB uncompressed).

Manhole Lidar Mapping Robot Market Outlook

According to our latest research, the global Manhole Lidar Mapping Robot market size reached USD 418.2 million in 2024, driven by rapid advancements in robotics and Lidar technology, as well as increasing demand for efficient underground infrastructure inspection. The market is projected to grow at a robust CAGR of 15.7% from 2025 to 2033, reaching a forecasted value of USD 1,355.4 million by 2033. This impressive growth is underpinned by the urgent need for smart city solutions, infrastructure modernization, and heightened safety standards for municipal and utility networks globally.

The primary growth factor for the Manhole Lidar Mapping Robot market is the surging global investment in urban infrastructure and smart city initiatives. Urbanization is accelerating at an unprecedented pace, especially in emerging economies, leading to increased pressure on aging underground utility networks, sewer systems, and drainage infrastructure. Traditional inspection methods are labor-intensive, costly, and often expose workers to hazardous environments. The adoption of advanced Lidar mapping robots not only enhances the accuracy and speed of inspections but also significantly reduces operational risks and costs. These robots are equipped with cutting-edge Lidar sensors and autonomous navigation systems, enabling municipalities and utility companies to carry out comprehensive inspections with minimal human intervention, thus ensuring the longevity and safety of critical infrastructure assets.

Another significant driver of market expansion is the ongoing evolution of Lidar technology itself. The transition from 2D to 3D Lidar sensors, coupled with the integration of artificial intelligence and machine learning algorithms, has revolutionized the capabilities of manhole mapping robots. These technological advancements have enabled robots to capture high-resolution, three-dimensional visualizations of underground spaces, facilitating precise defect detection, structural assessments, and predictive maintenance. The growing preference for data-driven asset management among municipal authorities and private utility operators is fueling demand for these advanced Lidar mapping solutions. Furthermore, the increasing availability of hybrid systems that combine multiple sensor modalities is broadening the application scope of these robots, making them indispensable tools for modern infrastructure management.

Moreover, stringent regulatory mandates and safety standards are compelling stakeholders to adopt innovative inspection technologies. Regulatory bodies across North America, Europe, and Asia Pacific are enforcing stricter guidelines for the maintenance and monitoring of underground utilities, aiming to prevent accidents, environmental contamination, and service disruptions. Manhole Lidar Mapping Robots provide a compliant, efficient, and non-invasive solution for meeting these regulatory requirements. Additionally, the rising awareness about environmental sustainability and the need to minimize urban disruptions during inspection activities are further catalyzing market growth. These factors, combined with ongoing R&D investments and partnerships between technology providers and government agencies, are expected to sustain the momentum of the market in the coming years.

Regionally, North America and Europe are currently leading the market, driven by mature infrastructure networks, high adoption rates of automation, and proactive regulatory frameworks. However, the Asia Pacific region is poised for the fastest growth, supported by rapid urbanization, significant infrastructure development projects, and increasing government focus on smart city technologies. Latin America and the Middle East & Africa are also witnessing rising adoption, albeit at a more gradual pace, as awareness and investment in advanced inspection technologies continue to grow. Overall, the global Manhole Lidar Mapping Robot market is set to experience robust expansion, with technology innovation and infrastructure modernization serving as the primary catalysts.

The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 1m spatial resolution.The DTM (Digital Terrain Model) is produced from the last or only laser pulse returned to the sensor. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface. Produced by the Environment Agency in 2022, the DTM is derived from a combination of our Time Stamped archive and National LIDAR Programme surveys, which have been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged. The 2022 LIDAR Composite contains surveys undertaken between 6th June 2000 and 2nd April 2022. Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the LIDAR composite.DEFRA Data Services Platform Metadata URLDefra Network WMS server provided by the Environment Agency

This dataset provides LiDAR derived stream locations for Calvert and Hecate Islands, British Columbia. Stream locations were delineated from a 3 m digital elevation model (DEM). For each stream segment, the dataset includes a unique identifier and Strahler stream order assignment.

This dataset is the result of “traditional” hydrological modeling conducted using the 2012 and 2014 LiDAR-based topographically complete bare earth DEM with a 10 m buffer around the coastline to ensure all modeled streams reach the ocean. After extraction, stream networks were clipped to the shoreline of the Island.

