This layer shows the 3D indoor map of some buildings' interior structures in Hong Kong. It is a set of the data made available by Lands Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

This layer shows the Floor Plan of a 3D Intelligent Map in an area of To Kwa Wan in Hong Kong. It is a set of data made available by the Urban Renewal Authority under the Government of Hong Kong Special Administrative Region (the "Government") at https://GEODATA.GOV.HK/ ("Hong Kong Geodata Store"). The source data is in GML format and has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong Geodata Store at https://geodata.gov.hk.

This 3D basemap presents OpenStreetMap (OSM) data hosted by Esri using the OpenStreetMap style. Esri created this Buildings, Places and Labels, Trees, and OpenStreetMap layers from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new scene available to the OSM, GIS, and Developer communities.

The 3D Mapping and 3D Modelling Software Market is estimated to be valued at USD 5.16 Billion in 2022 and is expected to reach USD 16.26 Billion by 2030, registering a CAGR of 15.4% during a forecast period of 2023-2030. What are the factors impacting the growth of 3D Mapping and 3D Modelling Software Market?

3D-enabled display devices for advanced and better navigation are increasing the demand for 3D Mapping and 3D Modelling Market 

The increasing need for HD experience is anticipated to spike the development of 3D maps. 3D technology in previous years available to the users was not that satisfactory. Consumers need the finest viewing experience of perceived 3D pictures that look like things. 3D mapping and 3D modeling provide real-life experiences of the surrounding buildings and landscape by seeing them through 3D-enabled devices like tablets, smartphones, and personal computers are projected to rise in the upcoming years. The increasing development in technology, more knowledge of advanced products, and changing lifestyles are surging the demand for 3D-enabled gadgets. Furthermore, the growing need for crisp and realistic picture representation, outstanding 3D effects, and an exceptional mapping and navigation experience is propelling the 3D mapping and 3D modeling market.

Rising corruption and theft concerns are the hurdles to the growth of the 3D Mapping and 3D Modeling Software Market.

The animation industry is still vulnerable to corruption and piracy. Companies' software installations are targeted, and pirated copies are sold on the black market. As a result, the industry suffers massive financial losses. Companies have developed surveillance and monitoring techniques to prevent illicit downloads of 3D mapping and modeling software in order to combat piracy. As a result, people have been encouraged to use lawful digital content. In recent years, government policies and regulatory reforms have been put in place to combat piracy. However, adaptable business plans are required to establish mitigation methods and to take proactive steps such as forming anti-piracy cells and promoting awareness. Moreover, in many countries, there is only one policy to avoid theft is to restrict the sites and penalties to illegal users. Thus, theft is the major hurdle in the growth of the 3D Mapping and 3D Modeling Software Market.

Impact of COVID-19 on the 3D Mapping and 3D Modelling Software Market:

The outbreak of the COVID-19 pandemic has increased the consumer demand for 3D mapping and 3D modeling software. Logistics, online learning, healthcare, e-commerce, and other various online business, collaborations experienced significant expansion, well exceeding the limits of their internal and customer-facing applications. For example, iMap9 is a floor-cleaning robot that can explore and clean floors without the need for human assistance. It uses 3D mapping technology to clean the floors. To manage huge volumes of geographical data while satisfying customer requirements, organizations deploy 3D mapping and modeling software solutions. What is 3D Mapping and 3D Modelling Software?

3D mapping software uses machine vision to help in profiling objects in 3D to map them with the real world, offering the recent technical methods, giving the most advanced technical approaches for visualization and information collecting.3D mapping imaging technology and other plenoptic techniques are also utilized to create the 3D effects by finding the light field. 3D modeling is the method of creating a mathematical representation of a three-dimensional object using software. The resulting product is called a 3D model, and these 3-dimension models are used in various different industries. Increasing demand for 3D animation in mobile applications and the development of 3D-enabled display devices for advanced and better navigation are boosting the growth of the 3D mapping and 3D modeling software market.

This layer shows the 3D indoor map of some buildings' interior structures in Hong Kong. It is a set of the data made available by Lands Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

1. Contents

This document describes two datasets collected at Tampere University facilities with samples taken from a Wi-Fi network interface for experiments with indoor positioning based on Wi-Fi fingerprinting.

