Raw lidar data consist of positions (x, y) and intensity values. They must undergo a classification process before individual points can be identified as belonging to ground, building, vegetation, etc., features. By completing this tutorial, you will become comfortable with the following skills:Converting .zlas files to .las for editing,Reassigning LAS class codes,Using automated lidar classification tools, andUsing 2D and 3D features to classify lidar data.Software Used: ArcGIS Pro 3.3Time to Complete: 60 - 90 minutesFile Size: 57mbDate Created: September 25, 2020Last Updated: September 27, 2024

Lidar (light detection and ranging) imagery provides valuable information in the field of remote sensing, allowing users to determine elevation, vegetation structure, and terrain with remarkable levels of detail. This manual will lead ArcGIS Pro users through the tools and methods needed to access, process, and analyze lidar data through a series of step-by-step tutorials. By completing this series of tutorials, you will be able to: •Manipulate data to create maps and map templates in ArcGIS Pro •Obtain and display lidar imagery •Use ArcGIS Pro tools to process and analyze lidar data •Classify lidar points using different classification methods • Process lidar point clouds to create digital elevation models

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.Lidar data have become an important source for detailed 3D information for cities as well as forestry, agriculture, archaeology, and many other applications. Topographic lidar surveys, which are conducted by airplane, helicopter or drone, produce data sets that contain millions or billions of points. This can create challenges for storing, visualizing and analyzing the data. In this tutorial you will learn how to create a LAS Dataset and explore the tools available in ArcGIS Pro for visualizing lidar data.To download the tutorial and data folder, click the Open button to the top right. This will download a ZIP file containing the tutorial documents and data files.Software & Solutions Used: ArcGIS Pro Advanced 3.x. Last tested with ArcGIS Pro version 3.3. Time to Complete: 30 - 60 minsFile Size: 337 MBDate Created: August 2020Last Updated: March 2024

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

On November 7, 2021, NV5 collected Quality Level 1 (QL1) lidar data across the preliminary CAL FIRE defined fire perimeter for the CZU lightning complex fire in San Mateo and Santa Cruz counties. The technical report for the lidar data collection is available here: https://fuelsmapping.com/czu_postfire_lidar_report From the QL1 postfire lidar, NV5 and Tukman Geospatial developed a set of derivatives. These derivatives are a Digital Terrain Model (DTM), a Digital Surface Model (DSM), a Hillshade derived from the DTM, a lidar intensity image, a Normalized Digital Surface Model (nDSM), a Canopy Cover raster, and a lidar intensity image. The derivatives will be used to study the effects of the CZU wildfire on the natural landscape, forests, and shrublands of Santa Cruz and San Mateo Counties. The lidar derivatives are provided as GeoTiffs available for download from ArcGIS Online and as dynamic image services. Table 1 provides more information (including download information) for the derivatives produced. The GeoTiffs can be used in desktop GIS software packages such as ArcGIS Pro and ERDAS Imagine; the image services can be used in web maps and web mapping applications by both GIS users and non-GIS users. Table 1. lidar derivatives for the CZU lightning fire footprint in San Mateo and Santa Cruz Counties

  Dataset


  Description


  Link to GeoTiff


  Link to Image Service






  Digital Terrain Model (DTM)


  Hydroflattened digital terrain model. Pixel values represent elevation above sea level of the ground.


  https://vegmap.press/czu_postfire_dtm_tif


  https://vegmap.press/czu_postfire_dtm




  Digital Surface Model (DSM)


  Pixel values in the DSM represent elevations above sea level of the ‘highest hit’ surface. The DSM provides elevation above sea level of the top of the tree canopy, the top of buildings, and the top of other features.


  https://vegmap.press/czu_postfire_dsm_tif


  https://vegmap.press/czu_postfire_dsm




  Hillshade


  The hillshade is derived from the DTM and provides a ‘shaded relief’ visualization of the earth’s surface.


  https://vegmap.press/czu_postfire_hillshade_tif


  https://vegmap.press/czu_postfire_hillshade




  Lidar Intensity


  Lidar intensity, scaled to 8-bit resolution.


  https://vegmap.press/czu_postfire_intensity_tif


  https://vegmap.press/czu_postfire_lidar_intensity




  Normalized Digital Surface Model (nDSM)


  In the nDSM, pixel values represent the maximum normalized height in feet of features such as vegetation and structures. For areas with aboveground features, pixel values represent the aboveground height of the tallest part of the feature in the 3x3 foot pixel. For areas with no aboveground features, the nDSM has pixel values of 0. 


