Mobile Mapping Vans for Corridor Scanning Market Outlook

According to our latest research, the global market size for Mobile Mapping Vans for Corridor Scanning reached USD 2.38 billion in 2024, with a robust year-on-year growth trajectory. The sector is experiencing a significant compound annual growth rate (CAGR) of 13.2% from 2025 to 2033, driven by rapid advancements in geospatial technologies and increasing demand for precision mapping across various industries. By 2033, the market is forecasted to reach USD 6.98 billion, underlining the expanding adoption of mobile mapping solutions for corridor scanning in transportation, utilities, and urban infrastructure projects worldwide. Key growth factors include the integration of advanced sensor technologies, the rising need for efficient asset management, and the push for smart infrastructure development.

One of the primary growth drivers for the Mobile Mapping Vans for Corridor Scanning Market is the escalating demand for high-precision geospatial data in transportation infrastructure projects. Governments and private stakeholders are increasingly prioritizing the modernization of roadways, railways, and utility corridors, necessitating rapid and accurate mapping solutions. Mobile mapping vans equipped with advanced LiDAR, photogrammetry, and GNSS technologies enable the efficient collection of corridor data, facilitating detailed planning, monitoring, and maintenance. The ability to capture real-time, high-resolution spatial information significantly reduces project timelines and costs, making these solutions indispensable for large-scale corridor management and rehabilitation initiatives. Additionally, the integration of AI-driven analytics and cloud-based data processing further enhances the value proposition of mobile mapping vans, allowing stakeholders to derive actionable insights from complex geospatial datasets.

Another major growth factor is the increasing emphasis on urban planning and smart city initiatives across both developed and emerging economies. Urban planners and municipal authorities are leveraging mobile mapping vans for corridor scanning to support the design and implementation of intelligent transportation systems, utility networks, and public safety infrastructure. The real-time mapping capabilities of these vans enable comprehensive asset inventories, accurate condition assessments, and proactive maintenance strategies, all of which are critical for sustainable urban development. Moreover, the growing adoption of 5G networks and IoT devices is amplifying the need for precise geospatial data, further propelling market expansion. As cities strive to enhance mobility, reduce congestion, and improve overall quality of life, mobile mapping vans are becoming essential tools in the urban planning toolkit.

The market is also benefiting from the rising demand for environmental monitoring and regulatory compliance. Industries such as oil & gas, mining, and utilities are under increasing pressure to adhere to stringent environmental standards and minimize their ecological footprint. Mobile mapping vans equipped with advanced sensor arrays and analytical software offer a cost-effective solution for monitoring corridor conditions, detecting anomalies, and ensuring compliance with regulatory requirements. Additionally, the ability to rapidly deploy these vans across remote or challenging terrains makes them ideal for large-scale environmental assessments and disaster response operations. The convergence of regulatory mandates and technological innovation is thus creating new avenues for market growth, particularly in regions with heightened environmental awareness and enforcement.

Regionally, North America continues to dominate the Mobile Mapping Vans for Corridor Scanning Market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of well-established infrastructure, significant investments in smart city projects, and a strong focus on technological innovation are key factors driving market leadership in these regions. Meanwhile, the Asia Pacific market is witnessing the fastest growth, fueled by rapid urbanization, government-led infrastructure modernization programs, and increasing adoption of digital technologies in countries such as China, India, and Japan. Latin America and the Middle East & Africa are also emerging as promising markets, supported by ongoing infrastructure development and a growing emphasis on sustainable urban planning.

Mobile LiDAR Mapping Service (Civil Works) Market Outlook

According to our latest research, the global Mobile LiDAR Mapping Service (Civil Works) market size reached USD 2.15 billion in 2024, driven by the growing demand for high-precision geospatial data in infrastructure projects. The market is expected to expand at a robust CAGR of 13.7% during the forecast period, reaching a projected value of USD 6.01 billion by 2033. This growth is fueled by the rapid adoption of advanced mapping technologies across civil engineering, urban planning, and infrastructure maintenance sectors, as organizations increasingly recognize the efficiency, accuracy, and cost-effectiveness of Mobile LiDAR for large-scale civil works applications.

