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

There are many ways to create spatial data. In this tutorial, you'll use an editing tool to draw features on an imagery basemap. The features you create will be saved in a feature class in your project geodatabase.Estimated time: 30 minutesSoftware requirements: ArcGIS Pro

Uses the Soil Data Development Toolbox for Gridded National Soil Survey Geographic Database (gNATSGO). Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptionsNotes:.A GeoTIFF version of the gNATSGO CONUS raster is availableThe USDA-NRCS-SPSD refreshes all published soil databases annually. gNATSGO will be included in the refresh cycle, which will provide a new up-to-date version of the database each year.gNATSGO is an ESRI file geodatabase.In the state and island territory databases, the soil map units are delivered only as a 10-meter raster version.In the CONUS database, the raster is 30-meter.No vectorized version of the soil map units is included in gNATSGO.The soil map units are uniquely identified by the mukey, which is included in the attribute table.The database has 70 tables that contain soil attributes, and relationship classes are built into the database to define relationships among tables.The raster can be joined to the Mapunit and Muaggatt tables in the MUKEY field.The database contains a feature class called SAPOLYGON. The “source” field in this feature class indicates whether the data was derived from SSURGO, STATSGO2, or an RSS.If you encounter an ArcMap error when working with a gNATSGO dataset that reads “The number of unique values exceeds the limit” try increasing the maximum number of unique values to render in your Raster ArcMap Options. Specific instructions can be obtained here: https://support.esri.com/en/technical-article/000010117

Renowned for its natural and man-made beauty, the historic city of Venice spans a series of islands in a shallow lagoon. Venice’s unique geography has a downside, however. Tidal patterns mix with low elevation to cause acqua alta (high water), a periodic flooding that, although not dangerous to human life, impedes transportation and endangers Venice’s priceless architecture.This layer package includes three layers. The Structures layer contains building footprint data. The Canals layer contains Venice's canals. The Landmarks layer contains famous landmarks throughout the city. The data was acquired from Comune di Venezia - Portale dei servizi in 2014.This layer package contains feature class data on Venice's landmarks, canals, and structures for the tutorial Map Venice in 2D. The data will be used to visualize the landscape of Venice.

Gridded SSURGO (gSSURGO) is similar to the standard product from the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) Database, but is in the Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase format. A file geodatabase has the capacity to store significantly more data and thus greater spatial extents than the traditional SSURGO product. This allows for statewide or even Conterminous United States (CONUS) tiling of data. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shape files. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS). An important addition to the new format is a 10-meter raster (MapunitRaster_10m) of the map unit soil polygons feature class, which provides statewide coverage in a single layer. The CONUS database includes a 30-meter raster because of size constraints. This new addition provides greater performance and important analysis capabilities to users of soils data. Statewide tiles consist of soil survey areas needed to provide full coverage for a given State. In order to create a true statewide soils layer, some clipping of excess soil survey area gSSURGO data may be required. The new format also includes a national Value Added Look Up (valu) Table that has several new “ready to map” attributes.Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptions

Renowned for its natural and man-made beauty, the historic city of Venice spans a series of islands in a shallow lagoon. Venice’s unique geography has a downside, however. Tidal patterns mix with low elevation to cause acqua alta (high water), a periodic flooding that, although not dangerous to human life, impedes transportation and endangers Venice’s priceless architecture.This ZIP file includes three layers. The Structures layer contains building footprint data. The Canals layer contains Venice's canals. The Landmarks layer contains famous landmarks throughout the city. It also includes an elevation layer. The data was acquired from Comune di Venezia - Portale dei servizi in 2014.This ZIP file contains feature class data on Venice's landmarks, canals, and structures for the project Map Venice in 2D on the Learn ArcGIS website. The data will be used to visualize the landscape of Venice.

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.