  https://vegmap.press/czu_postfire_nDSM_tif


  https://vegmap.press/czu_postfire_nDSM




  Canopy Height Model


  The canopy height model is the normalized digital surface model, with building footprints and a small buffer surrounding them set to 0 normalized height. Building footprint data came from the prefire CHM. The datasheet for the prefire CHM is available here: https://vegmap.press/sc_chm As such, this raster mostly represents the aboveground height of the vegetation canopy.  Note that it also includes some noise (e.g., powerlines and other objects that are not vegetation), as well as some structures that weren't captured as building footprints.


  https://vegmap.press/czu_postfire_chm_tif


  https://vegmap.press/czu_postfire_chm




  Canopy Cover


  This is the Canopy Height Model, thresholded to show pixel values greater than or equal to 15 feet aboveground as 1, and all other areas as 0. As such, it is a proxy for tree canopy cover. 


  https://vegmap.press/czu_postfire_cc_tif


  https://vegmap.press/czu_postfire_cc

Related Datasets: The QL1 point cloud, from which these deliverables were acquired, is available as laz files. The laz files are downloadable by tile. See this datasheet for more information: CZU postfire QL1 point cloudCZU postfire 4-band imagery

These lidar data are processed classified LAS 1.4 files at USGS QL2 covering the District of Columbia. Voids exist in the data due to data redaction conducted under the guidance of the United States Secret Service. This dataset provided as an ArcGIS Image service. Please note, the download feature for this image service in Open Data DC provides a compressed PNG, JPEG or TIFF. The individual LAS point cloud datasets are available under additional options when viewing downloads.

Latest LIDAR Projects required for statewide within New York State. More information for existing LIDAR collections can be found at https://gis.ny.gov/lidar. Last updated 9/5/25. Feature and map services available:https://elevation.its.ny.gov/arcgis/rest/services/indexes/Latest_LiDAR_Collections/FeatureServer https://elevation.its.ny.gov/arcgis/rest/services/indexes/Latest_LiDAR_Collections/MapServer For Historic Collections, see:https://elevation.its.ny.gov/arcgis/rest/services/indexes/Historic_LiDAR_Collections/FeatureServer https://elevation.its.ny.gov/arcgis/rest/services/indexes/Historic_LiDAR_Collections/MapServer Please contact nysgis@its.ny.gov if you have any questions.

Click here to access the data directly from the Illinois State Geospatial Data Clearinghouse. These lidar data are processed Classified LAS 1.4 files, formatted to 2,117 individual 2500 ft x 2500 ft tiles; used to create Reflectance Images, 3D breaklines and hydro-flattened DEMs as necessary. Geographic Extent: Lake county, Illinois covering approximately 466 square miles. Dataset Description: WI Kenosha-Racine Counties and IL 4 County QL1 Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a derived nominal pulse spacing (NPS) of 1 point every 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, U.S Survey Feet and vertical datum of NAVD88 (GEOID12B), U.S. Survey Feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 2,117 individual 2500 ft x 2500 ft tiles, as tiled Reflectance Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema. Ground Conditions: Lidar was collected April-May 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Ayers established a total of 66 ground control points that were used to calibrate the lidar to known ground locations established throughout the WI Kenosha-Racine Counties and IL 4 County QL1 project area. An additional 195 independent accuracy checkpoints, 116 in Bare Earth and Urban landcovers (116 NVA points), 79 in Tall Grass and Brushland/Low Trees categories (79 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data. Users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of its limitations. Acknowledgement of the U.S. Geological Survey would be appreciated for products derived from these data. These LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications.Link Source: Illinois Geospatial Data Clearinghouse

Historic LIDAR Projects within New York State. New York State has access to and distributes many of the older LIDAR collections within New York State. These historic projects have been fully replaced with more recent LIDAR projects. More information for existing LIDAR collections can be found at https://gis.ny.gov/lidar. Last updated 8/2/24.Feature and map services available:https://elevation.its.ny.gov/arcgis/rest/services/indexes/Historic_LiDAR_Collections/FeatureServerhttps://elevation.its.ny.gov/arcgis/rest/services/indexes/Historic_LiDAR_Collections/MapServerFor Latest Collections, see:https://elevation.its.ny.gov/arcgis/rest/services/indexes/Latest_LiDAR_Collections/FeatureServerhttps://elevation.its.ny.gov/arcgis/rest/services/indexes/Latest_LiDAR_Collections/MapServerPlease contact nysgis@its.ny.gov if you have any questions.

(Mature Support) This item is in mature support and is no longer updated. Available for historical reference only. Please visit njgin.nj.gov/edata/elevation for the latest information on elevation products available for download. This is a GIS polygon layer defining the geographic extents for all the LiDAR projects and DEM products in New Jersey. This layer was derived from the original LiDAR extents layer generated by NJDEP 20161230. Features were created from tile extents, project-specific boundaries provided in the deliverables, and county boundaries. Attributes were populated from LiDAR project metadata and fact sheets.

