These data were compiled for Cabeza Prieta National Wildlife Refuge (CPNWR) in southern Arizona, to support managment efforts of water resources and wildlife conservation. Objective(s) of our study were to 1) measure water storage capacity at select stage heights in three tanks (also termed tinajas), 2) build a stage storage model to help CPNWR staff accurately estimate water volumes throughout the year, and 3) collect topographic data adjacent to the tanks as a means to help connect these survey data to past or future work. These data represent high-resolution (sub-meter) ground based lidar measurements used to meet these objectives and are provided as: processed lidar files (point clouds), rasters (digital elevation models), and vectors (shapefiles). These data were collected in Southern Arizona at Buckhorn, Eagle, and Senita tanks in the CPNWR from February 13-18, 2022. These data were collected by U.S. Geological Survey - Southwest Biological Science Center - Grand Canyon Monitoring and Research Center (GCMRC) staff for the CPNWR using a Riegl VZ 1000 ground-based lidar to produces ground elevation models georeferenced using control target coordinates collected by a Trimble real-time kinematic (RTK) rover and base station. These data which represent maximum water storage capacity at Buckhorn, Eagle and Senita tanks following sediment removal by CPNWR staff less than one month prior can be used to support management efforts for water resources at these tanks, and wildlife conservation in the CPNWR. Additionally, these data can be used as baseline conditions for evaluating changes in water storage and water storage capacity.

This public data repository (https://public.spider.surfsara.nl/project/lidarac/MAMBO/) provides the LiDAR point cloud datasets which were clipped using the boundary polygons (shapefiles) of the MAMBO demonstration sites. The raw LiDAR point cloud tiles were first downloaded from the national repository in the respective country based on the approximate location of each demonstration site. The data repository uses the storage services from the Dutch IT infrastructure SURF (https://www.surf.nl/en). The code for downloading, clipping and uploading the LiDAR point cloud datasets is available on GitHub (https://github.com/Jinhu-Wang/Retile_Clip_LAZ).

L'objectif principal de ce projet est de développer et de tester un nuage de points optimisé (COPC) évolutif pour la détection et la télémétrie par la lumière (LiDAR) et une interface de programmation d'applications (API) GeoTIFF (CoGS) optimisée pour le cloud, basés sur le Web et facilement accessibles par de nombreux groupes d'utilisateurs. L'API sera conçue pour rendre les données LiDAR détectables et fournir un ensemble d'outils d'analyse simples et de types de formats d'exportation (par exemple, GeoTIFF et geopackage) pour faciliter la détection des changements de paysage. L'API reposera sur un système interopérable basé sur le cloud qui permettra la saisie de données LiDAR haute densité dans les plateformes de stockage de données prétraitées existantes et la connexion à une application en ligne accessible. Le projet utilisera des séries chronologiques de données LiDAR du COPC concernant trois régions étudiées en Colombie-Britannique qui ont connu des événements de modification du paysage en raison des changements climatiques : 1) le lac glaciaire Elliot Creek ; 2) le parc provincial du mont Robson ; et 3) Place Glacier.

Ce projet pilote est le fruit d'une collaboration entre l'Institut Hakai et GeoBC. Toutes les données LiDAR et d'imagerie utilisées et mises à disposition via l'application ont été collectées et traitées par l'équipe géospatiale de l'Institut Hakai dans le cadre du programme Airborne Coastal Observatory (ACO). Les données traitées incluent des fichiers LAZ, des fichiers d'imagerie orthomosaïque standard (.tif) et des rapports de métadonnées spécifiques à la localisation associés. Les nuages de points optimisés pour le cloud (CoPC) mis à disposition via l'application sont une extension de ces fichiers au format LAZ qui sont optimisés pour le stockage dans le cloud et l'accès en streaming aux données des nuages de points LiDAR. De plus, des GeoTIFF (COG) optimisés pour le cloud sont produits à partir des données d'imagerie traitées. Les COG sont un format spécialisé pour le stockage et la diffusion de données matricielles géospatiales optimisé pour le stockage dans le cloud et l'accès Web. Ce format permet un accès efficace à certaines parties des données sans qu'il soit nécessaire de télécharger l'intégralité du fichier.