One of the primary growth factors for the Mobile LiDAR Mapping Service (Civil Works) market is the surging demand for precise and real-time geospatial data in the construction and maintenance of transportation infrastructure. As roadways, bridges, and railways undergo expansion and modernization globally, stakeholders are turning to Mobile LiDAR solutions for topographic mapping, corridor analysis, and asset management. The ability of Mobile LiDAR to quickly and safely collect highly accurate three-dimensional data, even in challenging or inaccessible environments, is transforming how civil works projects are planned, executed, and monitored. This not only accelerates project timelines but also reduces costs associated with manual surveying and minimizes risks to personnel, further driving market adoption.

Another significant growth driver is the integration of Mobile LiDAR mapping with digital twin technologies and Building Information Modeling (BIM). As governments and private enterprises invest in smart city initiatives and infrastructure digitalization, the need for comprehensive, up-to-date, and interoperable geospatial data has never been greater. Mobile LiDAR mapping services provide the foundational datasets required for creating accurate digital replicas of physical assets, enabling predictive maintenance, enhanced asset management, and data-driven urban planning. This convergence of LiDAR with advanced analytics and visualization platforms is opening new avenues for service providers, while also pushing the boundaries of what is possible in civil works engineering and management.

Environmental regulations and the growing emphasis on sustainability in infrastructure development are also propelling the Mobile LiDAR Mapping Service (Civil Works) market forward. Regulatory authorities increasingly mandate detailed environmental impact assessments and ongoing monitoring for large-scale projects. Mobile LiDARÂ’s ability to deliver high-resolution terrain and vegetation data supports compliance, biodiversity management, and the minimization of ecological footprints. Furthermore, the technologyÂ’s non-intrusive nature ensures minimal disruption to natural habitats during data collection, aligning with global sustainability goals. Together, these factors are making Mobile LiDAR mapping indispensable for environmentally conscious civil engineering projects.

The application of Infrastructure LiDAR is revolutionizing the way infrastructure projects are approached and executed. By providing precise and comprehensive geospatial data, Infrastructure LiDAR supports the planning, design, and maintenance of critical infrastructure systems. This technology is particularly beneficial in areas such as transportation, where accurate mapping and analysis are crucial for the development of roadways, bridges, and rail networks. The ability to capture detailed three-dimensional data in real-time allows for more informed decision-making, enhancing the efficiency and safety of infrastructure projects. As the demand for modernized infrastructure grows globally, the integration of Infrastructure LiDAR into project workflows is becoming increasingly essential.

Regionally, North America continues to dominate the Mobile LiDAR Mapping Service (Civil Works) market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States leads in technology adoption, thanks to substantial investments in infrastructure modernization and a mature ecosystem of LiDAR service providers. Meanwhile, Asia Pacific is witnessing the fastest growth, buoyed by massive infrastructure development i

Roads have multiple effects on wildlife, from animal mortality, habitat and population fragmentation, to modification of animal reproductive behavior. Amphibians in particular, due to their activity patterns, population structure, and preferred habitats, are strongly affected by traffic intensity and road density. On the other hand, road-kills studies and conservation measures have been extensively applied on highways, although amphibians die massively on country roads, where conservation measures are not applied. Many countries (e.g. Portugal) have not any national program for monitoring road-kills, a common practice in other European countries (e.g. UK; The Netherlands). This is necessary to identify hotspots of road-kills in order to implement conservation measures correctly. However, monitoring road-kills is expensive and time consuming, and depend mainly on volunteers. Therefore, cheap, easy to implement, and automatic methods for detecting road-kills over larger areas (broad monitoring) and along time (continuous monitoring) are necessary. We present here the preliminary results from a research project which aims to build a cheap and efficient system for detecting amphibians roadkills using computer-vision techniques from robotics. We propose two different solutions: 1) a Mobile Mapping System to detect automatically amphibians’ road-kills in roads, and 2) a Fixed Detection System to monitor automatically road-kills in a particular road place during a long time. The first methodology will detect and locate road-kills through the automatic classification of road surface images taken from a car with a digital camera, linked to a GPS. Road kill casualties will be detected automatically in the image through a classification algorithm developed specifically for this purpose. The second methodology will detect amphibians crossing a particular road point, and determine if they survive or not. Both Fixed and Mobile system will use similar programs. The algorithm is trained with existing data. For now, we can only present some results about the Mobile Mapping System. We are performing different tests with different cameras, namely a lineal camera, used in different industrial solutions of control quality, and an outdoor Go-pro camera, very famous on different sports like biking. Our results prove that we can detect different road-killed and live animals to an acceptable car speed and at a high spatial resolution. Both Mapping Systems will provide the capacity to detect automatically the casualties of road-kills. With these data, it will be possible to analyze the distribution of road-kills and hotspots, to identify the main migration routes, to count the total number of amphibians crossing a road, to determine how many of that individuals are effectively road-killed, and to define where conservation measures should be implemented. All these objectives will be achieved more easily at with a lower cost in funds, time, and personal resources.

