This layer contains the Index Tiles for LiDAR data in the Wellington Region including Wellington City as well as the surrounding area captured between 2019 and 2020.

• The DEM is available as layer "https://data.linz.govt.nz/layer/105023">Wellington City LiDAR 1m DEM (2019-2020).

• The DSM is available as layer "https://data.linz.govt.nz/layer/105024">Wellington City LiDAR 1m DSM (2019-2020).

• The LAS point cloud and vendor project reports are available from OpenTopography.

LiDAR was captured for Wellington City Council by Aerial Surveys from 20 March 2019 to 14 March 2020. These datasets were generated by Aerial Surveys and their subcontractors. Data management and distribution is by Toitū Te Whenua Land Information New Zealand.

Data comprises:

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             DEM: tif or asc tiles in NZTM2000 projection, tiled into a 1:1,000 tile layout

• DSM: tif or asc tiles in NZTM2000 projection, tiled into a 1:1,000 tile layout

• Point cloud: las tiles in NZTM2000 projection, tiled into a 1:1,000 tile layout

Pulse density specification is at a minimum of 16 pulses/square metre.

Vertical datum is NZVD2016.

This layer contains the DSM for LiDAR data from the Wellington region captured in 2013. The DEM is available as layer Wellington LiDAR 1m DEM (2013). The index tiles are available as layer Wellington LiDAR Index Tiles (2013). The LAS point cloud is available from OpenTopography.

Lidar was captured for Greater Wellington Regional Council by Aerial Surveys in 2013. The datasets were generated by Landcare Research. The survey area includes Wellington, Porirua, Lower Hutt, Upper Hutt, Wairarapa, and Kapiti. Data management and distribution is by Land Information New Zealand.

Data comprises: •DEM: tif or asc tiles in NZTM2000 projection, tiled into a 1:1,000 tile layout •DSM: tif or asc tiles in NZTM2000 projection, tiled into a 1:1,000 tile layout •Point cloud: las tiles in NZTM2000 projection, tiled into a 1km x 1km tile layout

Data was collected at >1 pulse/square metre pulse density. Attributes include: -Elevation -Intensity values -Return number -Adjusted GPS time -Classification

Vertical datum is NZVD2016

Lidar was captured for Wellington City Council by Aerial Surveys between 2019 and 2020. The dataset was generated by Aerial Surveys and their subcontractors. The survey area includes Wellington City and the surrounding area. Data management and distribution is by Land Information New Zealand Prepared DEM and DSM files are available through the LINZ Data Service: Wellington City, New Zealand 2019 Digital Elevation Model 2019-2020 Wellington City, New Zealand 2019 Digital Surface Model 2019-2020

Lidar of the entire Wellington region, captured for Greater Wellington Regional Council by Aerial Surveys in 2013. Further processing including automated classification and conversion to NZVD2016 was done by Landcare Research. Data management and distribution is by Land Information New Zealand. Prepared DEM and DSM files are available through the LINZ Data Service Wellington, New Zealand 2013-2014 DEM Wellington, New Zealand 2013-2014 DSM

NAME: Wellington region LiDAR DEM 1mABSTRACT : Lidar was captured for Greater Wellington Regional Council by Aerial Surveys in 2013SOURCE DATE: LiDAR Flown Feb 2013 SUPPLIER: NZ Aerial Surveys Ltd.CAPTURE METHOD: Using LiDAR and surface created with Kriging interpolation. Vertical datum is NZVD2016POSITIONAL ACCURACY: Generally +/- 1.05m but worse in dense bush areasCUSTODIAN/CONTACT: Greater Wellington regional Council GIS TeamUPDATE FREQUENCY: As requested, within WAGGIS regional maintenance programmeUSAGE: Attributes include -Elevation, intensity values, return number, classificationCOMPLETENESS: 100% Wellington regional areaKEYWORDS: Landcover, topography, DEM

Lidar was collected for GNS Science by NZ Aerial Mapping in September 2010, under survey name Tararua LiDAR, and includes approximately 8 km of the Wellington Fault on the North Island, approximately 82 km northeast of Wellington. The polygon encloses an area of approximately 9 km2. The dataset was generated by NZ Aerial Mapping (NZAM) and their subcontractors and processed into various digital map data products. Note: This dataset was converted to LAS from XYZ (ASCII) points and lacks typical lidar attibutes.

This lidar was collected for Isabelle Manighetti, Observatoire de la Cote d'Azur, GEOAZUR. The requested survey area is located approximately 39 km east of Wellington. The polygon encloses an area of approximately 128 km².

