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 attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of geo-referenced x, y coordinates and z (elevation), as well as other attributes for each point. Additonal information about the las file format can be found here: https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities. All 3DEP products are public domain.

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Western Australia

Overview

3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

Our corresponding paper (published at ITSC 2022) is available here.
Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

• Python tools to read, generate, and visualize the dataset,
• 3DHDNet deep learning pipeline (training, inference, evaluation) for
  map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@INPROCEEDINGS{9921866,
  author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and 
  Fingscheidt, Tim},
  booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, 
  year={2022},
  pages={627-634}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

• Benjamin Sertolli: Major contributions to our DevKit during his master thesis
• Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json"
with open(json_path) as jf:
  data = json.load(jf)
print(data)

2. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

• traffic signs,
• traffic lights,
• pole-like objects,
• construction site locations,
• construction site obstacles (point-like such as cones, and line-like such as fences),
• line-shaped markings (solid, dashed, etc.),
• polygon-shaped markings (arrows, stop lines, symbols, etc.),
• lanes (ordinary and temporary),
• relations between elements (only for construction sites, e.g., sign to lane association).

3. HD_Map_MetaData

Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

4. HD_PointCloud_Tiles

The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

• x-coordinates: 4 byte integer
• y-coordinates: 4 byte integer
• z-coordinates: 4 byte integer
• intensity of reflected beams: 2 byte unsigned integer
• ground classification flag: 1 byte unsigned integer

After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

import numpy as np
import pptk

file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin"
pc_dict = {}
key_list = ['x', 'y', 'z', 'intensity', 'is_ground']
type_list = ['

The LiDAR Point Cloud Processing Software market is booming, projected to reach $4.57 billion by 2033 with a 15% CAGR. This comprehensive analysis explores market drivers, trends, restraints, key players (Trimble, Bentley Systems, Leica Geosystems), and regional insights, providing valuable data for informed business decisions. Discover the latest in point cloud processing software.

In 2021, a complete airborne LiDAR survey of the Northern Ireland coastline was captured as part of the NI 3D Coastal Survey, providing precise and accurate data of the current coastal morphology.The survey included the intertidal area and extended approximately 200 meters landward of the high-water mark.This is the LiDAR Point Cloud created from the LiDAR data.

surface normals

The LiDAR-equipped iPhone (iPhone-LiDAR) is highly portable for outdoor use and is significantly more cost-effective than other LiDAR-equipped devices such as uncrewed aerial vehicles (UAVs) or ground-based LiDAR. Therefore, this method holds practical promise for small-scale forest measurements. In this study, we conducted terrain mapping and positioning of newly planted small areas in northern Japan, as well as tree height measurements, using iPhone-LiDAR. The iPhone-LiDAR generated highly accurate 3D models, allowing detailed terrain measurements of the planted area. Although there were many false-positive points in the detection of tree positions, manual removal of these points made it possible to create a tree position map. This is believed to significantly reduce the workload compared to traditional survey methods. We also found LiDAR-estimated seedling heights to be underestimated due to some inevitable noise in the point cloud. Improvements in software, scanning, and analytical methodologies will open future possibilities for iPhone-LiDAR applications in forestry practices.

A selection of 3D lidar SLAM point clouds colourised using an arbitrary mobile mapping device and an independent camera. Lineage: The colourisation method is described in the article "Colourising Point Clouds using Independent Cameras" (link below).

This dataset contains terrestrial LiDAR point clouds and associated analysis files collected from five different devices along a predefined route in a favela in São Paulo, Brazil. It includes raw and processed data, aerial drone data, georeferencing references, and CloudCompare analysis files. The dataset supports comparative evaluation of LiDAR performance in dense urban environments and is intended for scientific reuse in the fields of 3D mapping, urban analytics, and infrastructure planning in informal settlements.

