The point cloud was delivered with data in the following classifications: Class 1 - Processed but Unclassified; Class 2 - Bare Earth Ground; Class 3 - Low Vegetation; Class 4 - Medium Vegetation; Class 5 - High Vegetation, Class 6 - Buildings; Class 7 - Low Point (Noise); Class 9 - Water; Class 17 - Bridge Decks; Class 18 - High Noise; Class 20 - Ignored Ground.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building 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 Building 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.The model is trained with classified LiDAR that follows the 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 6 BuildingApplicable 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 - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} 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-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington 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

Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows. Trees have a complex 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.Using the modelFollow the guide to use the model. The model can be used with the 3D Basemaps solution and 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.InputThe model accepts unclassified point clouds with the attributes: X, Y, Z, and Number of Returns.Note: This model is trained to work on unclassified point clouds that are in a projected coordinate system, where 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 provided deep learning 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.This 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. 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 2 classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThis model is expected to work well in all regions globally, with an exception of mountainous regions. However, results can vary for datasets that are statistically dissimilar to training data.Model 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. Class Precision Recall F1-score Trees / High-vegetation (5) 0.975374 0.965929 0.970628Training dataThis model is trained on a subset of UK Environment Agency's open dataset. The training data used has the following characteristics: X, Y and Z linear unit meter Z range -19.29 m to 314.23 m Number of Returns 1 to 5 Intensity 1 to 4092 Point spacing 0.6 ± 0.3 Scan angle -23 to +23 Maximum points per block 8192 Extra attributes Number of Returns Class structure [0, 5]Sample resultsHere are a few results from the model.

THE EXPOSURE OF THE SLOPES EXPRESSES THE VALUE OF THE ORIENTATION OF A SLOPE MEASURED WITH RESPECT TO THE NORTHERN DIRECTION. THE EXPOSURE VALUES REFERRING TO THE WIND ROSE ARE INDICATED FOR EACH GROUPING. COVERAGE: COASTLINE. SOURCE: DATA PROCESSING COMING FROM DIGITAL TERRAIN MODEL - DTM ORIGINATED BY LIDAR SURVEY AT 1M RESOLUTION

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

LIDAR data for the entire territory of the Republic of Lithuania will be collected in 2023. The LIDAR density is at least 6.5 pc ./m2. The mean square error of the horizontal adjustment of a point on solid, fixed objects shall not exceed 30 cm for RMSE and 10 cm for a vertical point. LIDAR data are levelled and classified according to LAS specification into 8 classes: • Class 0 and/or Class 1 — Created, never classified; Unclassified; • Class 2 – Ground; • Class 3 – Low Vegetation; • Class 4 – Medium Vegetation; • Class 5 – High Vegetation; • Class 6 – Building points; • Class 7 – Noise points Low Point (noise); • Class 12 – Overlap Points. Data is divided into M 1: 2,000 sheets, according to LKS-94 in * .laz format.

This dataset provides object detection results using five different LiDAR-based object detection algorithms: PointRCNN, SECOND, Part-A², PointPillars, and PVRCNN. The experiments aim to determine the optimal angular resolution for LiDAR-based object detection. The point cloud data was generated in the CARLA simulator, modeled in a suburban scenario featuring 30 vehicles, 13 bicycles, and 40 pedestrians. The angular resolution in the dataset ranges from 0.1° x 0.1° (H x V) to 1.0° x 1.0°, with increments of 0.1° in each direction.

For each angular resolution, over 2000 frames of point clouds were collected, with 1600 of these frames labeled across three object classes—vehicles, pedestrians, and cyclists, for algorithm training purposes The dataset includes detection results after evaluating 1000 frames, with results recorded for the respective angular resolutions.