This statewide product was created and will continue to be maintained by the Eastern Shore Regional GIS Cooperative (ESRGC). It's a comprehensive mosaic of the most current LiDAR available for the State of Maryland.The creation and maintenance of this dataset, along with the creation of its services, was funded by the Maryland Department of Information Technology.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/lidar/rest/services/Statewide/MD_statewide_dem_m/ImageServer

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

Tiles of datasets available for the MOA 2015 Photography and Lidar

THIS ITEM WILL BE UNDERGOING MAINTEANCE SOON TO UPGRADE TO VECTOR TILE BASEMAP LAYERS.Web map displaying Wisconsin DNR-produced Digital Elevation Model (DEM) and Hillshade image services, along with their index layer, in formats that are clickable and can be symbolized and filtered. This map can also be used as a starting point to create a new map. To open the web map from DNR's GIS Open Data Portal, click the View Metadata: link to the right of the description, then click Open in Map Viewer.

This dataset is a LAS (industry-standard binary format for storing large point clouds) dataset containing light detection and ranging (LiDAR) data and sonar data representing the beach and near-shore topography of Lake Superior at Minnesota Point, Duluth, Minnesota. Average point spacing of the LAS files in the dataset are as follows: LiDAR, 0.137 meters (m); multi-beam sonar, 1.029 m; single-beam sonar, 0.999 m. The LAS dataset was used to create a 10-m (32.8084 feet) digital elevation model (DEM) of the approximately 5.9 square kilometer (2.3 square mile) surveyed area using the "LAS dataset to raster" tool in Esri ArcGIS, version 10.7. LiDAR data were collected August 10, 2019 using a boat-mounted Optech ILRIS scanner and methodology similar to that described by Huizinga and Wagner (2019). Multi-beam sonar data were collected August 7-11, 2019 using an R2Sonic 2024 sonar unit and methodology similar to that described by Richards and Huizinga (2018). Single-beam sonar data were collected August 27-28, 2019 using a CEESCOPE single-beam echosounder and methodology similar to that described by Wilson and Richards (2006).

This shaded relief image was generated from the lidar-based bare-earth digital elevation model (DEM). A shaded relief image provides an illustration of variations in elevation using artificial shadows. Based on a specified position of the sun, areas that would be in sunlight are highlighted and areas that would be in shadow are shaded. In this instance, the position of the sun was assumed to be 45 degrees above the northwest horizon.The shaded relief image shows areas that are not in direct sunlight as shadowed. It does not show shadows that would be cast by topographic features onto the surrounding surface.Using ERDAS IMAGINE, a 3X3 neighborhood around each pixel in the DEM was analyzed, and a comparison was made between the sun's position and the angle that each pixel faces. The pixel was then assigned a value between -1 and +1 to represent the amount of light reflected. Negative numbers and zero values represent shadowed areas, and positive numbers represent sunny areas. In ArcGIS Desktop 10.7.1, the image was converted to a JPEG 2000 format with values from 0 (black) to 255 (white).See the MassGIS datalayer page to download the data as a JPEG 2000 image file.View this service in the Massachusetts Elevation Finder.MassGIS has also published a Lidar Shaded Relief tile service (cache) hosted in ArcGIS Online.

The District of Columbia government requires a comprehensive range of GIS data and photogrammetric mapping to support a wide variety of applications through the DC GIS program. Due to technology advances, expanding user base needs, and aging data, DC GIS acquired new LIDAR data in spring 2015 to establish a more thorough and better quality core LIDAR dataset The LiDAR data products are suitable for 1 foot (or less) contour generation. Intensity images generated from the RPC data for the DC OCTO 2015 LiDAR project covering approximately 80 square miles, in which its extents cover Arlington County in Washington DC. Intensity is a measure, collected for every point, of the return strength of the laser pulse that generated the point. It is based, in part, on the reflectivity of the object struck by the laser pulse. This project consists of deliverables in accordance with USGS v1.2 specifications and meets or exceeds the level of quality for QL1 (8 points per meter).

Governor's Island Dataset for ArcGIS This archive contains an ArcGIS Pro project with a geodatabase of raster and vector data for Governor's Island, New York City, USA. The SRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.

These lidar data are processed classified LAS 1.4 files at USGS QL1 covering the District of Columbia. Some areas have limited data. The lidar dataset redaction was conducted under the guidance of the United States Secret Service. All data returns were removed from the dataset within the United States Secret Service redaction boundary except for classified ground points and classified water points.

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