Although this LiDAR derived stream network represents a large improvement over the best alternative stream map for the area – in terms of spatial accuracy and resolution – appropriate caution should be used when interpreting the modeled stream locations, given the methodology used.

Hydrologic modelling of drainage networks from digital elevation models can produce drainage systems of varying detail (density and length of small tributary streams) depending on the thresholds used to define initiation of streams. We defined a stream initiation threshold by selecting a “net flow accumulation value” that best agreed with stream occurrence and initiation observed on aerial imagery and in the field. Net flow accumulation is obtained by taking the Log (base 10) of the flow accumulation raster produced during the hydrologic modelling exercise. We examined net flow accumulation values of 2.0 through 4.0 (in increments of 0.5), ultimately selecting a single value of 3.0 because it appeared to best determine stream initiation for the overall study area. Based on our field observations – which were opportunistic and of limited extent – higher values tend to omit observed surface channels and lower values tend to predict streams where surface channels are not observed. With a threshold value of 3.0, headwater stream reaches alternate between surface and subsurface flow, depending on local soil conditions. Choosing a single value for the entire landscape likely means that streams are over predicted in some areas and under predicted in others, depending on local conditions (e.g., terrain, soil type and depth). Modeling stream initiation as a function of local conditions could improve the stream network map but would require a large and representative sample of field observations.

Dataset Contributors: Hakai Institute, Santiago Gonzalez Arriola, Gordon W. Frazer, Ian Giesbrecht, Bill Floyd, Keith Holmes.

A high resolution LiDAR derived hillshade facilitates the visualization of the topography of a landscape at a variety of scales. This hillshade which was created from a LiDAR derived hydro-flattened bare earth digital elevation model shows the signal returns without any vegetation or human-made structures. In addition to that, bodies of water have been smoothed. This layer may be used on its own or in conjunction with other data.The Sonoma County Vegetation Mapping and LiDAR Program. and the University of Maryland (under grant NNX13AP69G from NASA’s Carbon Monitoring System, Dr. Ralph Dubayah, PI) contracted LiDAR and orthophoto data collection for all of Sonoma County in late 2013. Also included in the data collection were two areas in Mendocino County - the Soda Spring Creek-Dry Creek Watershed and Lake Mendocino. This fine scale data will help provide an accurate, up-to-date inventory of the county’s landscape features, ecological communities and habitats. Project funders include: NASA, the University of Maryland, the Sonoma County Agricultural Preservation and Open Space District, the Sonoma County Water Agency, the California Department of Fish and Wildlife, the United States Geological Survey, the Sonoma County Information Systems Department, the Sonoma County Transportation and Public Works Department, the Nature Conservancy, and the City of Petaluma.The hillshade is a greyscale image showing topography in the landscape. In this case it is created from a LiDAR derived hydro-flattened bare earth digital elevation model illuminated by hypothetical light source shining from the north west. A hydro flattened bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. In addition bodies of waters 2acres or larger have been smoothed.The DEM used to create this hillshade is described as a bare earth digital elevation model (DEM) representing the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from LiDAR data using triangulated irregular network (TIN) processing of the ground point returns. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average elevation for that area.

The Virginia Geographic Information Network (VGIN) contracted with Sanborn to provide LiDAR mapping services for Accomack and Northampton counties on the eastern shore of Virginia in the March of 2010. Utilizing multi-return systems, Light Detection and Ranging (LiDAR) data in the form of 3-dimensional positions of a dense set of mass points was collected for approximately 1090 square miles. Of...

LAS classified point cloud from Network Mapping Ltd. LiDAR data collection, flown September 4, 5 2011.A dropbox link to Airdrie's 2011 Classified LiDAR data in .LAS format.

NCALM Seed. PI: Louise J.E. Slater, University of St Andrews, Scotland, UK. The survey area consists of an irregular polygon located 90 kilometers southeast of Alamogordo, NM. Lidar data were collected to investigate the roles of floodplains in fluvial networks.

Micro-LiDAR Indoor Mapper Market Outlook

According to our latest research, the global Micro-LiDAR Indoor Mapper market size reached USD 1.26 billion in 2024, driven by surging demand for precise indoor mapping solutions across industries. The market is exhibiting a robust compound annual growth rate (CAGR) of 18.2% and is forecasted to reach USD 5.06 billion by 2033. This strong growth is primarily fueled by rapid advancements in LiDAR technology, increasing adoption of automation in industrial and commercial settings, and the expanding need for high-accuracy spatial data for facility management and security applications.