To reference this dataset, please use

E.S. Lohan et al. “Additional TAU datasets for Wi-Fi fingerprinting-based positioning” 10.5281/zenodo.3819917

Additional reference using these datasets

Torres-Sospedra, J.; Quezada-Gaibor, D.; Mendoza-Silva, G. M.; Nurmi, J.; Koucheryavy, Y. and Huerta, J. New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting Proceedings of the Tenth International Conference on Localization and GNSS (ICL-GNSS), 2020.

Dataset format

Two independent datasets are provided, they are in different folders, namely “Database_Building01” and “Database_Building02” respectively. Each dataset includes two sets of samples:

radio map – a set of Wi-Fi samples collected at a grid of points (reference points);

evaluation – a set of Wi-Fi samples randomly collected in the evaluation area.

Two files are provided for each set that include the rss vectors and the coordinates. For the radio map, the provided files have their names starting with “rm_”; for the evaluation, the evaluation files have their names starting with “eval_”. For instance, for the radio map they are:

rm_crd.csv: holds coordinates (x,y)and floor identifier (z) where the samples were collected;

rm_rss.csv: holds the measured RSSI values from each of the Access Points (AP) detected in each sample;

All the file are described in the same format, and all files are CSV – Comma Separated Values plain text (UTF-8).

Coordinates: Each sample is associated to a pair of coordinates in a 2D Euclidean reference system. The origin of the reference system was chosen arbitrarily for convenience. The units are meters. Therefore, distances between points can be easy calculated. Moreover, the floor identifier is included to enable 3D positioning.

RSSI values: The RSSI values provided as read from the Wi-Fi network interface through the Android API. In each sample, a value of +100 was assigned to each AP not detected during a measurement. No information is provided about the MAC addresses of the APs. However, in the files, the same order is used for all samples, meaning that the values in each column are all associated to the same AP.

Both datasets are independent and none of the provided files include an identifier for each sample. The values in the two provided files are associated by the line number, meaning that the coordinates and RSSI values in the same line, in each file, refer to the same sample.

A 3D map of the Cooper Basin region has been produced over an area of 300 x 450 km to a depth of 20 km (Figure 1). The 3D map was constructed from 3D inversions of gravity data using geological data to constrain the inversions. It delineates regions of low density within the basement of the Cooper/Eromanga Basins that are inferred to be granitic bodies. This interpretation is supported by a spatial correlation between the modelled bodies and known granite occurrences. The 3D map, which also delineates the 3D geometries of the Cooper and Eromanga Basins, therefore incorporates both potential heat sources and thermally insulating cover, key elements in locating a geothermal play. This study was conducted as part of Geoscience Australia's Onshore Energy Security Program, Geothermal Energy Project.

This 3D data release constitutes the first version of the Cooper Basin region 3D map. A future data release (version 2 of the 3D map) will extend the area to the north and east to encompass the entire Queensland extension of the Cooper Basin. The version 2 3D map will incorporate more detailed 3D models of the Cooper and Eromanga Basins by delineating the major internal sedimentary sequences within the basins. Thermal properties will then be incorporated into the 3D map to produce a 3D thermal model. The goal is to produce a 3D thermal model of the Cooper Basin region that not only matches existing temperature and heat flow data in the region, but also predicts regions of high heat flow and elevated temperatures in regions where no heat flow or temperature data exists.

LoD3 (Level of Detail 3) Road Space Models is CityGML dataset which contains road space models (over 50 building models) in the area of Ingolstadt.

There are several approaches to model Building in CityGML 2.0 (e.g. see Biljecki et al.). In our case, due to the acquisition geometry of MLS point clouds, the building objects consist of a very detailed representation of facade elements but on the other hand, it might lack roof elements and entities located in the Building's backyard. Thus, we encourage to see the list below for a detailed description of the Building in our Ingolstadt LoD3 dataset:

The building consists of:

• Ground Surfaces
• Roof Surfaces
• Wall Surfaces
• Outer Ceiling Surfaces
• Outer Floor Surfaces
• Closure Surfaces
• Windows modeled in detail
• Doors modeled in detail
• Building Installations (Balconies, Passages, Arcades, Loggias, Stairs and Porches, (Some) Dormers)
• Textures (approximated based on visual inspection)

Building does NOT consist of:

• Overhanging Building Elements
• Roof structure details
• Objects located in the Building's backyard (not facing the street)
• Building Installations (Chimneys, Rain Gutters, (Some) Dormers, Real (e.g. orthophoto) textures)

The terminology according to SIG3D.

