Neighborhood Data for Social Change Platform indicators across 10 Policy Areas

LiDAR (Light Detection and Ranging) data of the City of Vancouver and UBC Endowment Lands with an Area of Interest (AOI) covering a total of 134 square kilometers.​Data products includes a classification that defines "bare earth" ground surface, water and of the upper most surface defined by vegetation cover, buildings and other structures.Data accessEach of the 181 polygons on the map or rows in the table provides corresponding link to the data in LAS format (zipped, file sizes range from 16.45MB to 2.74GB).AttributesPoint data was classified as:Unclassified;Bare-earth and low grass;Low vegetation (height <2m);High vegetation (height >2m);Water;Buildings;Other; andNoise (noise points, blunders, outliners, etc) Note​The 2022 LiDAR data is being utilized for initiatives including land management, planning, hazard assessment, (e.g. floods, landslides, lava flows, and tsunamis), urban forestry, storm drainage, and watershed analysis. Data currency​Aerial LiDAR was acquired on September 7th and September 9th, 2022 and is current as of those dates. Data accuracyThe LiDAR data is positioned with a mean density of approximately 49 points per square metreSidelap: minimum of 60% in north-south and east-west directionsVertical accuracy: 0.081 metre (95% confidence level)Coordinate system​The map of grid cells on this portal is in WGS 84 but the LiDAR data in the LAS files are in the following coordinate system:Projection: UTM Zone 10 (Central Meridian 123 West)Hz Datum: NAD 83 (CSRS) 4.0.0.BC.1.GVRDVertical Datum: CGVD28GVRDMetro Vancouver Geoid (HTMVBC00_Abbbyn.zip) Websites for further information City boundary dataset

Abstract LiDAR (Light Detection and Ranging) data was collected for the Geological and Bioregional Assessment Program by Fugro Australia Land Pty Ltd over two mobilisations. The data was acquired at an average density of 1 point per square metre, processed and compiled as LiDAR Classified Data in LAS 1 km tiles and 1 m grid DEM in ESRI ascii 1 km tiles. The total area of survey is 31,780 km². The data was used to develop a Digital Elevation Model and to determine bathymetry for a …Show full descriptionAbstract LiDAR (Light Detection and Ranging) data was collected for the Geological and Bioregional Assessment Program by Fugro Australia Land Pty Ltd over two mobilisations. The data was acquired at an average density of 1 point per square metre, processed and compiled as LiDAR Classified Data in LAS 1 km tiles and 1 m grid DEM in ESRI ascii 1 km tiles. The total area of survey is 31,780 km². The data was used to develop a Digital Elevation Model and to determine bathymetry for a two-dimensional hydrodynamic flood inundation model of the Cooper Creek floodplain. Attribution Geological and Bioregional Assessment Program History LiDAR data was acquired by Fugro Australia Land Pty Ltd over two mobilisations. The first mobilisation was flown from 20 March - 4 April 2019 with an aircraft equipped with a Riegl LMS Q780 LiDAR system. The second mobilisation was flown from 6 - 17 October 2019 with an aircraft equipped with a Riegl LMS Q1560 LiDAR system. Nominal flying height was 2800 mAGL with a swath width of 3200 m. Processing steps: • Project Planning • Aircrew briefing • Acquisition as per requirements of dry Season • Field data QA for integrity and completeness • Downloading raw data • QA Raw data • GNSS-IMU Coupled Solution • Riprocess processing • Geodetic Validation Flight Line matching • Ground Filter • LiDAR data Classification • Product Generation LiDAR Derivates • QA of data • Delivery of Data and reports

Industry standard .las LiDAR (Light Detection And Ranging) classified points. This LiDAR data was collected using Leica's ALS50 Phase I sensor. The raw data was verified in Merrick and Company's LiDAR software (MARS) for complete coverage of the project area, and boresighted to align the flightlines. Raw data files were parsed into manageable client-specific tiles. These tiles were then processed through automated filtering that separates the data into different classification groups: unclassified points, ground points, breakline proximity points, "noise" points and water. The data was next taken into MARS to reclassify the erroneous points that may remain in the LiDAR point cloud after auto-filter.

The horizontal datum used is the North American 1983 HARN. The vertical datum is the North American Vertical Datum of 1988. The projection is Illinois State Plane, Eastern Zone, using US Survey Feet as units.