In 2010, the Alaska Regional Director and Alaska Leadership Council directed all parks to complete a geospatial layer of installations and keep it up to date for use in cumulative impacts analysis. The AKR Installations Geodatabase is a centralized data repository that supports a regionally consistent approach useful for NPS permitting and resource management. By supporting the 2010 initiatives for strengthening NPS wilderness stewardship, this system can inform NEPA analyses, help parks track accountability for maintaining and removing installations, and provide critical information for wilderness character monitoring. This AKR Installations Geodatabase, developed and stewarded by the AKR GIS Team, is available for upkeep by each park through its designated Installations Point-of-Contact (POC) and supporting GIS POC. The Installations POC is generally the park coordinator for wilderness, compliance, and/or permitting while the GIS POC is someone who can assist with organizing and preparing the geospatial data for inclusion in the AKR Installations Geodatabase. The AKR Installations Geodatabase contains four point‐type feature classes: Instrumentation Installations (INSTRUMENTATION), Marker Installations (MARKER), Communication Installations (COMMUNICATION), and Generic Installation Point Installations (GENERIC INSTALLATION POINT). Each feature class in the geodatabase is designed to store a specific type of installation data and represent that installation data as a point feature in ArcGIS. To facilitate data entry, viewing, and analysis the non‐spatial attribute fields contained in each feature class have been standardized. The Installations User Guide was developed as a reference to introduce the NPS Alaska Region Installation geodatabase and provide guidance for installation data entry and record management in this system. It is recommended that the Installations User Guide be reviewed prior to populating the NPS Alaska Region Installations geodatabase.The COMMUNICATION feature class is designed to contain records for installations of stand‐alone communication relay equipment (e.g. radio repeaters, cell phone towers). Installations in the COMMUNICATION feature class differ from those in the INSTRUMENTATION feature class as they serve to transmit data, not collect or record data for scientific purposes. Communication‐related equipment associated with a station‐type installation managed in the INSTRUMENTATION feature class (i.e. continuous GPS station, seismic station, precipitation station, SNOTEL station, snow course station, weather station) are not considered to be stand‐alone. Please review the COMMUNICATION feature class data model (Section 12) of the Installations User Guide to gain a general understanding of its structure and data requirements. Table 10 and the domain values assigned in the data model’s Installation_Subtype field may further clarify the types of items relevant to this feature class.The GENERIC INSTALLATION POINT feature class is designed to contain records for installations the user deems relevant to the cumulative effects analysis process, but are not appropriate for inclusion in the INSTRUMENTATION, MARKER, or COMMUNICATION features classes at present time. The GENERIC INSTALLATION POINT feature class is intended to serve the purpose of a temporary placeholder for installation data while NPS data standards and the NPS Alaska Region Installations geodatabase evolve. Please review the GENERIC INSTALLATION POINT feature class data model (Section 12) of the Installations User Guide to gain a general understanding of its structure and data requirements. Table 11 may further clarify the types of items relevant to this feature class. Installation data outside of Table 11 items may also be included, but please remember that all records contained in this feature class will be represented as points in ArcGIS. OHV trails and maintained foot trails are better represented as line features in ArcGIS and have therefore not been marked for inclusion in the GENERIC INSTALLATION POINT feature class.The INSTRUMENTATION feature class is designed to contain records for installations of equipment that collect or record data for scientific purposes. Please review the INSTRUMENTATION feature class data model (Section 12) of the Installations User Guide to gain a general understanding of its structure and data requirements. Table 2 and the domain values assigned in the data model’s Installation_Subtype field may further clarify the types of items relevant to this feature class.The MARKER feature class is designed to contain records for installations that serve to mark a location of interest. Please review the MARKER feature class data model (Section 12) of the Installations User Guide to gain a general understanding of its structure and data requirements. Table 7 and the domain values assigned in the data model’s the Installation_Subtype field may further clarify the types of items relevant to this feature class. The MARKER feature class in the NPS Alaska Region Installations geodatabase was developed to manage installation data relevant to the cumulative effects analysis process and is not intended to replace the NPS Survey Monumentation Data Standard for the distribution of survey monumentation data to enterprise GIS systems.The corresponding NPS DataStore on Integrated Resource Management Applications (IRMA) reference is:AKR Science in Wilderness Installations PUBLIC