Date of mapping: 17-10-20Lab. method: Machigin (GOST 26205-91)Unit: mg/100gDate of sampling: 2015-2019Mapping method: Quantile regression forestMean error: -0.15RMSE: 1.6Uncertainty map: half-width of 90% confidence intervalData provider: Centre of the Agricultural Services, SNCOContact: stepan.davtyan@yahoo.com

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)

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

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">

Mobile Robot Dataset Versioning Market Outlook

According to our latest research, the global mobile robot dataset versioning market size reached USD 412 million in 2024, and is expected to grow at a robust CAGR of 16.2% during the forecast period, reaching approximately USD 1.15 billion by 2033. This growth is primarily driven by the increasing adoption of mobile robots across diverse industries and the critical need for robust dataset management solutions to ensure accurate training, deployment, and continuous improvement of autonomous systems. The proliferation of AI-powered robots and rapid advancements in machine learning algorithms are further fueling the demand for sophisticated dataset versioning platforms, enabling organizations to manage, track, and audit data changes efficiently.

One of the most significant growth factors for the mobile robot dataset versioning market is the exponential increase in the deployment of autonomous robots in industries such as logistics, manufacturing, and healthcare. As these robots become more sophisticated, the datasets required for their training and operation also become larger and more complex. Accurate dataset versioning ensures that every iteration of training and operational data is meticulously tracked, which is essential for regulatory compliance, quality assurance, and continuous performance improvement. Companies are increasingly recognizing the role of dataset versioning in minimizing errors, reducing operational downtime, and accelerating the development lifecycle of autonomous systems. The ability to roll back to previous dataset versions or audit changes has become a vital requirement, especially in safety-critical applications.

Another key driver is the rise of collaborative robotics and multi-robot systems, which generate vast amounts of heterogeneous data from diverse sources such as sensors, cameras, and LIDAR. Managing these datasets in real time, especially when updates and modifications are frequent, necessitates advanced versioning solutions that can handle distributed environments. The growing emphasis on data quality, integrity, and traceability is pushing organizations to invest in specialized software and services that provide granular control over dataset modifications. Furthermore, the integration of cloud-based platforms with dataset versioning capabilities allows for seamless collaboration among geographically dispersed teams, thus enhancing productivity and innovation in robot development and deployment.

The market is also benefiting from increased research activities in academia and industry, focusing on improving the accuracy and efficiency of autonomous navigation, mapping, and object recognition. These research initiatives generate vast volumes of experimental data that must be versioned and managed efficiently to support reproducibility and peer collaboration. The growing adoption of open-source frameworks and standardized dataset management practices is further catalyzing market growth. At the same time, regulatory requirements for data transparency and auditability in sectors like healthcare and defense are compelling organizations to adopt advanced dataset versioning solutions, ensuring that all data used in robot training and operation is properly documented and traceable.

From a regional perspective, North America and Europe currently dominate the mobile robot dataset versioning market, driven by robust investments in robotics research, a strong presence of technology vendors, and early adoption of advanced data management solutions. However, the Asia Pacific region is emerging as the fastest-growing market, propelled by rapid industrialization, increased automation in manufacturing and logistics, and significant government initiatives supporting AI and robotics innovation. The Middle East & Africa and Latin America are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the benefits of dataset versioning in optimizing robot performance and ensuring data compliance. The global landscape is thus characterized by a dynamic interplay of technological advancement, regulatory evolution, and industry-specific adoption patterns.