To ensure the highest accuracy geometrically as well as semantically, the dataset was manually modeled based on the mobile laser scannings (MLS) provided by the company 3D Mapping Solutions GmbH (relative accuracy in the range of 1-3cm). Moreover, a complementary OpenDRIVE dataset is available, which includes the road network, traffic lights, fences, vegetation and so on:

• CityGML & SketchUp
  • Download via the releases section
  • Please note, that the 'Download ZIP' button doesn't include the project files due to Git LFS
• OpenDRIVE
• Download via the website of 3D Mapping Solutions (initial registration required)
• Relevant OpenDRIVE dataset is named *Ingolstadt Innercity Halls* and can be found in the demo data area
• Conversion to CityGML can be carried out using the tool r:trån

Further Information:

• Overview: Web-Map-Client (LoD3 models combined with converted OpenDRIVE dataset)
• Creation guideline: Creating LoD3 Models Based on Point Clouds
• License:(CC BY-SA 4.0)

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https://raw.githubusercontent.com/savenow/lod3-road-space-models/main/documentation/images/lod3-models-citygml-with-point-clouds.png" alt="B3" width="800px">

https://raw.githubusercontent.com/savenow/lod3-road-space-models/main/documentation/images/lod3-models-citygml-overview-3d.png" alt="B3" width="800px">

3D building model LoD2-EN For the building model LoD2-DE dataset, standardised roof shapes are formed from point clouds (Airborne laser scanning or photogrammetry) fully automated, assigned to the buildings and aligned according to the actual ridge course. The building floor plan is basically taken from the official digital property map and the model is therefore floor plan compliant.The location accuracy corresponds to that of the underlying building floor plan. The height accuracy is approx. ± 1 m. Coarse deviations are possible in individual cases with complex roof shapes. Shared geometry is managed redundantly. The buildings are additionally blended with terrain information of the Digital Terrain Model (DGM) held at the state operation. There is no manual post-processing of the individual models. The modelling corresponds to the AdV product and quality standard for 3D building models. The timeliness of the data base is usually older from the previous year, in the case of ALS point clouds. (Example: The download LoD2-DE 2023 is based on ALKIS floor plans and point clouds from 2022) The building model LoD2-DE is reserved for the entire urban area of Hamburg (about 750 km²), including the island of Neuwerk. The data can be downloaded as a complete record in CityGML V.1.0 format. Further data formats and excerpts can be obtained at 3d-info@gv.hamburg.de.3D building model LoD2-EN For the building model LoD2-DE dataset, standardised roof shapes are formed from point clouds (Airborne laser scanning or photogrammetry) fully automated, assigned to the buildings and aligned according to the actual ridge course. The building floor plan is basically taken from the official digital property map and the model is therefore floor plan compliant. The location accuracy corresponds to that of the underlying building floor plan. The height accuracy is approx. ± 1 m. Coarse deviations are possible in individual cases with complex roof shapes. Shared geometry is managed redundantly. The buildings are additionally blended with terrain information of the Digital Terrain Model (DGM) held at the state operation. There is no manual post-processing of the individual models.The modelling corresponds to the AdV product and quality standard for 3D building models. The timeliness of the data base is usually older from the previous year, in the case of ALS point clouds.(Example: The download LoD2-DE 2023 is based on ALKIS floor plans and point clouds from 2022) The building model LoD2-DE is reserved for the entire urban area of Hamburg (about 750 km²), including the island of Neuwerk. The data can be downloaded as a complete record in CityGML V.1.0 format. Further data formats and excerpts can be obtained at 3d-info@gv.hamburg.de.

A 3D building model is a digital, numerical surface model of the object areas of buildings and structures defined in the AAA model of the AdV. In principle, the building floor plan will be based on the official digital Property map taken. In LoD2 (Level of Detail), standardized roof shapes are assigned to the buildings and aligned with the actual ridge course. -This dataset is only available free of charge on online retrieval.-

Comparison of the dimensions of the construction site.