A key growth factor for the Micro-LiDAR Indoor Mapper market is the escalating demand for real-time, high-resolution, and three-dimensional mapping in complex indoor environments. As businesses and organizations increasingly focus on optimizing facility management, operational efficiency, and asset tracking, the ability to generate accurate indoor spatial data has become indispensable. Micro-LiDAR devices, with their compact design and ability to deliver detailed point cloud data, are being rapidly adopted in sectors such as logistics, retail, healthcare, and manufacturing. The proliferation of smart buildings and the integration of LiDAR-based solutions with building information modeling (BIM) systems are further amplifying the market’s growth trajectory, enabling seamless digital transformation of indoor spaces.

Technological advancements in LiDAR sensors, including the miniaturization of components and the development of solid-state and hybrid LiDAR systems, are significantly enhancing the performance and affordability of Micro-LiDAR Indoor Mappers. These innovations have enabled the deployment of LiDAR solutions in previously inaccessible or cost-prohibitive environments, such as small- and medium-sized commercial properties and residential complexes. Moreover, the integration of artificial intelligence and machine learning algorithms with LiDAR data processing is facilitating automated feature recognition, anomaly detection, and predictive maintenance, thereby expanding the scope of applications and driving market penetration across new verticals.

The Micro-LiDAR Indoor Mapper market is also benefiting from the growing emphasis on safety, security, and compliance in industrial and governmental facilities. Regulatory mandates for accurate facility mapping, emergency response planning, and asset tracking are compelling organizations to invest in advanced mapping technologies. Additionally, the rise of Industry 4.0 and the Internet of Things (IoT) is fostering the convergence of LiDAR systems with other sensor networks, enabling real-time monitoring, automation, and enhanced situational awareness. As a result, the market is witnessing heightened demand from sectors such as warehousing, manufacturing, utilities, and public infrastructure.

Regionally, North America leads the global Micro-LiDAR Indoor Mapper market, accounting for over 38% of the total revenue in 2024, followed by Europe and Asia Pacific. The United States, in particular, is witnessing rapid adoption of LiDAR-based indoor mapping solutions in commercial, industrial, and government sectors due to strong technological infrastructure and supportive regulatory frameworks. Meanwhile, Asia Pacific is poised for the highest CAGR during the forecast period, driven by rapid urbanization, smart city initiatives, and increasing investments in digital infrastructure across countries such as China, Japan, and South Korea.

Product Type Analysis

The Micro-LiDAR Indoor Mapper market by product type is segmented into Handheld Micro-LiDAR Mappers, Mounted Micro-LiDAR Mappers, and Portable Micro-LiDAR Mappers. Among these, Handheld Micro-LiDAR Mappers have emerged as the most widely adopted segment in 2024, owing to their flexibility, ease of use, and ability to access confined or complex indoor spaces. These devices are particularly favored for quick site surveys, facility

description: Terrapoint, on behalf of multiple agencies, collected topographic lidar of the Lower Columbia River area. Field data collection took place between the dates of January 10th and February 20th, 2005. The control network and checkpoint surveys were performed from January 4th to February 12th, 2005. The project area covers approximately 890 square miles along the Columbia River, from the Bonneville Dam to the Pacific Ocean. A total of 431 flightlines were required to cover the project area flightlines. Terrapoint used a 40 kHz Airborne Laser Terrain Mapping System ALTMS sensor attached to a Navajo Twin-engin aircraft (C-FVZM). The mission was flown at 3500 feet above ground level at an average speed of 140 knots. The system consists of a 36 degree full scan angle laser, a Trimble 4700 GPS receiver and a Honeywell H764 IMU unit. The nominal flightline spacing was 1070 feet with 30 to 50% sidelap. The surveyed and processed data was received divided by funding agency. The agency responsible for funding each tile is represented as a number (0:COE, 1:DNR, 2:DOGAMI, 3:DOI) in the "User Data" field of each LAS file.; abstract: Terrapoint, on behalf of multiple agencies, collected topographic lidar of the Lower Columbia River area. Field data collection took place between the dates of January 10th and February 20th, 2005. The control network and checkpoint surveys were performed from January 4th to February 12th, 2005. The project area covers approximately 890 square miles along the Columbia River, from the Bonneville Dam to the Pacific Ocean. A total of 431 flightlines were required to cover the project area flightlines. Terrapoint used a 40 kHz Airborne Laser Terrain Mapping System ALTMS sensor attached to a Navajo Twin-engin aircraft (C-FVZM). The mission was flown at 3500 feet above ground level at an average speed of 140 knots. The system consists of a 36 degree full scan angle laser, a Trimble 4700 GPS receiver and a Honeywell H764 IMU unit. The nominal flightline spacing was 1070 feet with 30 to 50% sidelap. The surveyed and processed data was received divided by funding agency. The agency responsible for funding each tile is represented as a number (0:COE, 1:DNR, 2:DOGAMI, 3:DOI) in the "User Data" field of each LAS file.