Component Analysis

The component segment of the mobile robot dataset versioning market is divided into software, hardware, and services, each playing a distinct role in the ecosystem. Software solutions form the backb

Depreciation Time Series for Hi target Navigation Tech Co. Hi-Target Navigation Tech Co.,Ltd manufactures professional high-precision surveying and mapping instruments worldwide. It offers GNSS RTK, optical, GIS handheld, hydrography and oceanography, CORS and precise positioning, software, laser and MMS, precision agriculture, and monitoring products under Hi-Target brand name. The company also provides GNSS infrastructure, autonomous driving, automatic monitoring, machine control, precision UAV kits, mobile mapping, and GNSS antenna solutions. It serves surveying and engineering, geographic information system, 3D scanning and aerial mapping, hydrography, global correction service, and monitoring industries. Hi-Target Navigation Tech Co.,Ltd was founded in 1999 and is headquartered in Guangzhou, China.

✨ Introduction The SemanticRail3D dataset is a 3D point cloud collection tailored for railway infrastructure semantic and instance segmentation. Originally, the dataset comprises 438 point clouds covering approximately 200 meters of track each—with a total of around 2.8 billion points annotated into 11 semantic classes. Collected using high-resolution LiDAR via a LYNX Mobile Mapper (≈980 points/m² with 5mm precision), this dataset serves as an excellent benchmark for state-of-the-art AI models .

🚀 Key Enhancements & Processing To further enrich its utility for machine learning applications, the dataset has undergone several advanced preprocessing steps and quality assurance measures:

🔍 Data Standardization via PCA Targeted Features: • Linear elements, including rails and all associated wires.

PCA Application: • Extracts the principal orientation of these elements by identifying the axis of maximum variance.

Reorientation: • Aligns the extracted principal axis with the x-axis, ensuring consistency and simplifying downstream analysis.

📸 Multi-Perspective Visualizations Each point cloud in the dataset is accompanied by four rendered images, generated from distinct camera viewpoints to enhance interpretability and usability. These views are designed to showcase the spatial structure of the railway environment from meaningful angles, aiding both visual inspection and AI model training.

The saved camera views are based on spherical coordinates and include:

🔹 Front View • A head-on perspective with a slight downward angle (azimuth = 50°, elevation = 35°) to give a balanced overview of the scene structure.

🔹 Side View • A lateral perspective (azimuth = 130°, elevation = 55°) that highlights the side profile of rail and overhead wire structures.

🔹 Diagonal View • An oblique angle (azimuth = -40°, elevation = 55°) providing depth perception and a richer understanding of the 3D layout.

🔹 Overhead View • A top-down (bird’s-eye) perspective (azimuth = -140°, elevation = 35°) showing the full track arrangement and spatial alignment.

🎨 Visual Color Coding

Color Code Mapping: The points in the images are colorized based on a standardized mapping to clearly differentiate between semantic classes:

🎨 Semantic Class Color Legend

✅ Quality Assurance through Human Evaluation

Detailed Review: • Each point cloud undergoes a rigorous expert review to ensure accurate and consistent labeling.

Rating System: • Files are rated on a scale from 1 (needs improvement) to 5 (excellent quality). • The ratings are compiled in a separate CSV file for ease of reference.

Label Error Codes: Within the CSV file, objects with labeling mistakes are flagged using the following codes: • R: Rails • W: Any kind of wires and cables • M: Masts • TS: Traffic signs • Noise: Miscellaneous errors or irrelevant data

🎯 Dataset Highlights Comprehensive Coverage: • 438 point clouds covering ~200 meters each • Approximately 2.8 billion points annotated into 11 semantic classes

High-Quality LiDAR Acquisition: • Dual LiDAR sensors on a Mobile Mapping System • Point density of ~980 points/m² and a precision of 5 mm

Consistent Data Alignment: • PCA is applied to linear elements (rails and wires) for reorientation along the x-axis