This dataset is associated with the article submitted to Scientific Data (Nature Portfolio), titled "Evaluation of Lidar devices and data to conduct terrestrial surveys in favelas"
This dataset is distributed in two parts due to file size limitations. The two subsets are identified by separate DOIs but form a single, unified dataset for scientific reuse:

Dataset Subset A: DOI 10.5281/zenodo.15161438 ;

Dataset Subset B: DOI: 10.5281/zenodo.15161756

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LiDAR Point Cloud Analytics Market Outlook

As per our latest research, the global LiDAR Point Cloud Analytics market size reached USD 1.27 billion in 2024 and is set to grow at a robust CAGR of 16.3% during the forecast period. By 2033, the market is projected to attain a value of USD 4.11 billion, driven by the accelerating adoption of LiDAR technologies in diverse sectors such as urban planning, autonomous vehicles, environmental monitoring, and infrastructure development. The primary growth factor fueling this market is the increasing demand for high-precision geospatial data and advanced analytics capabilities that support smart city initiatives, efficient resource management, and automation across industries.

One of the most significant growth drivers for the LiDAR Point Cloud Analytics market is the rapid proliferation of autonomous vehicles and advanced driver-assistance systems (ADAS). Automotive manufacturers are increasingly integrating LiDAR sensors into their vehicles to enable real-time 3D mapping and object detection, which are crucial for safe navigation and obstacle avoidance. The ability of point cloud analytics to process and interpret massive volumes of spatial data in real time empowers automotive systems to make split-second decisions, thereby enhancing passenger safety and operational efficiency. Additionally, the rising investments in research and development by key industry players and governments are accelerating innovations in sensor technology, data processing algorithms, and cloud-based analytics platforms, further propelling market expansion.

Another major factor driving the growth of the LiDAR Point Cloud Analytics market is its adoption in the construction, mining, and infrastructure sectors. These industries are leveraging LiDAR-based analytics for precise topographic mapping, volumetric analysis, and progress monitoring. LiDAR point cloud data enables stakeholders to create highly accurate digital twins of construction sites, monitor changes over time, and optimize resource allocation. The integration of LiDAR analytics with Building Information Modeling (BIM) and Geographic Information Systems (GIS) allows for seamless collaboration among project teams, reducing errors and minimizing project delays. Furthermore, the increasing focus on sustainable urban development and environmental conservation is driving the use of LiDAR analytics for vegetation analysis, flood risk assessment, and habitat mapping.

The surge in smart city initiatives across the globe is also a pivotal growth catalyst for the LiDAR Point Cloud Analytics market. Governments and municipal authorities are deploying LiDAR solutions for urban planning, infrastructure management, and disaster response. The ability to capture high-resolution 3D models of urban environments enables planners to design efficient transportation networks, assess building footprints, and monitor environmental changes. In addition, the integration of artificial intelligence and machine learning with LiDAR analytics is enhancing the accuracy and automation of feature extraction, anomaly detection, and predictive modeling. This convergence of technologies is creating new opportunities for market players to develop innovative solutions tailored to the evolving needs of urban planners, emergency responders, and environmental agencies.

From a regional perspective, North America currently dominates the LiDAR Point Cloud Analytics market, accounting for the largest share due to the presence of leading technology providers, robust infrastructure, and high adoption rates in automotive, defense, and construction sectors. Europe follows closely, driven by strong government support for smart city projects and environmental monitoring. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid urbanization, increasing investments in infrastructure development, and the emergence of local LiDAR technology providers. Latin America and the Middle East & Africa are also experiencing steady growth, supported by expanding applications in agriculture, mining, and energy sectors.