Cloud Point Cloud Processing Market Outlook

According to our latest research, the global Cloud Point Cloud Processing market size reached USD 2.43 billion in 2024. The market is poised for robust expansion, with a projected compound annual growth rate (CAGR) of 16.8% through the forecast period. By 2033, the market is expected to attain a value of approximately USD 10.98 billion. This rapid growth is primarily driven by the increasing adoption of 3D data and spatial analytics across various industries, as organizations seek advanced solutions to manage, analyze, and visualize complex data sets for improved decision-making and operational efficiency.

A primary growth factor for the Cloud Point Cloud Processing market is the surge in demand for high-precision 3D mapping and modeling across industries such as construction, automotive, and urban planning. As digital transformation accelerates, organizations are increasingly leveraging point cloud processing to convert raw spatial data into actionable intelligence. The proliferation of LiDAR and laser scanning technologies, which generate vast quantities of point cloud data, has created a pressing need for scalable cloud-based processing solutions. These solutions enable seamless storage, management, and real-time analysis of large datasets, facilitating enhanced project planning, design accuracy, and resource optimization. The integration of AI and machine learning algorithms with cloud point cloud processing platforms further enhances automation, accuracy, and speed, making these tools indispensable for modern enterprises.

Another significant driver is the growing emphasis on remote collaboration and data accessibility, especially in the context of globalized operations and distributed teams. Cloud-based point cloud processing platforms allow stakeholders to access, share, and manipulate 3D data from any location, fostering enhanced collaboration and reducing project timelines. This capability is particularly valuable in sectors such as engineering, construction, and asset management, where real-time coordination and data-driven decision-making are critical. The shift toward cloud deployment also reduces the need for extensive on-premises infrastructure, lowering the total cost of ownership and enabling organizations to scale their operations efficiently. As a result, enterprises are increasingly migrating their point cloud processing workloads to the cloud, driving sustained market growth.

The Cloud Point Cloud Processing market is also benefiting from the expanding application landscape, which now encompasses quality inspection, asset management, urban planning, and more. In the manufacturing and automotive sectors, for instance, point cloud processing is being used for precise quality inspection, reverse engineering, and rapid prototyping. In urban planning and smart city initiatives, the technology supports the creation of detailed 3D models for infrastructure development and environmental monitoring. The versatility of cloud-based point cloud processing solutions enables organizations to address a wide range of use cases, thereby expanding the addressable market and fueling innovation. Furthermore, the increasing availability of industry-specific solutions and APIs is making it easier for businesses to integrate point cloud processing into their existing workflows.

From a regional perspective, North America currently dominates the Cloud Point Cloud Processing market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology providers, robust digital infrastructure, and early adoption of advanced spatial analytics solutions contribute to North America's leadership. However, the Asia Pacific region is expected to exhibit the fastest growth over the forecast period, driven by rapid urbanization, infrastructure development, and increasing investments in smart city projects. The market in Europe is also witnessing steady growth, supported by stringent regulatory standards and a strong focus on innovation in industries such as automotive and aerospace. As cloud adoption continues to rise globally, all regions are expected to contribute significantly to the overall market expansion.

NSF Project. PI: Stephen Lancaster, Oregon State University. The survey areas are two rectangular polygons 50-70 kilometers southwest of Eugene, Oregon. Portions of Upper Knowles Creek and Upper Wasson Creek, located in the Oregon Coast Range, were surveyed to investigate sediment storage in debris-flows.

Publications associated with this dataset can be found at NCALM's Data Tracking Center

Archived are Light Detection and Ranging (LiDAR) data collected at Lava Beds National Monument by the NASA funded TUBEX project (Dr. Kelsey Young – PI) and the “LiDAR-Team” in 2017 and 2018. This team was led by Dr. Patrick Whelley and included Drs. W. Brent Garry and Jacob Richardson as well as the rest of the TUBEX Team. The instrument used to collect the LiDAR data was a Riegl VZ-400 tripod mounted terrestrial laser scanner (TLS). The data are tiled for storage and ease of download.