Each file in the dataset contains five sheets, corresponding to the five different algorithms evaluated. The data structure includes the following columns:

1. Frame Index: Indicates the frame number, ranging from 1 to 1000.

2. Object Classification: Labels objects as 1 (Vehicle), 2 (Pedestrian), or 3 (Cyclist).

3. Confidence Score: Represents the confidence level of the detected object in its bounding box.

4. Number of LiDAR Points: Indicates the count of LiDAR points within the bounding box.

5. Bounding Box Distance: Specifies the distance of the bounding box from the LiDAR sensor.

This dataset has been created in the context of the Leibniz Young Investigator Grants- programmed by the Leibniz University Hannover and is funded by the Ministry of Science and Culture of Lower Saxony (MWK) Grant Nr. 11-76251-114/2022

Between January and March 2010, lidar data was collected in southeast/coastal Georgia under a multi-agency partnership between the Coastal Georgia Regional Development Center, USGS, FEMA, NOAA and local county governments. Data acquisition is for the full extent of coastal Georgia, approximately 50 miles inland, excluding counties with existing high-resolution lidar derived elevation data. The data capture area consists of an area of approximately 5703 square miles. This project is within the Atlantic Coastal Priority Area as defined by the National Geospatial Program (NGP) and supports homeland security requirements of the National Geospatial-Intelligence Agency (NGA). This project also supports the National Spatial Data Infrastructure (NSDI) and will advance USGS efforts related to The National Map and the National Elevation Dataset. The data were delivered in LAS format version 1.2 in 5000 x 5000 foot tiles. The data are classified according to ASPRS LAS 1.2 classification scheme: Class 1 - Unclassified Class 2 - Bare Earth Class 7 - Low Point (Noise) Class 9 - Water Class 10 - Land below sea level Class 12 - Overlap

Tasks. In moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. Therefore, the input to all evaluated methods is a list of coordinates of the three-dimensional points along with their remission, i.e., the strength of the reflected laser beam which depends on the properties of the surface that was hit. Each method should then output a label for each point of a scan, i.e., one full turn of the rotating LiDAR sensor. Here, we only distinguish between static and moving object classes.

Metric To assess the labeling performance, we rely on the commonly applied Jaccard Index or intersection-over-union (mIoU) metric over moving parts of the environment. We map all moving-x classes of the original SemanticKITTI semantic segmentation benchmark to a single moving object class.

Citation Citation. More information on the task and the metric, you can find in our publication related to the task: @article{chen2021ral, title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}}, author={X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss}, year={2021}, journal={IEEE Robotics and Automation Letters(RA-L)}, doi = {10.1109/LRA.2021.3093567} }

THE EXPOSURE OF THE SLOPES EXPRESSES THE VALUE OF THE ORIENTATION OF A SLOPE MEASURED WITH RESPECT TO THE NORTHERN DIRECTION. THE EXPOSURE VALUES REFERRING TO THE WIND ROSE ARE INDICATED FOR EACH GROUPING. COVERAGE: COASTLINE. SOURCE: DATA PROCESSING COMING FROM DIGITAL TERRAIN MODEL - DTM ORIGINATED BY LIDAR SURVEY AT 1M RESOLUTION

Many Ontario lidar point cloud datasets have been made available for direct download by the Government of Canada through the federal Open Government Portal under the LiDAR Point Clouds – CanElevation Series record. Instructions for bulk data download are available in the Download Instructions document linked from that page. To download individual tiles, zoom in on the map in GeoHub and click a tile for a pop-up containing a download link.

See the LIO Support - Large Data Ordering Instructions to obtain a copy of data for projects that are not yet available for direct download. Data can be requested by project area or a set of tiles. To determine which project contains your area of interest or to view single tiles, zoom in on the map above and click. For bulk tile orders follow the link in the Additional Documentation section below to download the tile index in shapefile format. Data sizes by project area are listed below.

The Ontario Point Cloud (Lidar-Derived) consists of points containing elevation and intensity information derived from returns collected by an airborne topographic lidar sensor. The minimum point cloud classes are Unclassified, Ground, Water, High and Low Noise. The data is structured into non-overlapping 1-km by 1-km tiles in LAZ format.

This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters, accuracy and sensors may vary by project. Some project have additional classes, such as vegetation and buildings. See the detailed User Guide and contractor metadata reports linked below for additional information, including information about interpreting the index for placement of data orders.

Raster derivatives have been created from the point clouds. These products may meet your needs and are available for direct download. For a representation of bare earth, see the Ontario Digital Terrain Model (Lidar-Derived). For a model representing all surface features, see the Ontario Digital Surface Model (Lidar-Derived).

You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page.

