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

This links to a .ZIP file contains Montana Spatial Data Infrastructure (MSDI) and other pertinent data layers clipped to the Montana Yellowstone River 2022 Spring Flood Disaster Subset Area of Interest polygon. The Area of Interest includes areas immediately adjacent to the flooded tributaries of the Yellowstone River in Carbon, Park, Stillwater, Sweet Grass, Treasure and Yellowstone Counties. The data layers are current as of July 2022. The .ZIP file also contains ArcMap layer files, map templates, and metadata for the source geodatabase data.For datasets clipped to the county or statewide use the Montana Data Bundler: https://msl.mt.gov/GIS/BundlerInside the zip are: A 2022MontanaFlood_DataList.docx that lists all GIS data included in this archive.A ReadMe.docx that details the data organization, instructions on how to set he map file paths, how to change the display map extents, and how to connect to web GIS services.ArcMap Layer Symbology Files (.lyr)GIS Layer MetadataMap Project Templates (ArcMap 10.7 and ArcGIS Pro 2.9 are included; other versions available upon request)File Geodatabase with data layers clipped to the Spring 2022 Flood Yellowstone River Area of InterestData Included:Montana Spatial Data Infrastructure (MSDI) DataAdministrative Boundaries - County Boundaries - Municipalities-Cities, TownsCadastral - Ownership - Public Lands - Conservation Easements Geographic Names - MT_NamesNational Hydrography Dataset - WBDHUC8-HUC8SubBasin - WBDHUC10-HUC10Watershed - WBDHUC12-HUC12Subwatershed - NHDFlowline - NHDWaterbody - NHDAreaCADNSDI (Public Land Survey database) - PLSSFirstDivision-Sections - PLSSTownship-TownshipsStructure/Address PointsTransportation - Bridges - Railroads - Roads Wetland and RiparianMTNHP Landcover - Landcover 2017 - Landcover 2021 (version 1)Elevation - NED 10 meter digital elevation model (DEM) - NED-Continuous, Integer rasters - Aspect-Continuous, Integer rasters (10 meter) - Slope-Continuous, Integer rasters (10 meter) - LiDAR-Derived Building Footprints - LiDAR Building Footprint Boundary - LiDAR ProjectsSoils (NRCS SSURGO) - Soils Map units - Soils Points - Soils LinesUSDA Forest ServiceLandfire – Existing Vegetation Type (EVT)Landfire – Existing Vegetation Height (EVH)Landfire – Existing Vegetation Cover (EVC)USDA NASS DataCropLand Data Layer 2021Department of Revenue Data2020 DOR Final Land Units (FLU)MiscellaneousBuilding Footprints (Microsoft)USGS 24k Topo Quads

With this mapping application, users can click anywhere within the Commonwealth of Massachusetts to find the elevation at that location in both meters and feet. The elevation data digital elevation model (DEM), in integer units, are derived from statewide Lidar (2013-2021) Terrain Data. The Vertical Datum of the lidar data used to create the DEM is NAVD88 – Geoid18 (m).

The map displays a tile service that shows the DEM using a custom color ramp along with Lidar-derived shaded relief image. The symbology was created by MassGIS staff in ArcGIS Pro using the 'multiply' layer blending option. At medium and large scales the MassGIS Map Features for Imagery tile layer displays atop the imagery.Click the "i" button in the lower left to view a legend.This application is hosted by MassGIS at ArcGIS Online.