Cloud Storage Software Market Outlook

The cloud storage software market is poised for substantial growth over the next decade, with a projected market size reaching approximately USD 182 billion by 2032, up from USD 69 billion in 2023, reflecting a robust Compound Annual Growth Rate (CAGR) of 11.2%. This remarkable growth trajectory is underpinned by several critical factors, including the increasing adoption of cloud computing technologies across various industries, the need for scalable and flexible storage solutions, and the rising importance of data analytics and business intelligence. The shift towards digital transformation and the growing emphasis on remote work have further accelerated the demand for cloud storage solutions, making this market a focal point for investment and innovation.

One of the primary growth factors driving the cloud storage software market is the exponential increase in data generation. Organizations across the globe are amassing vast amounts of data due to the proliferation of connected devices, social media, and e-commerce activities. This surge in data necessitates efficient storage solutions that can scale seamlessly with growing demands. Cloud storage offers the flexibility to manage large datasets and provides tools for data management, accessibility, and security. Additionally, the cost-effectiveness of cloud storage compared to traditional on-premises solutions is a significant factor encouraging its adoption. Companies can avoid the high upfront costs of physical storage infrastructure, opting instead for a pay-as-you-go model that aligns with their operational needs and budgetary constraints.

Another crucial driver for the market is the increasing emphasis on data security and compliance. As data breaches become more prevalent and regulatory requirements more stringent, organizations are turning to cloud storage solutions that offer robust security features. These solutions often come equipped with advanced encryption, multi-factor authentication, and continuous monitoring capabilities, ensuring data integrity and compliance with industry standards. Furthermore, the ability of cloud storage systems to facilitate backup, disaster recovery, and business continuity planning has become indispensable for businesses aiming to safeguard their critical data and maintain uninterrupted operations in the face of unforeseen events.

The integration of artificial intelligence (AI) and machine learning (ML) technologies into cloud storage systems is another notable trend bolstering market growth. AI and ML enhance the functionality of cloud storage by enabling predictive analytics, automated data classification, and improved resource management. These technologies offer organizations the ability to derive actionable insights from their data, optimize storage resources, and reduce operational costs. As a result, enterprises are increasingly investing in cloud storage solutions that incorporate AI and ML capabilities, further driving the market's expansion. This integration also facilitates more efficient and intelligent data management strategies, allowing businesses to capitalize on their data assets for competitive advantage.

The convergence of Cloud and Internet of Things (IoT) Storage Technologies is revolutionizing the way data is managed and utilized across industries. As IoT devices proliferate, they generate massive volumes of data that require efficient storage solutions capable of handling real-time data processing and analytics. Cloud storage offers a scalable and flexible platform to accommodate this influx of data, enabling seamless integration with IoT systems. This synergy not only enhances data accessibility and management but also facilitates the development of smart applications and services. By leveraging cloud and IoT storage technologies, businesses can unlock new opportunities for innovation, optimize operations, and deliver enhanced customer experiences.

Deployment Type Analysis

The deployment type is a critical consideration in the cloud storage software market, encompassing public cloud, private cloud, and hybrid cloud models. Public cloud storage solutions have gained prominence due to their scalability, cost-effectiveness, and ease of access. Organizations leverage public cloud offerings to scale storage needs dynamically and reduce infrastructure overhead. With tech giants like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud leading the charge, public cloud storage solutions are

The market is segmented into the following types and applications: Report Coverage & Deliverables Market Segmentations: Type:

Point Cloud Processing Software GIS Integration Software Others

Application:

Land Surveying and Mapping Urban Planning and Design Environmental Monitoring Water Resources Management Others

Regional Insights:

North America: Largest market due to high adoption in construction and infrastructure projects Europe: Growing demand for environmental monitoring and urban planning Asia-Pacific: Rapid urbanization and increasing investments in infrastructure Rest of the World: Emerging markets with potential for growth

Lidar Data Processing Software Trends Driving Forces:

Increasing adoption of lidar technology in various industries Growing need for accurate and detailed data for decision-making Advancements in cloud computing and artificial intelligence

Challenges and Restraints:

High cost of lidar data collection and processing Limited availability of skilled professionals Data storage and management challenges