STAQS_AircraftRemoteSensing_NASA-G3_HALO_Data is the remotely sensed trace gas data for the NASA Gulfstream III aircraft taken by the High Altitude Lidar Observatory (HALO) instrument as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.

STAQS_AircraftRemoteSensing_JSC-GV_HSRL2_Data is the remotely sensed trace gas data for the JSC Gulfstream V aircraft taken by the High Spectral Resolution Lidar-2 (HSRL-2) as part of the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.

With the rapid expansion of railway networks globally, ensuring rail infrastructure safety through efficient detection methods has become critical. Traditional inspection systems face limitations in flexibility, adaptability to adverse weather, and multifunctional integration. This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. The system employs a lightweight rail inspection vehicle equipped with dual LiDARs and an Astro camera, synchronized with an unmanned aerial vehicle (UAV) carrying industrial-grade LiDAR. We propose an improved LiDAR odometry and mapping with sliding window (LOAM-SLAM) algorithm enables real-time dynamic mapping, while an optimized iterative closest point (ICP) algorithm achieves high-precision point cloud registration and colorization. For intrusion detection, a You Only Look Once version 3 (YOLOv3)-ResNet fusion model achieves a recall rate of 0.97 and precision of 0.99. The system’s innovative design and technical implementation offer significant improvements in railway track inspection efficiency and safety. This work establishes a new paradigm for adaptive railway maintenance in complex environments.

With the rapid expansion of railway networks globally, ensuring rail infrastructure safety through efficient detection methods has become critical. Traditional inspection systems face limitations in flexibility, adaptability to adverse weather, and multifunctional integration. This study proposes a ground-air collaborative multi-source detection system that integrates 3D light detection and ranging (LiDAR)-based point cloud imaging and deep learning-driven intrusion detection. The system employs a lightweight rail inspection vehicle equipped with dual LiDARs and an Astro camera, synchronized with an unmanned aerial vehicle (UAV) carrying industrial-grade LiDAR. We propose an improved LiDAR odometry and mapping with sliding window (LOAM-SLAM) algorithm enables real-time dynamic mapping, while an optimized iterative closest point (ICP) algorithm achieves high-precision point cloud registration and colorization. For intrusion detection, a You Only Look Once version 3 (YOLOv3)-ResNet fusion model achieves a recall rate of 0.97 and precision of 0.99. The system’s innovative design and technical implementation offer significant improvements in railway track inspection efficiency and safety. This work establishes a new paradigm for adaptive railway maintenance in complex environments.

This data package includes two related data files that can be used as input for habitat network analyses on amphibians using a specific habitat network analysis tool (HNAT; v0.1.2-alpha):