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

Lidar of the entire Wellington region, captured for Greater Wellington Regional Council by Aerial Surveys in 2013. Further processing including automated classification and conversion to NZVD2016 was done by Landcare Research. Data management and distribution is by Land Information New Zealand. Prepared DEM and DSM files are available through the LINZ Data Service Wellington, New Zealand 2013-2014 DEM Wellington, New Zealand 2013-2014 DSM

Tree cover data has been uploaded to the databse on 21st of March 2022.This tree cover feature class was produced for Wellington City and Suburbs; the study area can be seen in the accompanying tree canopy cover report (https://dx.doi.org/10.26021/11224). The tree cover feature class was produced using an object-based image analysis (OBIA) approach. OBIA is a semi-automated image classification method that can be used to identify trees based on aerial photography and LiDAR data. Following the OBIA, tree canopy cover was manually refined to correct errors in the tree cover classification. Boundary adjustmentfor tree crowns was also undertaken at a scale of no greater than 1:2,500. For the purpose of the OBIA, a tree was defined as an object having vegetation-like reflectance characteristics, exceeding 3.5 m in height and having a minimum diameter of 1. 5 m. Treefeatures comprise all tree and forest types. This includes, but is not limited to, park and reserve trees, street trees, trees on private property, orchards, remnant patches of native forest, hedgerows, and trees in commercially-managed, large-scale forestry plantations. The data on which the OBIA was performed included aerial photography and LiDAR data. Aerial photography was captured by AAM NZ Ltd. for the Wellington City Council during the summer of 2016-17. Images were acquired on 24, 27, 28 February and 5 March 2017. Imagery was supplied as 10 cm pixel resolution, 3-band (RGB) uncompressed GeoTIFF. The final spatial accuracy is ± 0.2 m at 90% confidence level. LiDAR data were captured for Wellington City Council by Aerial Surveys from 20 March 2019 to 14 March 2020. As a consequence of the range in time of acquisition for LiDAR data, the tree canopy cover assessment that was completed for this report should be considered accurate as at 20 March 2019. Both aerial imagery and LiDAR data were sourced from the LINZ Data Service and licensed by Wellington City Council, for re-use under CC BY 4.0.

Lidar was collected for GNS Science by NZ Aerial Mapping in September 2010, under survey name Tararua LiDAR, and includes approximately 8 km of the Wellington Fault on the North Island, approximately 82 km northeast of Wellington. The polygon encloses an area of approximately 9 km2. The dataset was generated by NZ Aerial Mapping (NZAM) and their subcontractors and processed into various digital map data products. Note: This dataset was converted to LAS from XYZ (ASCII) points and lacks typical lidar attibutes.

This dataset contains 5m Contours created by AAM from 2017 aerial photography. The DTM was created from 2006 LiDAR, 2009, 2011 & 2017 photogrametry masspoints and breaklines. Contours generated from LiDAR and photogrametry should not be used for detailed engineering design.This item has been created to be used in WCC's Open Data Portal.

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 dataset provides boundaries for trees in Wellington, New Zealand. The area for which tree cover boundaries are provided corresponds to the area covered by the city's Green Network Plan. Tree cover is a 2D representation of the outline of a tree which has been classified from LINZ aerial imagery (acquired in the summer of 2016-17) and lidar data (acquired in the summer of 2019-2020) using a combination of automated and manual processes to identify and delineate trees. Trees were considered to have a minimum height of 3.5 m and a minimum diameter of 1.5 m. Because of this definition, this tree cover dataset may contain large shrubs or other tall forms of vegetation. Where trees exceeded 7 m in height, a maximum height was determined from lidar data and assigned to that polygon. Because polygons include one or more trees, the height associated with a polygon means that at least one tree within that polygon has that height. Justin Morgenroth, University of Canterbury, 28 October, 2020. Commissioned by Urban Design, Wellington City Council.
Please contact the Urban Design, [City Design Place Planning] team for more information.

This file contains fault mapping used to construct Fault Avoidance Zones for active faults in Wellington City (Morgenstern and Van Dissen 2020). The faults were mapped using: 1) LiDAR data; 2) 2017 urban and rural aerial photographs; 3) the NZ Active Faults Database; 4) published papers and maps (Ota et al. 1981; Begg and Mazengarb 1996; Begg and Johnston 2000; Barnes et al. 2019; Kaiser et al. 2019); 5) unpublished GNS Science Consulting and Science reports (Perrin and Wood 2003a, b; Van Dissen et al. 2003, 2005; Litchfield and Van Dissen 2014; Berryman 2019); 6) and the authors’ first-hand knowledge of the geology and active faulting in the district.These data should be used to assist future land use planning, particularly with regard to building on "Greenfield" (i.e. previously undeveloped land) sites or in the renovation of buildings in areas adjacent to active faults in accordance to the Ministry for the Environment "Planning for Development on or Close to Active Faults" (Kerr et al. 2003).These data should be used in conjunction with the associated GNS Science report CR2020/57.