Emerging Trends:

Integration of lidar data with other data sources Real-time data processing and visualization Automated workflows and machine learning

Growth Catalysts:

Government initiatives to promote lidar technology Increasing awareness of the benefits of lidar data Collaboration between industry players

Leading Players in the Lidar Data Processing Software

Trimble: Faro Technologies: ESRI: L3Harris Geospatial: Leica Geosystems: Autodesk: PointCloud International: Beijing Yupont Electric Power Technology Co., Ltd.: Blue Marble Geographics: Terrasolid: Beijing Green Valley Technology Co., Ltd: RIEGL Laser Measurement Systems: QCoherent Software: TopoDOT: Merrick & Company: Teledyne Optech: RiAcquisition: RIEGL Software: SLAMTEC: LizarTech:

Significant Developments in Lidar Data Processing Software Sector

Partnerships between software providers and lidar sensor manufacturers Investment in research and development to enhance software capabilities Growing adoption of cloud-based solutions for data storage and processing

description: QSI has completed the acquisition and processing of Light Detection and Ranging (LiDAR) data describing the Oregon LiDAR Consortium's (OLC) Umpqua Study Area. The Umpqua area of interest (AOI) shown in Figure 1 encompasses 1,414,070 acres. The collection of high resolution geographic data is part of an ongoing pursuit to amass a library of information accessible to government agencies as well as the general public. LiDAR data occurred between February 14 and November 12, 2015. Settings for LiDAR data capture produced an average resolution of at least eight pulses per square meter. Final products created include lidar point cloud data, three foot digital elevation models of highest hit and bare earth ground models, 1.5 foot intensity rasters, study area vector shapes, and corresponding statistical data. Final deliverables were projected in Oregon Statewide Lambert Conformal Conic. Lidar point clouds were projected back to geographic coordinates for storage in the Digital Coast Data Access Viewer.; abstract: QSI has completed the acquisition and processing of Light Detection and Ranging (LiDAR) data describing the Oregon LiDAR Consortium's (OLC) Umpqua Study Area. The Umpqua area of interest (AOI) shown in Figure 1 encompasses 1,414,070 acres. The collection of high resolution geographic data is part of an ongoing pursuit to amass a library of information accessible to government agencies as well as the general public. LiDAR data occurred between February 14 and November 12, 2015. Settings for LiDAR data capture produced an average resolution of at least eight pulses per square meter. Final products created include lidar point cloud data, three foot digital elevation models of highest hit and bare earth ground models, 1.5 foot intensity rasters, study area vector shapes, and corresponding statistical data. Final deliverables were projected in Oregon Statewide Lambert Conformal Conic. Lidar point clouds were projected back to geographic coordinates for storage in the Digital Coast Data Access Viewer.

The Virginia LiDAR Inventory web map provides access to LiDAR point cloud and individual project metadata collected in the Commonwealth of Virginia according to the USGS 3DEP specification. Data is obtained from NOAA and USGS data portals. LiDAR Point Clouds are compressed for file storage and transfer. This map shows the spatial extents and status of LiDAR acquisition projects in Virginia. Metadata, Point Cloud, and DEMs (where hosted) are available via inventory polygons and a download tile grid which appears when zoomed in.Contact:For questions about the data that is downloaded please contact USGS

In response to growing concerns about the impacts of climate change and the need for sustainable urban development, urban forests have emerged as a crucial tool for mitigating climate change impacts and enhancing the quality of life in cities. Previous studies have established that urban forests provide a wide range of ecosystem services, including air purification, temperature regulation, and carbon sequestration. However, precise estimation of urban tree carbon storage remains a key challenge for effective urban forest management and planning. In this work, we expand on this body of work by investigating the carbon storage of trees on the UBC Vancouver Campus using 2018 Light Detection and Ranging (LiDAR) data sourced from the City of Vancouver. The aim was to determine the total carbon storage and the average carbon density of the campus. Tree height and structure were estimated using an existing model, which facilitated the calculation of individual tree biomass and carbon storage based on the LiDAR data. The results revealed that the UBC Vancouver Campus has a total carbon storage of 24.63 Gg and an average carbon density of 6.13 kg m-2. These findings emphasize the significant role urban forests play in climate change mitigation and urban life improvement. Employing LiDAR data in conjunction with the existing model proved to be an efficient and effective method for estimating urban tree carbon storage. The results can inform urban planning and policy decisions, fostering the integration of urban forests into sustainable campus development.