1. AmphibianHabitatNetwork_Parameters.xlsx
2. BiotopeMap_GothenburgRegion_withPondsRoadsAndBuildings.tif

HNAT is a plugin for the open-source Geographic Information System QGIS (https://qgis.org/en/site/). HNAT can be downloaded at https://github.com/SMoG-Chalmers/hnat/releases/tag/v0.1.2-alpha. To run the habitat network analyses based on the input data provided in this package one must install the plugin HNAT into QGIS. This software has been created by Chalmers within a research project financed by the Swedish government research council for sustainable development, Formas (FR -2021/0004), within the framework of the national research program "From research to implementation for a sustainable society 2021". The Excel-file contains the parameters for amphibians and the GeoTiff-file is representing a biotope raster map covering the Gothenburg region in western Sweden. SRID=3006 (Sweref99 TM). Pixel size =10x10 metres. The pixel values of the biotope map correspond to the biotope codes listed in the in the parameter file (see column “BiotopeCode”). For each biotope the parameter file holds biotope specific parameter values for two alternative amphibian models denoted “Amphibians_NMDWater_ponds” and Amphibians_NMDWater_ponds_NoFriction”. The two alternative parameter settings can be used to demonstrate the difference in model prediction with or without the assumption that amphibian movements are affected by barrier effects caused by roads, buildings and certain biotopes biotope types. The “NoFriction” version assumes that amphibian dispersal probability declines exponentially with increasing Euclidian distance whereas the other set assumes dispersal to be affected by barriers. Read the readme file for details on each parameter provided in the parameter file.

The GeoTiff-file is a biotope mape which has been created by combining a couple of publicly available geodata sets. As a base for the biotope map the Swedish land cover map NMD was used (https://geodata.naturvardsverket.se/nedladdning/marktacke/NMD2018/NMD2018_basskikt_ogeneraliserad_Sverige_v1_1.zip). To achieve a greater cartographic representation of small ponds, streams, buildings and transport infrastructure relevant for amphibian dispersal, reproduction and foraging, NMD was complemented by information from a number of vector layers. In total, 20 new biotope classes representing buildings of different height ranging from less than 5 m up to 100 m, were added to the basic land cover map. The heights were obtained by analyzing the LiDAR data provided by Swedish Land Survey (for details see Berghauser Pont et al., 2019). The data was rasterized and added on top of existing pixels representing buildings in the Swedish land cover map. The roads were separated into 101 new biotope classes with different expected number of vehicles per day. Instead of using statistics from the Swedish Transport Administration on observed number of vehicles per day relative traffic volumes were predicted based on angular betweenness centrality values calculated from the road network using PST (Place Syntax Tool, Stavroulaki et al. 2023). PST is an open-source plugin for QGIS (https://www.smog.chalmers.se/pst). Traffic volumes are expected to be correlated to the centrality values (Serra and Hillier, 2019). The vector layer with the centrality values was buffered by 15 m prior to rasterization. After that the new pixel values were added to the basic Land cover raster in sequence following the order of centrality values. Information on small streams with a maximum width of 6 m was added from a vector layer of Swedish streams (https://www.lantmateriet.se/en/geodata/geodata-products/product-list/topography-50-download-vector/). These lines where rasterized and added to the land cover raster by replacing the underlaying pixel values with new class specific pixel values. Small pondlike waterbodies was identified from the NMD data selecting contiguous fragments of the original NMD biotope class 61 with a smaller area than 1 hectare. Pixels representing the smaller water bodies was then changed to 201.

References Berghauser Pont M, Stavroulaki G, Bobkova E, et al. (2019). The spatial distribution and frequency of street, plot and building types across five European cities. Environment and Planning B: Urban analytics and city science 46(7): 1226-1242. Serra M and Hillier B (2019) Angular and Metric Distance in Road Network Analysis: A nationwide correlation study. Computers, Environment and Urban Systems 74: 194-207. Stavroulaki I, Berghauser Pont M, Fitger M, et al. (2023) PST Documentation_v.3.2.5_20231128, DOI:10.13140/RG.2.2.32984.67845.

A bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from LiDAR data using triangulated irregular network (TIN) processing of the ground point returns. Hydro-flattened Bare Earth DEMs represent water bodies in a cartographically and aesthetically pleasing manner, and are not intended to accurately map water surface elevations. In a Hydro-flattened DEM, water surfaces are flat and level for lakes with a greater area than two acres, and gradated for rivers or other long impoundments (e.g., reservoirs) that are wider than 100 feet, and tidal areas. Any existing island larger than one acre was be delineated. Water surface edge elevations were at or below the immediately surrounding terrain. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average elevation for that area. The specified coordinate system for this dataset is California State Plane Zone II (FIPS 0402), NAD83 (2011), with units in US Survey Feet for horizontal, and vertical units are NAVD88 (12A) US Survey Feet. The dataset encompasses a portion of Sonoma County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Consortium.