In order to reduce the download times of these files we have compressed them with LASzip. A free decoder is available from the website http://www.laszip.org.

Neighborhood Data for Social Change Platform indicators across 10 Policy Areas

This dataset has been deprecated. Please see 2017 Countywide LiDAR Point Cloud for more information.Industry standard .las LiDAR (Light Detection And Ranging) classified points. This LiDAR data was collected using Leica's ALS50 Phase I sensor. The raw data was verified in Merrick and Company's LiDAR software (MARS) for complete coverage of the project area, and boresighted to align the flightlines. Raw data files were parsed into manageable client-specific tiles. These tiles were then processed through automated filtering that separates the data into different classification groups: unclassified points, ground points, breakline proximity points, "noise" points and water. The data was next taken into MARS to reclassify the erroneous points that may remain in the LiDAR point cloud after auto-filter.The horizontal datum used is the North American 1983 HARN. The vertical datum is the North American Vertical Datum of 1988. The projection is Illinois State Plane, Eastern Zone, using US Survey Feet as units.

In January 2020, the North Carolina Department of Transportation (NCDOT) began work on the Interstate 26 (I 26) highway widening project that involves a bridge crossing over the French Broad River (FBR) near Asheville, North Carolina. The U.S Geological Survey (USGS) in cooperation with the NCDOT conducted a pre-construction light detection and ranging (lidar) survey of the streambanks within a one-kilometer reach of the FBR at the bridge construction site in November 2019 (Whaling and others, 2023). In December 2020, a canoe-based repeat streambank lidar survey was collected approximately 11 months after construction began, with the purpose to monitor geomorphological changes to the streambank and inform the NCDOT of potential impacts from construction activities. The survey extended from 300 meters (m) upstream to 700 m downstream from the bridge. Two georeferenced lidar scans were collected; one of the right-descending bank and one of the left-descending bank. Three-dimensional points of the streambanks were collected with a canoe-mounted Velodyne VLP-16 laser scanner integrated with an SBG Systems Ellipse2-D inertial navigation systems (INS), which consists of dual Global Navigation Satellite Systems (GNSS) receivers and an inertial motion unit. The lidar scanner creates a “point cloud” of lidar returns and the INS computes the position and orientation of the points in three-dimensional space. The navigation solution from the INS was further improved in post processing. Ground points were identified in each point cloud with a Cloth Simulation Filter (Zhang and others, 2016) implemented in CloudCompare software (CloudCompare, 2023) and classified with code 2 (ground) according to the American Society for Photogrammetry and Remote Sensing (ASPRS) standard lidar point classes (ASPRS, 2011). Water-surface reflections were identified and classified as code 7 (low noise; ASPRS, 2011). All other points in each point cloud were classified as code 1 (unclassified; ASPRS, 2011). The left and right streambank point clouds are provided as separate LAS files, an industry-standard binary format for storing large point cloud datasets. Each LAS file is provided with position and elevation data in three dimensions in units of meters, 8-bit scaled intensity, and the classification code. The data are projected in Universal Transverse Mercator (UTM) coordinate system, zone 17 north, horizontally referenced to the North American Datum of 1983 (NGS, 2018a), 2011 realization (NAD83 2011), and vertically referenced to the North American Vertical Datum of 1988 (NAVD88; NGS, 2018b).

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Realisation of an NTM thanks to IGN data (Lidar HD) available on Geoservices [https://geoservices.ign.fr/lidarhd]

Filter las on: Bridge deck, ground, roads and water surface. The mnt therefore excludes any building as well as vegetation, but keeps roads and bridges.

Realisation with 57 LAS data blocks (39GO) with the following parameters:

—Interpolation: Binning —Cell assignment: Average — Linear => Cell Size of 0.5

The Merced Vernal Pools and Grassland Reserve is 6,500 acres of protected habitat adjacent to the University of California Merced containing rare and endangered species and a unique seasonal wetland habitat. These data were gathered to be used for hydrological modelling on the Reserve for potential restoration projects and to be made public for other researchers who may find very high resolution topographical information useful for their work. This dataset contains a Digital Elevation Model created from 8 field survey days of Aerial LiDAR Scanning (ALS) with a small Unmanned Aerial System (sUAS).