The Mobile Laser Scanning Systems market is experiencing robust growth, driven by increasing demand across diverse sectors like construction, mining, and agriculture. The market's expansion is fueled by the technology's ability to provide accurate and detailed 3D data rapidly, improving efficiency and reducing project costs. Applications range from site surveying and asset management to accident investigation and precision farming. While the real-time systems segment currently holds a larger market share, post-processing systems are witnessing significant growth due to advancements in processing power and software capabilities. Leading players like Leica Geosystems, Trimble, and Topcon are investing heavily in R&D, incorporating AI and automation into their systems, further fueling market expansion. Geographic growth is diverse, with North America and Europe currently dominating due to early adoption and well-established infrastructure. However, the Asia-Pacific region is showing substantial potential for future growth, driven by rapid infrastructure development and increasing government investments in digitalization. The market faces some challenges including high initial investment costs for the systems and the need for skilled professionals to operate and process the data. Nevertheless, the overall market outlook remains positive, with a projected Compound Annual Growth Rate (CAGR) driving substantial market expansion through 2033. The competitive landscape is characterized by a mix of established industry giants and innovative startups. Established players leverage their extensive distribution networks and strong brand recognition while newcomers focus on niche applications and innovative technological advancements. The market is likely to see increased consolidation in the coming years, with larger players acquiring smaller companies to expand their product portfolios and technological capabilities. Future growth will be significantly influenced by technological advancements, such as the integration of LiDAR with other technologies like drones and autonomous vehicles, which will open up new applications and markets. The increasing adoption of cloud-based solutions for data processing and storage will also influence market dynamics, facilitating collaboration and data sharing among stakeholders. Regulatory changes, particularly related to data privacy and safety standards, will also play a role in shaping the future of the mobile laser scanning market.

The handheld imaging laser scanner market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market's expansion is fueled by several key factors. Firstly, the construction industry leverages these scanners for precise documentation, accelerating project timelines and enhancing quality control. Similarly, facility management benefits from detailed as-built models for efficient maintenance and space planning. The archaeology and forensic examination fields utilize the technology for meticulous site recording and evidence analysis, highlighting its versatility. Furthermore, the rise of virtual reality (VR) content creation necessitates high-quality 3D models, boosting demand for handheld scanners. While precise market sizing data is unavailable, considering the current CAGR and reported market values for related technologies, we can reasonably estimate the 2025 market size to be around $500 million, projecting a steady upward trajectory. The market is segmented by scanning speed (≤320,000 points per second and 320,000-420,000 points per second), reflecting varying application needs and technological advancements. Major players like Leica, Stonex, ComNav Technology, Geosun Navigation, and FJDynamics are actively shaping the market landscape through innovation and competitive pricing strategies. Geographic expansion continues across North America, Europe, and the Asia-Pacific region, with North America holding a significant market share due to early adoption and technological advancements. Looking ahead, several trends are shaping the future of the handheld imaging laser scanner market. The integration of advanced features like improved point cloud processing and automated data analysis is driving efficiency gains. Miniaturization and improved portability are enhancing usability, making the technology accessible to a wider range of users. The increasing availability of cloud-based data processing and storage solutions simplifies workflow and facilitates collaboration. Despite the promising outlook, the market faces certain restraints, including the high initial investment cost of the scanners and the need for skilled operators. However, these challenges are likely to be mitigated by ongoing technological advancements that enhance user-friendliness and reduce overall operating expenses. The forecast period from 2025-2033 anticipates sustained growth based on the underlying drivers and market trends, with a projected CAGR of approximately 15%. This growth will primarily be driven by the expansion of applications in various industries, particularly in developing economies where infrastructure development is rapidly accelerating.