Component Analysis

The Component segment of the LiDAR Point Cloud Analytics market is bifurcated into Software and Services, with each playing a distinct yet complementary role in the value chain. Software solutions form the backbone of the market, enabling the processing, visualization, and analysis of massive LiDAR point clou

The UAV LiDAR Mapping market is experiencing robust growth, driven by increasing demand for high-precision 3D data across diverse sectors. The market size reached $185.6 million in 2025. Considering the widespread adoption of UAVs for surveying and mapping applications, coupled with the advantages of LiDAR technology in generating accurate and detailed data, a Compound Annual Growth Rate (CAGR) of 15% is a reasonable estimate for the forecast period (2025-2033). This signifies a substantial expansion of the market, projecting a significant value increase by 2033. Key drivers include the rising need for efficient and cost-effective surveying solutions in infrastructure development (highways, urban planning, civil structures), precision agriculture (vegetation mapping, volumetric analysis), and environmental monitoring (forestry, land surveys). Furthermore, technological advancements leading to more compact, efficient, and affordable LiDAR sensors are fueling market expansion. The market is segmented by both the type of LiDAR mapping applications (3D visualization, digital twin creation, vegetation mapping, contour mapping, volumetric analysis) and the industries they serve (land surveys, forestry, quarries, highways, urban planning, civil structures, and others). This segmentation reflects the versatility of UAV LiDAR technology and its applicability across numerous sectors. The geographic distribution of the market is extensive, with significant contributions anticipated from North America and Europe, followed by the Asia-Pacific region. Rapid urbanization, infrastructure development projects, and increasing government investments in digital mapping initiatives in these regions are major contributors to market growth. While potential restraints include regulatory hurdles surrounding drone operations and the high initial investment costs associated with LiDAR systems, the ongoing technological improvements and cost reductions are mitigating these challenges. The competitive landscape is dynamic, with both established surveying companies and specialized drone service providers actively contributing to the market’s expansion. The continued adoption of UAV LiDAR mapping solutions is poised to redefine data acquisition methods for various industries, enabling efficient decision-making and enhanced operational processes.

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.The purpose of G-LiHT’s LiDAR Point Cloud data product (GLLIDARPC) is to provide high-density individual LiDAR return data, including 3D coordinates, classified ground returns, Above Ground Level (AGL) heights, and LiDAR apparent reflectance. GLLIDARPC data are processed as a LAS Version 1.1 binary format specified by the American Society for Photogrammetry and Remote Sensing (ASPRS). The point cloud includes a density of more than 10 points per square meter. A low resolution browse is also provided showing the LiDAR Point Cloud as an Inverse Data Weighted (IDW) interpolation in PNG format.

The LiDAR technology market for mapping is booming, with a projected CAGR of 15% reaching $7.65 Billion by 2033. Discover key trends, applications (autonomous vehicles, architecture, mining), leading companies, and regional market share analysis in this comprehensive market report.

A pilot bathymetric LiDAR survey was commissioned in 2021 which mapped the nearshore areas of Dundrum Bay and areas of Carlingford Lough.For the pilot bathymetric survey a Rapid Airborne Multi-bean Mapping System (RAMMS) operated at approximately 25000 range observations per second, while achieving 3-Secchi disk depth penetration. Where possible data was collected to depths of 10m, however, RAMMS is capable of capturing high resolution data to depths of three times the visual water clarity.This is the LiDAR Point Cloud created from the LiDAR data.

The Airborne Light Detection and Ranging (LiDAR) System market is experiencing robust growth, driven by increasing demand across diverse sectors. Let's assume a 2025 market size of $2.5 billion and a Compound Annual Growth Rate (CAGR) of 12% for the forecast period 2025-2033. This implies significant expansion, reaching an estimated market value of approximately $7 billion by 2033. Key drivers include the rising need for high-precision 3D mapping in civil engineering projects (infrastructure development, surveying, and construction monitoring), the expanding application of LiDAR in forestry and agriculture (precision farming, deforestation monitoring), and its crucial role in autonomous vehicle development and urban planning. Furthermore, advancements in sensor technology, leading to improved accuracy, resolution, and data processing capabilities, fuel market expansion. The market is segmented by application (civil engineering, forestry & agriculture, transportation, urban mapping, others) and by type (topographic LiDAR, bathymetric LiDAR). While data limitations prevent precise regional breakdowns, it’s expected that North America and Europe will maintain significant market shares, driven by early adoption and technological advancements. Growth within the Airborne LiDAR System market is also fueled by government initiatives promoting infrastructure development and environmental monitoring, coupled with the decreasing cost of LiDAR systems, making the technology more accessible to a wider range of users. However, challenges exist; the high initial investment cost associated with acquiring and operating LiDAR systems remains a barrier to entry for some organizations. Further, data processing and analysis can be complex and time-consuming, requiring specialized expertise. Despite these restraints, the continued integration of LiDAR technology into various applications and ongoing technological improvements suggest a consistently expanding market with significant potential for further growth in the coming decade. The leading players in this market, including Hexagon, Trimble, Teledyne Optech, RIEGL, and others, are actively involved in developing innovative solutions and expanding their market presence.