3D building model LoD2-DE For the LoD2-DE building model data set, standardized roof shapes are created from point clouds (airborne laser scanning or photogrammetry), assigned to the buildings and aligned according to the actual ridge course. The building floor plan is always taken from the official digital property map, so the model conforms to the floor plan. The positional accuracy corresponds to that of the underlying building floor plan. The height accuracy is approx. ± 1 m. Large deviations are possible in individual cases with complex roof shapes. Shared geometry is maintained redundantly. The buildings are additionally blended with terrain information from the digital terrain model (DGM) kept by the state authority. There is no manual post-processing of the individual models. The modeling corresponds to the AdV product and quality standard for 3D building models. The database is usually up-to-date from the previous year, and in the case of ALS point clouds it is sometimes older. (Example: The LoD2-DE 2023 download is based on ALKIS floor plans and point clouds from 2022) The LoD2-DE building model is available for the entire urban area of ​​Hamburg (approx. 750 km²), including the Neuwerk island. The data can be downloaded as a complete data set in CityGML V.1.0 format. Other data formats and excerpts can be obtained from 3d-info@gv.hamburg.de for a fee.

The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of a greenhouse, achieving precise positioning and navigation by robots has become the primary problem to be solved. Simultaneous localization and mapping (SLAM) technology is a hot spot in solving the positioning and navigation in an unknown indoor environment in recent years. Among them, the SLAM based on a two-dimensional (2D) Lidar can only collect the environmental information at the level of Lidar, while the SLAM based on a 3D Lidar demands a high computation cost; hence, it has higher requirements for the industrial computers. In this study, the robot navigation control system initially filtered the information of a 3D greenhouse environment collected by a 3D Lidar and fused the information into 2D information, and then, based on the robot odometers and inertial measurement unit information, the system has achieved a timely positioning and construction of the greenhouse environment by a robot using a 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of a greenhouse environmental map but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm was used to plan the global navigation path of the robot while the Dynamic Window Approach (DWA) algorithm was used to plan the local navigation path of the robot. Through the positioning test, the average position deviation of the robot from the target positioning point is less than 8 cm with a standard deviation (SD) of less than 3 cm; the average course deviation is less than 3° with an SD of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2, 0.4, and 0.6 m/s, respectively; the average lateral deviation between the actual movement path and the target movement path is less than 10 cm, and the SD is less than 6 cm; the average course deviation is

Building_surface_bavaria_lod1_230516 - The LoD1 (Level of Detail 1) corresponds to the first expansion stage of the 3D building models. Building floor plans from ALKIS® and eave mean height from airborne laser scanning data, ALKIS®-3D building surveys, and the aerial image-based Digital Surface Model serve as the basis for modeling. The dataset has been derived from theLOD2 building models for Bavaria are available as open data via https://geodaten.bayern.de/opengeodata/OpenDataDetail.html?pn=lod2

3D Mapping of Surficial Aquifers contains information regarding the three dimensional distribution and character of surficial materials that may form groundwater aquifers and aquitards.

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Location-Based Services Market Size 2025-2029

The location-based services (LBS) market size is forecast to increase by USD 330 billion, at a CAGR of 30.4% between 2024 and 2029.

The market is witnessing significant growth due to the increasing demand for personal and enterprise navigation services. IoT technologies, such as radar sensors, RFID tags, and Wi-Fi access points, are being integrated into LBS to enhance accuracy and efficiency. Augmented reality (AR) and virtual reality (VR) technologies are also gaining popularity in LBS, providing good experiences for users. Digital maps and 3D mapping are essential components of LBS, offering real-time location information and visual representations of the environment. Artificial intelligence (AI) and satellite-based augmentation are being utilized to improve the accuracy and reliability of LBS.
The 5G IoT infrastructure is expected to further boost the growth of the LBS market by enabling real-time data processing and transmission. Social media and smartphones are also driving the adoption of LBS, as users increasingly rely on location-based services for various applications. However, privacy and security concerns remain a challenge for the LBS market, as location data can be sensitive and vulnerable to misuse. It is crucial for LBS providers to implement strong security measures to protect user data and maintain trust.

What will be the Size of the Location-Based Services (LBS) Market During the Forecast Period?