description: Quantum Spatial collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Big Windy 2015 study area. This study area is located near Glendale, Oregon. The collection of high resolution geographic data is part of an ongoing pursuit to amass a library of information accessible to government agencies as well as the general public. In June 2015 QSI employed remote-sensing lasers in order to obtain a total area flown of 131,357 acres. Settings for LiDAR data capture produced an average resolution of at least eight pulses per square meter. The LiDAR survey occurred between June 26, 2015 and July 3, 2015 utilizing a Leica ALS70 mounted in a Cessna Grand Caravan. The systems were programmed to emit single pulses at around 198 kHz and flown at 1,400 m AGL, capturing a scan angle of 15 degrees from nadir. These settings were developed to yield points with an average native density of greater than eight pulses per square meter over terrestrial surfaces. Final products created include 3-inch orthophotos, RGB extracted (from NAIP imagery) LiDAR point cloud data, three foot digital elevation models of highest hit and bare earth ground models, 1.5 foot intensity rasters, study area vector shapes, and corresponding statistical data. Final deliverables were projected in Oregon Statewide Lambert Conformal Conic. Lidar point clouds were projected back to geographic coordinates for storage in the Digital Coast Data Access Viewer.; abstract: Quantum Spatial collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Big Windy 2015 study area. This study area is located near Glendale, Oregon. The collection of high resolution geographic data is part of an ongoing pursuit to amass a library of information accessible to government agencies as well as the general public. In June 2015 QSI employed remote-sensing lasers in order to obtain a total area flown of 131,357 acres. Settings for LiDAR data capture produced an average resolution of at least eight pulses per square meter. The LiDAR survey occurred between June 26, 2015 and July 3, 2015 utilizing a Leica ALS70 mounted in a Cessna Grand Caravan. The systems were programmed to emit single pulses at around 198 kHz and flown at 1,400 m AGL, capturing a scan angle of 15 degrees from nadir. These settings were developed to yield points with an average native density of greater than eight pulses per square meter over terrestrial surfaces. Final products created include 3-inch orthophotos, RGB extracted (from NAIP imagery) LiDAR point cloud data, three foot digital elevation models of highest hit and bare earth ground models, 1.5 foot intensity rasters, study area vector shapes, and corresponding statistical data. Final deliverables were projected in Oregon Statewide Lambert Conformal Conic. Lidar point clouds were projected back to geographic coordinates for storage in the Digital Coast Data Access Viewer.

As an important part of agricultural cash crops in Hainan Province, more attention should be paid to monitoring sample plots of rubber trees. We developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with Multiview Stereo method and sample plot survey data. Deviation analyses and accuracy evaluations of quadrat information were performed in the study area, covering different age classes and morphological characteristics, to explore its practical value in monitoring rubber forest sample plots. Furthermore, the relationship between under branch height, diameter at breast height (DBH), and rubber tree volume was explored, and a rubber tree binary volume model was established. Here, we innovatively proposed a planning scheme for a terrestrial photogrammetry system for the sustainable management of rubber forests, to provide a novel solution to the issues faced by current sample plot monitoring practices. In the future, the application of a terrestrial photogrammetry system to other types of forest monitoring will gradually be explored.The following is the data package of our research: 1. The data table in the folder (Coordinate) is the point position coordinate information of our sample shooting image. 2. The data table in the folder (Data) is the data summary table of our study, which includes the measurement value of rubber forest volume, the measurement value of rubber forest DBH, the measurement value of under branch height and the specific information of each rubber tree in 19 sample plots. 3. The data in the folder (Figures) is the relevant illustration in the research paper. Due to some missing data in the process of saving, we have supplemented and uploaded the data of the rubber forest 3D point cloud data obtained by the terrestrial photogrammetry system in the data set. If you need, please feel free to contact us. Due to the large amount of data storage, can not upload data website, we will provide you with a net disk download link.

The Virginia LiDAR Inventory Web Mapping Application provides access to LiDAR point cloud and individual project metadata collected in the Commonwealth of Virginia according to the USGS 3DEP specification. Data is obtained from NOAA, USGS, and VGIN data portals. LiDAR Point Clouds are compressed for file storage and transfer. USGS and NOAA utilize the compressed .LAZ format. This dataset will provide the end user a necessary set of geographic extents that can be used with an ArcGIS Desktop or Pro session to select by location specific areas of download. The downloads can either be batch processed by the analysis with scripting and modeling or individual tiles can be downloaded. This is the tile data powering VGIN ArcGIS server services utilized in the VGIN LiDAR Download Application.