Methods Work Completed by Researchers at University of California, Merced under the direction of Dean/Director/Professor Joshua H. Viers | Vicelab and CITRIS Aviation

Spatial Reference: WGS 1984 UTM Zone 10N / WGS84 Geoid

Units: Meters

Equipment: DJI M600 Pro with Phoenix Aerial Systems AL3-32 LiDAR

Software: Phoenix LiDAR Systems SpatialSuite 4.0.3, LasTools, ArcGIS Pro 2.4, Litchi, ArduPilot Mission Planner

Field Crew/Processing: Michael Kalua (sUAS Pilot/Mission Planning/Sensor Operator/Data Processing), Andreas Anderson (sUAS Pilot/Mission Planning/Sensor Operator), Daniel Gomez (Sensor Operator), Hayden Namgostar (Sensor Operator)

Field Methods: An RTK reference station was set up before each field day over a previously-surveyed benchmark near the entrance of the Reserve, which would continuously send RTK corrections to the LiDAR system over an internet connection service. Before flight the LiDAR system was allowed at least 15 minutes to reach thermal equilibrium and for the onboard Intertial Measurement Unit (IMU) to get a fix on the sensor's position and attitude. At the beginning of each set of flights the Pilot in Command (PIC) would perform a manual takeoff and IMU calibration maneuvers (straight-and-level flight and figure-eights) as per Phoenix LiDAR System's recommended procedures. Once the manuevers were completed and the Sensor Operator determined IMU attitude and position uncertainties were below threshold (0.003- typical values ranged an order of magnitude lower) the PIC would begin the automated waypoint mission via Litchi. During flight, the Sensor Operator would ensure the scanner was operational, that the IMU uncertainties were below margin, and address any potential error messages. In the event of errors, the PIC would bring the sUAS back and the section would be re-surveyed after the issues were addressed.

Processing Methods: The raw flightlines were fused using Phoenix SpatialExplorer 4.0.3 to include only the straight-and-level flightlines over the region of interest. The output were individual flightline .las point clouds conforming to LAS 1.4 format. These flightlines were then passed through a noise filter using LasNoise to remove any "birds" or unwanted noise. Using LasTools these noise-removed flightlines were then tiled, classified into ground/non-ground points, and rasterized into 0.25-meter Digital Surface Models (DSM) containing all points and Bare-Earth Digital Elevation Models (DEM) containing only ground-classified points. These tiled raster outputs were then mosiaced together in ArcGIS Pro.

Please reach out to Michael Kalua (mkalua@ucmerced.edu) for any questions about this dataset.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Variables for the Data Explorer, unpivoted to the format aligned to our final schema.

*****PLEASE NOTE: THIS SERVICE IS NOT CONSIDERED AUTHORITATIVE*****For authoritative case and death counts please see the data in the Department of Public Health's LA County COVID-19 Surveillance Dashboarddashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/Several tables of the data are made available to download, including the current daily count, by selecting a table from the menu on the left side of the dashboard and clicking the "Download his table" button at the top of the table's page.*********************************************************************************This is the hosted feature layer VIEW for Historic case counts that is being updated from the SDE data source through automated scripting.Additionally, this feature layer contains the Accumulated Cases and Death counts. To just view the accumulated totals, apply a filter for Community = County of Los Angeles.The script runs daily at 8pm and finishes around 8:15pm.This view layer replaces the older version. Please update your data source for historic or accumulated COVID-19 cases with this feature layer and remove the older version from your webmaps and applications. Please contact the GIS Unit with questions at gis@ceooem.lacounty.gov.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Variables for the Data Explorer, unpivoted to the format aligned to our final schema.