Additional Documentation

Ontario Classified Point Cloud (Lidar-Derived) - User Guide (DOCX)

OMAFRA Lidar 2016-18 - Cochrane - Additional Metadata (PDF) OMAFRA Lidar 2016-18 - Peterborough - Additional Metadata (PDF) OMAFRA Lidar 2016-18 - Lake Erie - Additional Metadata (PDF) CLOCA Lidar 2018 - Additional Contractor Metadata (PDF) South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF) OMAFRA Lidar 2022 - Lake Huron - Additional Metadata (PDF) OMAFRA Lidar 2022 - Lake Simcoe - Additional Metadata (PDF) Huron-Georgian Bay Lidar 2022-23 - Additional Metadata (Word) Kawartha Lakes Lidar 2023 - Additional Metadata (Word) Sault Ste Marie Lidar 2023-24 - Additional Metadata (Word) Thunder Bay Lidar 2023-24 - Additional Metadata (Word) Timmins Lidar 2024 - Additional Metadata (Word)

OMAFRA Lidar Point Cloud 2016-18 - Cochrane - Lift Metadata (SHP) OMAFRA Lidar Point Cloud 2016-18- Peterborough - Lift Metadata (SHP) OMAFRA Lidar Point Cloud 2016-18 - Lake Erie - Lift Metadata (SHP) CLOCA Lidar Point Cloud 2018 - Lift Metadata (SHP) South Nation Lidar Point Cloud 2018-19 - Lift Metadata (SHP) York-Lake Simcoe Lidar Point Cloud 2019 - Lift Metadata (SHP) Ottawa River Lidar Point Cloud 2019-20 - Lift Metadata (SHP) OMAFRA Lidar Point Cloud 2022 - Lake Huron - Lift Metadata (SHP) OMAFRA Lidar Point Cloud 2022 - Lake Simcoe - Lift Metadata (SHP) Eastern Ontario Lidar Point Cloud 2021-22 - Lift Medatadata (SHP) DEDSFM Huron-Georgian Bay Lidar Point Cloud 2022-23 - Lift Metadata (SHP) DEDSFM Kawartha Lakes Lidar Point Cloud 2023 - Lift Metadata (SHP) DEDSFM Sault Ste Marie Lidar Point Cloud 2023-24 - Lift Metadata (SHP) DEDSFM Sudbury Lidar Point Cloud 2023-24 - Lift Metadata (SHP) DEDSFM Thunder Bay Lidar Point Cloud 2023-24 - Lift Metadata (SHP) DEDSFM Timmins Lidar Point Cloud 2024 - Lift Metadata (SHP) GTA 2023 - Lift Metadata (SHP)

Ontario Classified Point Cloud (Lidar-Derived) - Tile Index (SHP)

Ontario Lidar Project Extents (SHP)

Data Package Sizes

LEAP 2009 - 22.9 GB

OMAFRA Lidar 2016-18 - Cochrane - 442 GB OMAFRA Lidar 2016-18 - Lake Erie - 1.22 TB OMAFRA Lidar 2016-18 - Peterborough - 443 GB

GTA 2014 - 57.6 GB GTA 2015 - 63.4 GB Brampton 2015 - 5.9 GB Peel 2016 - 49.2 GB Milton 2017 - 15.3 GB Halton 2018 - 73 GB

CLOCA 2018 - 36.2 GB

South Nation 2018-19 - 72.4 GB

York Region-Lake Simcoe Watershed 2019 - 75 GB

Ottawa River 2019-20 - 836 GB

Lake Nipissing 2020 - 700 GB

Ottawa-Gatineau 2019-20 - 551 GB

Hamilton-Niagara 2021 - 660 GB

OMAFRA Lidar 2022 - Lake Huron - 204 GB OMAFRA Lidar 2022 - Lake Simcoe - 154 GB

Belleville 2022 - 1.09 TB

Eastern Ontario 2021-22 - 1.5 TB

Huron Shores 2021 - 35.5 GB

Muskoka 2018 - 72.1 GB Muskoka 2021 - 74.2 GB Muskoka 2023 - 532 GB The Muskoka lidar projects are available in the CGVD2013 or CGVD28 vertical datums. Please specifify which datum is needed when ordering data.