The Cloud Data Loss Prevention (CDLP) market is experiencing robust growth, driven by the increasing adoption of cloud technologies and the rising concerns surrounding data breaches and regulatory compliance. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033, reaching approximately $15 billion by 2033. Key drivers include the escalating volume of sensitive data stored in the cloud, stringent data privacy regulations like GDPR and CCPA, and the growing sophistication of cyberattacks targeting cloud environments. The IT and Telecom sector currently dominates the application segment, followed by BFSI and Healthcare, reflecting the high concentration of sensitive data within these industries. However, growing cloud adoption across manufacturing, retail, and energy sectors is expected to fuel significant growth in these segments over the forecast period. The Network DLP type currently holds the largest market share, but Endpoint and Storage DLP solutions are gaining traction due to their enhanced capabilities in protecting data across various access points. North America and Europe currently represent the largest regional markets, fueled by early adoption of cloud technologies and stringent data protection regulations. However, Asia Pacific is poised for significant growth, driven by increasing digitalization and a rising awareness of data security. Restraints to market growth include the high cost of implementation, the complexity of integrating CDLP solutions with existing IT infrastructures, and the ongoing skills gap in managing and maintaining these solutions. The competitive landscape is characterized by a mix of established players and emerging vendors. Major players like McAfee, Symantec, and Check Point Software Technologies are leveraging their existing security expertise to expand their CDLP offerings. Meanwhile, specialized CDLP vendors like CipherCloud and Digital Guardian are focusing on innovation and niche market penetration. The market is expected to witness further consolidation through mergers and acquisitions as companies seek to expand their product portfolios and global reach. Future growth will be significantly influenced by advancements in artificial intelligence (AI) and machine learning (ML) capabilities within CDLP solutions, allowing for more proactive threat detection and automated response mechanisms. The ongoing evolution of cloud computing architectures, including serverless and multi-cloud environments, will also necessitate the development of more adaptable and integrated CDLP solutions to effectively address the evolving threat landscape.

WSI, a Quantum Spatial company, has collected Light Detection and Ranging (LiDAR) data for the Oregon LiDAR Consortium (OLC) Colville study area. This study area is located in northeastern Washington, abutting Canada. The collection of high resolution geographic data is part of an ongoing pursuit to amass a library of information accessible to government agencies as well as the general public. In August 2014 WSI employed remotesensing lasers in order to obtain a total area flown of 171,091 acres. Settings for LiDAR data capture produced an average resolution of at least eight pulses per square meter. Final products created include LiDAR point cloud data, one meter digital elevation models of bare earth ground models, ground density rasters, and highest-hit returns, one-half meter intensity rasters, study area vector shapes, and corresponding statistical data. Final deliverables are projected in United States Forest Service (USFS) Region 6 Albers. NOAA Office for Coastal Management has reprojected the point cloud data to geographic coordinates and ellipsoid heights for storage in the Digital Coast Data Access Viewer. Custom processing may have further changed the projection and datums. See the spatial reference information and processing steps for details.