These data are superseded by the following dataset, which has superior alignment quality: https://doi.org/10.18739/A26688K9D. These Terrestrial Laser Scanning (TLS) data were collected on February 22, 2020 at the Multidisciplinary Drifting Observatory for Arctic Climate (MOSAiC) Expedition. The MOSAiC Expedition continuously made in-situ measurements on a collection of drifting sea ice floes In the Arctic Ocean from October 2019 to May 2020. TLS, also known as Terrestrial Light Detection and Ranging (LiDAR), is an active remote sensing modality in which a tripod-mounted, rotating laser emitter and detector (the TLS sensor) scans the surroundings emitting pulses of light and tracking the time it takes for a pulse to return. From the time of flight and the orientation of each pulse, the sensor creates a point cloud of the surroundings. These point clouds have been written out as LAS 1.4 files. This dataset consists of nine point clouds collected from scan positions in the vicinity of the Remote Operated Vehicle (ROV) tent on February 22, 2020. This dataset contains 9 Terrestrial Laser Scans (TLS) of snow and sea ice on the MOSAiC Expedition. There were moderate blowing snow conditions on the day these data were collected. These data are used by Clemens-Sewall et al. 2021 to demonstrate the efficacy of FlakeOut, a filter designed to remove wind-blown snowflakes from TLS data. They were collected by Ian Raphael and processed by David Clemens-Sewall. These data are provided in a directory structure such that they can be processed and analyzed by FlakeOut (Clemens-Sewall 2021: https://github.com/davidclemenssewall/flake_out/tree/v1.0.0 or https://zenodo.org/record/5657286#.YZRRcLtOlH4). To download these data in this directory structure, please press 'Download All'.

This bar chart depicts PERM case filings at Las Positas College sorted by the citizenship of the graduates. The filter by major feature provides a deeper understanding of the international diversity of graduates who are being sponsored by employers in the U.S.

These data were collected by the National Oceanic Atmospheric Administration National Geodetic Survey Remote Sensing Division using an OPTECH ALTM system on June 8, 2008. The data includes topographic data in an LAS format file. The NOAA Office for Coastal Management (OCM) processed the data to bare earth (class 2) and water points classified (class 9). An automated filter was used, followed by semi-manual classification and bridge removal. Original contact information: Contact Org: National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS), Remote Sensing Division Title: Chief, Remote Sensing Division Phone: 301-713-2663

This dataset includes topographic elevations (in meters) surrounding and bathymetric elevations within the upper Delaware River (USA). Bathymetric lidar data was acquired using the Experimental Advanced Airborne Research Lidar, version B. The EAARL-B is a successor instrument to the original EAARL bathymetric LiDAR sensor developed for mapping coral reef environments in clear water, but subsequently used in river mapping. Both the original EAARL and the EAARL-B are small footprint, full waveform digitizing, green wavelength (532nm) airborne laser scanners, capable of acquiring laser returns from submerged as well as subaerial topography. Improvements from the original sensor include increased sample density, increased pulse rate, enhanced deep and shallow bathymetry performance, and improved data processing hardware. The EAARL-B sensor differs from the original in a 10x laser power increase, and incorporation of three shallow water receiving channels, as well as a deep water receiving channel. The EAARL-B splits each generated laser pulse into 3 pulses that are spaced 1.6m apart across the 250 m flight track and 2.0 m along track when flown at the nominal altitude of 300m above ground level at 100 knots per hour. Lidar data acquired with the EAARL-B was processed using the Airborne Lidar Processing System (ALPS) purpose-built software to analyze waveforms (for shallow bathymetry, deep bathymetry, and topography), geo-reference laser pulses, filter the resulting point cloud for noise, and to export standard lidar data format files (LAS). Additional processing included editing the point clouds to remove water surface and volume returns, off-ground objects, and erroneous returns. Topographic lidar point clouds from the Pennsylvania PAMAP program collected outside of the river channel were merged with the EAARL-B point clouds to create a complete topobathymetric elevation model of the active river area and riparian zone. Areas where the EAARL bathymetric lidar failed to map the river bottom (voids) were visually interpreted and the boundaries were digitized into a vector spatial data layer. Data is stored and processed in ALPS in 2 km x 2km tiles, organized into larger 10 km x 10 km tiles. The EAARL-B sensor was flown out of Salisbury, MD by the sensor developer (C. Wayne Wright) using USGS-owned, fixed-wing aircraft. Flights were conducted November 26, 28, and 29, and December 6, 14 and 20, 2012. Each flight day included multiple passes over a section of the river, and each river section may have been flown on multiple days, resulting in densifying the data collection over the entire river area flown, however, some river sections may have been flown with more over-passes than others. In total, over 200 river miles were surveyed (from Trenton, NJ to the reservoirs on the East and West Branch Delaware River), however, the data presented herein focuses only on the 122-river mile stretch between Hancock, NY, and Portland, PA.

This bar chart depicts PERM case filings at Universidad Adventista de las Antillas sorted by the citizenship of the graduates. The filter by major feature provides a deeper understanding of the international diversity of graduates who are being sponsored by employers in the U.S.