Digital Elevation Data to Support Flood Mapping 2022-26:

Huron-Georgian Bay 2022 - 1.37 TB Huron-Georgian Bay 2023 - 257 GB Huron-Georgian Bay 2023 Bruce - 95.2 GB Kawartha Lakes 2023 - 385 GB Sault Ste Marie 2023-24 - 1.15 TB Sudbury 2023-24 - 741 GB Thunder Bay 2023-24 - 654 GB Timmins 2024 - 318 GB

GTA 2023 - 985 GB

Status On going: Data is continually being updated

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

The classification of point cloud datasets to identify distribution wires is useful for identifying vegetation encroachment around power lines. Such workflows are important for preventing fires and power outages and are typically manual, recurring, and labor-intensive. This model is designed to extract distribution wires at the street level. Its predictions for high-tension transmission wires are less consistent with changes in geography as compared to street-level distribution wires. In the case of high-tension transmission wires, a lower ‘recall’ value is observed as compared to the value observed for low-lying street wires and poles.Using the modelFollow the guide to use the model. The model can be used with ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with point geometry (X, Y and Z values). Note: The model is not dependent on any additional attributes such as Intensity, Number of Returns, etc. This model is trained to work on 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: Classcode Class Description 0 Background Class 14 Distribution Wires 15 Distribution Tower/PolesApplicable geographiesThe model is expected to work within any geography. It's seen to produce favorable results as shown here in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the RandLANet 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 Background (0) 0.999679 0.999876 0.999778 Distribution Wires (14) 0.955085 0.936825 0.945867 Distribution Poles (15) 0.707983 0.553888 0.621527Training dataThis model is trained on manually classified training dataset provided to Esri by AAM group. The training data used has the following characteristics: X, Y, and Z linear unitmeter Z range-240.34 m to 731.17 m Number of Returns1 to 5 Intensity1 to 4095 Point spacing0.2 ± 0.1 Scan angle-42 to +35 Maximum points per block20000 Extra attributesNone Class structure[0, 14, 15]Sample resultsHere are a few results from the model.

This dataset provides object detection results using five different LiDAR-based object detection algorithms: PointRCNN, SECOND, Part-A², PointPillars, and PVRCNN. The experiments aim to determine the optimal angular resolution for LiDAR-based object detection. The point cloud data was generated in the CARLA simulator, modeled in a suburban scenario featuring 30 vehicles, 13 bicycles, and 40 pedestrians. The angular resolution in the dataset ranges from 0.1° x 0.1° (H x V) to 1.0° x 1.0°, with increments of 0.1° in each direction. For each angular resolution, over 2000 frames of point clouds were collected, with 1600 of these frames labeled across three object classes—vehicles, pedestrians, and cyclists, for algorithm training purposes The dataset includes detection results after evaluating 1000 frames, with results recorded for the respective angular resolutions. Each file in the dataset contains five sheets, corresponding to the five different algorithms evaluated. The data structure includes the following columns: Frame Index: Indicates the frame number, ranging from 1 to 1000. Object Classification: Labels objects as 1 (Vehicle), 2 (Pedestrian), or 3 (Cyclist). Confidence Score: Represents the confidence level of the detected object in its bounding box. Number of LiDAR Points: Indicates the count of LiDAR points within the bounding box. Bounding Box Distance: Specifies the distance of the bounding box from the LiDAR sensor. This dataset has been created in the context of the Leibniz Young Investigator Grants- programmed by the Leibniz University Hannover and is funded by the Ministry of Science and Culture of Lower Saxony (MWK) Grant Nr. 11-76251-114/2022

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. The polygonal layer of lidar slope classes expresses the slope of the terrain. The slopes are generated from a digital terrain model (DTM) with a resolution of 10 meters. The latter is the result of an aggregation by bilinear interpolation of lidar NCDs at 1 m. The minimum area of the resulting polygons is 0.2 hectares. Lidar digital slopes are divided into 7 classes. • A - Null from [0 to 3]% • B - Low from] 3 to 8]% • C - Soft from] 8 to 15]% • D - Moderate from] 15 to 30]% • E - Strong from] 15 to 30]% • E - Strong from] 30 to 40]% • B - Low from] 3 to 8]% • C - Soft from] 8 to 15]% • D - Moderate from] 15 to 30]% • E - Strong from] 15 to 30]% • E - Strong from] 30 to 40]% • E - Strong from] 30 to 40]% • F - Steep from] 40 to 40]% • F - Steep from] 40 to ∞ [% • S — Summit entirely surrounded by slopes F This map covers the entire territory of the Southern Quebec Ecoforest Inventory (IEQM) and was developed in order to provide stakeholders with the tools they need when applying for financial assistance from the Forest Management Investment Program (PIAF). _We do not recommend using the information in this layer for detailed analysis. _**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The SemanticTHAB dataset is a large-scale dataset designed for semantic segmentation in autonomous driving. It contains 4,750 3D LiDAR point clouds collected from urban environments. The dataset includes labeled point clouds with 20 semantic classes, such as road, car, pedestrian, and building. It provides ground truth annotations for training and evaluating semantic segmentation algorithms, offering a real-world benchmark for 3D scene understanding in self-driving car applications. The dataset is desinged to extent the SemanticKITTI benchmark by scans of a modern high resolution LiDAR sensor (Ouster OS2-128, Rev7).