The Retail Cloud Security market is experiencing robust growth, projected to reach $5.14 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 13.44% from 2025 to 2033. This expansion is fueled by the increasing adoption of cloud-based technologies by retail businesses to enhance operational efficiency and customer experience. The rising threat landscape, including data breaches, cyberattacks, and ransomware, compels retailers to prioritize robust cloud security measures. Key drivers include the proliferation of omnichannel retail strategies, the expanding use of IoT devices within retail environments, and the growing reliance on sensitive customer data for personalized marketing and improved services. Market segmentation reveals a strong demand across various solutions, including Identity and Access Management (IAM), Data Loss Prevention (DLP), and Security Information and Event Management (SIEM), as well as across deployment models like private, hybrid, and public clouds. North America currently holds a significant market share, driven by early adoption of cloud technologies and stringent data privacy regulations. However, Asia Pacific is expected to witness substantial growth, fueled by rising e-commerce penetration and increasing digitalization within the retail sector. The competitive landscape is marked by a mix of established players like IBM, Cisco, and Trend Micro, and specialized security vendors focusing on retail-specific solutions. The market is expected to see continued consolidation as companies seek to expand their offerings and cater to the evolving needs of retailers. Challenges include the complexity of managing cloud security across diverse environments, the skills gap in cybersecurity expertise, and the rising costs associated with implementing and maintaining comprehensive security solutions. Nevertheless, the long-term outlook for the Retail Cloud Security market remains positive, driven by ongoing digital transformation within the retail industry and the increasing awareness of the critical importance of protecting sensitive customer data and intellectual property. Future growth will likely be shaped by the emergence of advanced security technologies like AI-driven threat detection and the increasing adoption of cloud-native security solutions. Recent developments include: March 2023: IBM revised its Storage Defender solution by adding Cohesity data protection. Cohesive Data Protect’s integration into IBM Storage Defender will boost business resilience for organizations’ hybrid cloud systems by minimizing data fragmentation and maximizing availability. Cohesity DataProtect positions clients to copy and store data on numerous servers, on-site or off-site, or in various clouds. In the event of a disaster or cyber security breach, this helps to ensure that a current and exact copy of the data is available., January 2023: HDFC Bank adopted Microsoft’s Azure to consolidate and modernize its enterprise data landscape, scaling its information management capabilities across enterprise reporting and advanced analytics through artificial intelligence. Using the Microsoft Cloud Platform and technology built on AI/ML, uniform architecture, and security, The solution will provide consumers with stringent security and regulatory requirements., December 2022: Tata Motor signed a deal with Oracle for its cloud solutions that would provide enhanced business insights, improved security, increased flexibility, and reduced costs. The automotive player was to transform its Dealer Management System (DMS), which contains data of 60,000 customers, to Oracle Cloud Infrastructure. Oracle was expected to help the automotive leader monitor sales performance and share insights to improve collaboration across its dealer network.. Key drivers for this market are: Growing Threats of Cyber Attacks are Augmenting Market Growth. Potential restraints include: Growing Threats of Cyber Attacks are Augmenting Market Growth. Notable trends are: Intrusion Detection and Prevention to Register a Significant Growth.

LiDAR (Light Detection And Ranging) is a modern survey method that produces three-dimensional spatial information in the form of a data point cloud. LiDAR is an active remote sensing system; it produces its own energy to acquire information, versus passive systems, like cameras, that only receive energy. LiDAR systems are made up of a scanner, which is a laser transmitter and receiver; a GNSS (GPS) receiver; and an inertial navigation system (INS). These instruments are mounted to an aircraft. The laser scanner transmits near-infrared light to the ground. The light reflects off the ground and returns to the scanner. The scanner measures the time interval and intensity of the reflected signals. This information is integrated with the positional information provided by the GNSS and INS to create a three-dimensional point cloud representing the surface. A LiDAR system can record millions of points per second, resulting in high spatial resolution, which allows for differentiation of many fine terrain features. Point clouds collected with LiDAR can be used to create three-dimensional representations of the Earth’s surface, such as Digital Elevation Models (DEMs) and Digital Surface Models (DSMs). DEMs model the elevation of the ground without objects on the surface, and DSMs model ground elevations as well as surface objects such as trees and buildings. LidarBC's Open LiDAR Data Portal (see link under Resources) is an initiative to provide open public access to LiDAR and associated datasets collected by the Government of British Columbia. The data in the portal is released as Open Data under the Open Government Licence – British Columbia (OGL-BC). Four Government of British Columbia business areas and one department of the Government of Canada make LiDAR data available through the portal: * GeoBC * Emergency Management and Climate Readiness (EMCR) * BC Timber Sales (BCTS) * Forest Analysis and Inventory Branch (FAIB) * Natural Resources Canada (NRCan) GeoBC is the provincial branch that oversees and manages LidarBC’s Open LiDAR Data Portal, including storage, distribution, maintenance, and updates. Please direct questions to LiDAR@gov.bc.ca.