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The market encompasses a range of technologies and applications that leverage mobile positioning, including Satellite-Based GPS, Enhanced Observed Time Difference (E-OTD), Observed Time Difference of Arrival (OTDOA), Wireless-Assisted Global Navigation Satellite Systems (WAGNSS), and Hybrid Technologies. These positioning technologies are integral to various industries, from Smart City projects to 3D mapping applications, providing real-time geolocation data for connected devices. The market's growth is driven by the proliferation of GPS-enabled smartphones, IoT integration, and the increasing adoption of Augmented Reality (AR) and Virtual Reality (VR) technologies. The market size is significant, with applications spanning navigation services, social media, and various industries, such as transportation, logistics, and retail.
The market's direction is towards more advanced, integrated, and real-time positioning solutions, with an emphasis on improving user experience and enhancing location-based applications' functionality. Geographic location data is a valuable asset in today's data-driven economy, with applications ranging from personalized marketing to emergency response services. The LBS market continues to evolve, with ongoing innovation in positioning technologies, integration with other technologies like 5G networks, and the potential for new applications in areas like autonomous vehicles and drones.

How is this Location-Based Services (LBS) Industry segmented and which is the largest segment?

The location-based services (LBS) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Hardware
  Software
  Services


Type

  Outdoor
  Indoor


Application

  Navigation and tracking
  GIS and mapping
  Geo marketing and advertising
  Social networking and entertainment
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

By Component Insights

The hardware segment is estimated to witness significant growth during the forecast period.

Location-based services (LBS) encompass a range of technologies and applications that leverage real-time data and positioning technologies to deliver customized services and enhance user experiences. These services include mobile positioning through satellite-based GPS, enhanced observed time difference, OTDOA(observed time difference of arrival), wireless-assisted GNSS (global navigation satellite system), a-GNSS (assisted global navigation satellite system), and hybrid technologies. LBS finds applications in various sectors such as smart city projects, 3D mapping applications, e-commerce, mobile apps, artificial intelligence, real-time location tracking, bluetooth beacons, autonomous vehicles, disaster information systems, geolocation data, connected devices, GPS, IoT, augmented reality (AR), virtual reality (VR), navigation and tracking, indoor location services, transportation and logistics, healthcare, 5G infrastructure, and business intelligence.

Hardware components, including passive and active RFID tags, beacons, sensors, and cameras, are

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Metadata Portal Metadata Information

EMAG2v3: the Earth Magnetic Anomaly Grid (2 arc-minute resolution), version 3 is compiled from satellite, ship, and airborne magnetic measurements. Magnetic anomalies result from geologic features enhancing or depressing the local magnetic field. These maps increase knowledge of subsurface structure and composition of the Earth's crust. Global magnetic anomaly grids are used for resource exploration, navigation where GPS is unavailable (submarine, directional drilling, etc.), and for studying the evolution of the lithosphere.The 2017 release of the EMAG2v3 utilizes updated precompiled grids and a revised process for accurately incorporating the long-wavelength anomalies, as modeled by the satellite-based MF7 lithospheric field model. It is an update from the previous EMAG2v3 released by NCEI in 2016. EMAG2v3 further differs from the previous EMAG2 (version 2), which relied on an ocean age model to interpolate anomalies into non-existent data areas and on the earlier MF6 model. EMAG2v3 relies solely on the data available. As a result, EMAG2v3 better represents the complexity of these anomalies in oceanic regions and accurately reflects areas where no data has been collected. The current version reports anomalies in two ways:A consistent altitude of 4 km (referred to as Upward Continued)Anomaly altitude at Sea LevelThis tile layer displays a color relief image of the EMAG2v3 (Upward Continued) rendered with a "hillshade" effect to simulate a 3D surface. A coastline is also provided for reference. The magnetic anomaly values in nanotesla (nT) are displayed using the color ramp below:The EMAG2 dataset illustrates Earth evolution (plate tectonics and crustal interaction with the deep mantle). Distinct patterns and magnetic signatures are attributed to the formation (seafloor spreading) and destruction (subduction zones) of oceanic crust, and the formation of continental crust by accretion of various terranes to cratonic areas and large scale volcanism (both on continents and oceans).Magnetization is weaker at the equator and stronger at high latitudes, reflecting the strength of the ambient geomagnetic field, which induces magnetization in rocksStripes of alternating magnetization in the oceans are due to sea floor spreading and the alternating polarity of the geomagnetic fieldVery old crust (North American Shield, Baltic Shield, Siberian Craton) have strongest magnetization, seen as dark shades of purple and blueThere are four related ArcGIS services providing access to EMAG2v3:Color shaded relief image (tiled, Web Mercator projection)Color shaded relief image (tiled, WGS84 geographic)Multi-layer map serviceImage service (provides data values)