The topographic LiDAR market is experiencing robust growth, driven by increasing demand for high-accuracy 3D mapping and surveying data across various sectors. Applications range from infrastructure development and urban planning to precision agriculture and environmental monitoring. The market's expansion is fueled by advancements in LiDAR technology, offering improved data acquisition speed, higher point density, and enhanced accuracy. Furthermore, the decreasing cost of LiDAR sensors and the rising adoption of cloud-based data processing solutions are contributing to market growth. Let's assume, for illustrative purposes, a 2025 market size of $2.5 billion and a compound annual growth rate (CAGR) of 12% for the forecast period (2025-2033). This suggests a significant expansion of the market, reaching approximately $7 billion by 2033. This growth trajectory is supported by the increasing integration of LiDAR data with other geospatial technologies, facilitating comprehensive analysis and decision-making. However, market growth faces certain challenges. High initial investment costs for LiDAR systems can be a barrier to entry for smaller companies, particularly in developing regions. Moreover, data processing and analysis can be complex and require specialized expertise, limiting widespread adoption. Regulatory hurdles related to data privacy and airspace restrictions can also hinder market expansion. Nevertheless, the ongoing technological advancements, coupled with increasing demand for precise geospatial data across a widening range of applications, are expected to overcome these challenges and drive sustained growth in the topographic LiDAR market. The key players in this market, including Hexagon Geosystems, Trimble, and others, are continuously innovating to improve the technology and expand its reach.

The Airborne 3D LiDAR market is experiencing robust growth, projected to reach a market size of $844 million in 2025, expanding at a Compound Annual Growth Rate (CAGR) of 19% from 2025 to 2033. This significant expansion is driven by increasing demand across diverse sectors. The surging adoption of 3D LiDAR technology in infrastructure development, precision agriculture, and environmental monitoring is a primary catalyst. Advancements in sensor technology, leading to improved accuracy, resolution, and data processing capabilities, are further fueling market growth. Government initiatives promoting the use of advanced surveying and mapping techniques, particularly in urban planning and disaster management, also contribute significantly. Competition is intense, with a mix of established players like Teledyne Geospatial and CHC Navigation alongside emerging innovative companies such as Emesent and SPH Engineering. The market’s geographical distribution is likely diverse, with North America and Europe expected to hold significant market shares due to high adoption rates and technological advancements in these regions. However, the Asia-Pacific region is poised for considerable growth, driven by infrastructure development projects and increasing government investments in surveying and mapping technologies. Restraints on market growth could include the high initial investment costs associated with LiDAR systems and the need for specialized expertise in data acquisition and processing. Nevertheless, the overall market outlook remains strongly positive, indicating continued expansion throughout the forecast period. The competitive landscape is characterized by both established players and emerging companies. Companies like Zhonghaida, Wuhan Zojirushi Technology, and Guangzhou Nanfang Satellite Navigation Instruments are well-established in the Chinese market. International players such as Teledyne Geospatial and RIEGL are leveraging their technological expertise and global reach to maintain a significant presence. The market is witnessing increased innovation, with companies focusing on developing more efficient, cost-effective, and user-friendly LiDAR systems. This includes integration with advanced data processing software and the development of autonomous systems for improved data acquisition efficiency. The evolution of LiDAR technology, coupled with increasing applications, suggests a promising future for the Airborne 3D LiDAR market.