This Data Series Report contains lidar elevation data collected February 6, 2012, over the Chandeleur Islands, Louisiana. LAS 1.2 formatted point data files were generated based on these data. The point cloud data were processed to extract bare earth data; therefore, the point cloud data are classified into only these classes: 1 and 17-unclassified, 2-ground, 9-water, and 10-breakline proximity. Digital Aerial Solutions, LLC, was contracted by the USGS to collect and process these data. The lidar data were collected at a nominal pulse spacing (NPS) of 0.5 meter (m). The data are in decimal degree geographic coordinates, North American Datum 1983, National Spatial Reference System 2007 (NAD83 NSRS2007)). The vertical datum is North American Vertical Datum 1988, Geoid 2009, Geodetic Reference System 1980 (NAVD88 GEOID09 GRS80) in meters. Thirty-three LAS files, based on a 2-kilometer by 2-kilometer tiling scheme, cover the entire survey area. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

This publication presents lidar data collected over the community of Golovin, on the southern coast of the Seward Peninsula in western Alaska (fig. 1). The original data were collected on November 5, 2013, by Quantum Spatial. The complete, classified lidar dataset was purchased by the State of Alaska Division of Geological & Geophysical Surveys in 2014 in support of coastal vulnerability mapping efforts. For the purposes of open access to lidar datasets in coastal regions of Alaska, this collection is being released as a Raw Data File with an open end-user license. The horizontal datum for this dataset is NAD83 (CORS96), the vertical datum is NAVD88, Geoid 09, and it is projected in UTM Zone 3 North. Units are in Meters. Data have been classified to Ground (class 2) and Default (class 1). Quantum Spatial collected the Golovin LiDAR data on 11/05/2013.

This data collection of the 3D Elevation Program (3DEP) consists of Lidar Point Cloud (LPC) projects as provided to the USGS. These point cloud files contain all the original lidar points collected, with the original spatial reference and units preserved. These data may have been used as the source of updates to the 1/3-arcsecond, 1-arcsecond, and 2-arcsecond seamless 3DEP Digital Elevation Models (DEMs). 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. Lidar (Light detection and ranging) discrete-return point cloud data are available in LAZ format. The LAZ format is a lossless compressed version of the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. Point Cloud data can be converted from LAZ to LAS or LAS to LAZ without the loss of any information. Either format stores 3-dimensional point cloud data and point ...

This Light Detection and Ranging (LiDAR) LAS dataset is a topographic survey conducted for a coalition of GIS practitioners, including the Florida Division of Emergency Management (FDEM), Florida Water Management Districts, Florida Fish and Wildlife Conservation Commission, Florida Department of Environmental Protection, Army Corps of Engineers Jacksonville District, and other state and federal agencies. The goal for this project is to use the LiDAR data as new elevation inputs for updated SLOSH data grids. The ultimate result is the update of the Regional Hurricane Evacuation Studies (RHES) for the state. The State of Florida Division of Emergency Management LiDAR Survey was collected under the guidance of a Professional Mapper/Surveyor. This data was collected for a portion of Levy County, Florida from 1 July - August 16 2007. This is a classified lidar data set; bare-earth points (class 2), water returns (class 9), and unclassified data (class 1). The LiDAR data was flown at a density sufficient to support a maximum final post spacing of 4 feet for unobscured areas. A footprint of this data set may be viewed in Google Earth at: ftp://coast.noaa.gov/pub/DigitalCoast/lidar1_z/geoid12a/data/530/supplemental/FDEM_Lidar_Levy_County.kmz