Single photon lidar light detection and ranging (SPL LiDAR) is an active remote sensing technology for: * mapping vegetation aspects including cover, density and height * representing the earth's terrain and elevation contours We acquired SPL data on an airborne acquisition platform under leaf-on conditions to support Forest Resources Inventory (FRI) development. FRI provides: * information to support resource management planning and land use decisions within Ontario’s Managed Zone * information on tree species, density, heights, ages and distribution The SPL data point density ranges from a min of 25pts/m. Each point represents heights of objects such as: * ground level terrain points * heights of vegetation * buildings The lidar was classified according to the Ontario lidar classifications. Low, medium and tall vegetation are classed as 3, 4, 5 and 12 classes. The FRI SPL products include the following digital elevation models: * digital terrain model * canopy height model * digital surface model * intensity model (signal width to return ratio) * forest inventory raster metrics * forest inventory attributes * predicted streams * hydro break lines * block control points Lidar fMVA data supports developing detailed 3D analysis of: * forest inventory * terrain * hydrology * infrastructure * transportation * other mapping applications We made significant investments in Single Photon LiDAR data, now available on the Open Data Catalogue. Derivatives are available for streaming or through download. The map reflects areas with LiDAR data available for download. Zoom in to see data tiles and download options. Select individual tiles to download the data. You can download: * classified point cloud data can also be downloaded via .laz format * derivatives in a compressed .tiff format * Forest Resource Inventory leaf-on LiDAR Tile Index. Download | Shapefile | File Geodatabase | GeoPackage Web raster services You can access the data through our web raster services. For more information and tutorials, read the Ontario Web Raster Services User Guide. If you have questions about how to use the Web raster services, email Geospatial Ontario (GEO) at geospatial@ontario.ca. Note: Internal users replace "https://ws.” with “https://intra.ws." * CHM https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_CHM_SPL/ImageServer * DSM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DSM_SPL/ImageServer * DTM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DTM_SPL/ImageServer * T1 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_Imagery_T1/ImageServer * T2 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_Imagery_T2/ImageServer * Landcover - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Compilation_v2/ImageServer

The SeaBed NSW program provides data on the structure and composition of the seabed adjacent to our beaches. This program is funded through the NSW Coastal Reforms, combined with data from other sources. It improves our understanding of connectivity between coastal (beaches, estuaries) and marine environments, enabling improved approaches to managing and predicting the impacts of human activities, severe storms and climate change.

The program builds on the research expertise and specialist capabilities developed in 2005 as part of the HABMAP program mapping seabed habitats of our marine estate. The SeaBed NSW project maps large areas of the seafloor on the inner continental shelf using airborne laser mapping (LADS) in shallow water depths and vessel-based multi-beam sonar further offshore.

The program contributes to the goal of a continuous high-resolution digital elevation model (DEM) for the entire NSW coast. This new marine LiDAR data builds significantly on our existing database; provides a more accurate 3D surface for our wave modelling tools, enables us to answer questions about how sand moves around the coastal-marine environment and provide baseline information from which to measure change.

Seabed data is generated by collating and analysing bathymetric and marine sediment datasets, and seabed habitats defined from swath acoustic surveys and aerial photography. Data from approximately 120 kilometres of vessel-towed underwater video surveys is also used to allow field validation of swath acoustic data and a description of the visually dominant sessile biota over large areas of the seabed.

An understanding of what type of seabed there is, what it is made of and what is living there is critical so we can better manage coastal hazards and risk for NSW and ensure there are viable and healthy marine ecosystems for the future. The data are intended to inform coastal and marine management and should not be used for navigation without additional processing.

The Seabed NSW program has developed and issued the following datasets:

• NSW Marine LiDAR Topo-Bathy 2018 Geotif
• NSW seabed landforms derived from marine lidar data 2022
• Shellharbour seabed landforms derived from multibeam echosounder data 2022
• Wollongong seabed landforms derived from multibeam echosounder data 2022
• HABMAP Multi-beam Survey Coverage Areas
• Single Beam Bathymetry & Coastal Topography Survey Coverage Areas - Coastal Marine Unit
• NSW Environment Multi Beam Echo Sounder Gridded Data
• NESP Biodiversity Hub Hunter Marine Park Multi Beam Echo Sounder Surveys