Intended Purpose:Polygon layer of area affected by the Ohariu Fault (High Hazard Area) Hazard Overlay . Created for the 2024 operative version of the Wellington City Council District Plan as part of the District Plan Review Process.Abbreviations/Acronyms:ePlan - "Electronic Plan" the web version of the District PlanPDP - Proposed District PlanIHP - Independent Hearings PanelWCC - Wellington City Council Refresh Rate (Data only):Static Ownership:This data is owned by WCC District Planning Team, contact District.Plan@wcc.govt.nz for questions about this layer and its appropriate use cases. Ownership specifies legal or administrative control over the content. Stewardship:This data is maintained by WCC City Insights Team, contact cityinsightsgis@wcc.govt.nz for information about the creation of this layer and its maintenance. Custodianship:This data is maintained by WCC City Insights Team, contact cityinsightsgis@wcc.govt.nz for information about the creation of this layer and its maintenance. Stewardship addresses the ongoing care, maintenance, and management of the content.Authoritative Data Sources (Data only):This layer is based on fault mapping by GNS of Fault Avoidance Zones for active faults in Wellington City (Morgenstern and Van Dissen 2020). Further data changes were made as part of the District Plan Review Process.Summary of Data Collection (Data only):Faults were mapped using: 1) LiDAR data; 2) 2017 urban and rural aerial photographs; 3) the NZ Active Faults Database; 4) published papers and maps (Ota et al. 1981; Begg and Mazengarb 1996; Begg and Johnston 2000; Barnes et al. 2019; Kaiser et al. 2019); 5) unpublished GNS Science Consulting and Science reports (Perrin and Wood 2003a, b; Van Dissen et al. 2003, 2005; Litchfield and Van Dissen 2014; Berryman 2019); 6) and the authors’ first-hand knowledge of the geology and active faulting in the district.These data should be used to assist future land use planning, particularly with regard to building on "Greenfield" (i.e. previously undeveloped land) sites or in the renovation of buildings in areas adjacent to active faults in accordance with the Ministry for the Environment "Planning for Development on or Close to Active Faults" (Kerr et al. 2003). These data should be used in conjunction with the associated GNS Science report CR2020/57.

Intended Purpose:Polygon layer of area affected by the Shepherds Gully Fault (Low Hazard Area) Hazard Overlay . Created for the 2024 operative version of the Wellington City Council District Plan as part of the District Plan Review Process.Abbreviations/Acronyms:ePlan - "Electronic Plan" the web version of the District PlanPDP - Proposed District PlanIHP - Independent Hearings PanelWCC - Wellington City Council Refresh Rate (Data only):Static Ownership:This data is owned by WCC District Planning Team, contact District.Plan@wcc.govt.nz for questions about this layer and its appropriate use cases. Ownership specifies legal or administrative control over the content. Stewardship:This data is maintained by WCC City Insights Team, contact cityinsightsgis@wcc.govt.nz for information about the creation of this layer and its maintenance. Custodianship:This data is maintained by WCC City Insights Team, contact cityinsightsgis@wcc.govt.nz for information about the creation of this layer and its maintenance. Stewardship addresses the ongoing care, maintenance, and management of the content.Authoritative Data Sources (Data only):This layer is based on fault mapping by GNS of Fault Avoidance Zones for active faults in Wellington City (Morgenstern and Van Dissen 2020). Further data changes were made as part of the District Plan Review Process.Summary of Data Collection (Data only):Faults were mapped using: 1) LiDAR data; 2) 2017 urban and rural aerial photographs; 3) the NZ Active Faults Database; 4) published papers and maps (Ota et al. 1981; Begg and Mazengarb 1996; Begg and Johnston 2000; Barnes et al. 2019; Kaiser et al. 2019); 5) unpublished GNS Science Consulting and Science reports (Perrin and Wood 2003a, b; Van Dissen et al. 2003, 2005; Litchfield and Van Dissen 2014; Berryman 2019); 6) and the authors’ first-hand knowledge of the geology and active faulting in the district.These data should be used to assist future land use planning, particularly with regard to building on "Greenfield" (i.e. previously undeveloped land) sites or in the renovation of buildings in areas adjacent to active faults in accordance with the Ministry for the Environment "Planning for Development on or Close to Active Faults" (Kerr et al. 2003). These data should be used in conjunction with the associated GNS Science report CR2020/57.

This file contains fault mapping used to construct Fault Avoidance Zones for active faults in Wellington City (Morgenstern and Van Dissen 2020). The faults were mapped using: 1) LiDAR data; 2) 2017 urban and rural aerial photographs; 3) the NZ Active Faults Database; 4) published papers and maps (Ota et al. 1981; Begg and Mazengarb 1996; Begg and Johnston 2000; Barnes et al. 2019; Kaiser et al. 2019); 5) unpublished GNS Science Consulting and Science reports (Perrin and Wood 2003a, b; Van Dissen et al. 2003, 2005; Litchfield and Van Dissen 2014; Berryman 2019); 6) and the authors’ first-hand knowledge of the geology and active faulting in the district.These data should be used to assist future land use planning, particularly with regard to building on "Greenfield" (i.e. previously undeveloped land) sites or in the renovation of buildings in areas adjacent to active faults in accordance to the Ministry for the Environment "Planning for Development on or Close to Active Faults" (Kerr et al. 2003).These data should be used in conjunction with the associated GNS Science report CR2020/57.