Enhanced Visualizations: • Four images per point cloud provide multiple viewpoints • Points are colorized based on the standardized color code for immediate visual clarity

Robust Quality Control: • Expert human evaluation rates each point cloud (1 to 5) • A separate CSV file holds the quality ratings along with detailed error codes for any mislabeling

🔗 Summary The enhanced SemanticRail3D dataset builds on a robust collection of 3D railway point clouds with advanced preprocessing techniques and comprehensive quality assurance. Through PCA-driven alignment, multi-perspective image generation, and an intuitive color coding system, the dataset standardizes data for efficient model training. Furthermore, the additional CSV file detailing human evaluation ratings and specific label error codes provides users with clear insights into the reliability and accuracy of the annotations. This complete solution sets a new benchmark for railway infrastructure analysis, empowering researchers and practitioners to develop more precise and reliable AI solutions.

This database presents the results of the MOVE Project Survey (Work Package 4) that has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 649263. The consortium of MOVE comprises nine partners in six countries: Luxembourg, Germany, Hungary, Norway, Romania, and Spain. The central aim of MOVE is to provide evidence-based knowledge on mobility of young people in Europe as a prerequisite to improve mobility conditions, and to identify fostering and hindering factors of “beneficial” mobility. This aim is pursued using a multilevel interdisciplinary research approach, aiming at a comprehensive and systematic analysis of the mobility of young people in Europe. Objectives of the Survey: –To find out about the role and value of information and support services for young people and their decision making process to go abroad. –To explore the role of transnational networks for support and as a potential “pull factor” for mobility. –To examine the agency of young people with mobility experience and without it. –To study the formation of social capital and the dimensions of social inequality of mobile young people and their effects on future perspectives as well as the reproduction of social inequalities. –To carry out research on the formation of identity by those mobile young people compared to non- mobile ones. –To examine the career-plans of young people and their personal attachments related to their commitments in their home country (e.g. sending money home, supporting the family, etc.) –To gain insights into the (re)production of social inequality concerning mobility and non- mobility. CASI (Computerunterstützte Selbstbefragung) - Interaktiver Selbstausfüller Interactive self-administered questionnaire: CASI (Computer Assisted Self- Interview) Young people between 18 and 29 years of age. Nationals from one of the participating countries or those who obtained the secondary school certificate/diploma in any of the six participating countries

We present the classification results of a supervised algorithm of road images containing amphibians. We used a prototype of a mobile mapping system composed of a scanning system attached to a traction vehicle capable of recording road surface images at speed up to 30 km/h. We tested the algorithm in three test situations (two control and one real): with plastic models of amphibians; with dead specimens of amphibians; and with real specimens of amphibians in a road survey. The classification results of the algorithm changed among tests, but in any case, it was able to detect more than 80% of the amphibians (more than 90% in control tests). Unfortunately, the algorithm presented as well a high rate of false-positive detections, varying from 80% in the real test to 14% in the control test with dead specimens. The Mobile Mapping Systems (MMS) is ideal for passive surveys and can work by day or night. This is the first study presenting an automatic solution to detect amphibians on roads. The classification algorithm can be adapted to any animal group. Robotics and computer vision are opening new horizons for wildlife conservation Palabras clave: Amphibian

Overview The dataset includes data collected during the ATMO-ACCESS Trans-National Access project "Industrial Pollution Sensing with synergic techniques (IPOS TNA)" that has been conducted from June 8 to June 24, 2024 at the Cabauw Experimental Site for Atmospheric Research (CESAR, 51°58'03''N, 4°55'47"E, 3 m.a.s.l.) of the Royal Netherlands Meteorological Institute (KNMI). The IPOS TNA was supporting the 3rd Intercomparison Campaign of UV-VIS DOAS Instruments (CINDI-3).The observations were taken with use of three instruments:ESA Mobile Raman Lidar (EMORAL). Lidar emits pulses at fixed wavelengths (355, 532 and 1064 nm), simultaneously with the pulse repetition rate of 10 Hz and pulse duration of 5-7 ns. The backward scattered laser pulses are detected at 5 Mie narrow-band channels (355p,s 532p,s and 1064 nm) and 3 Raman narrow-band channels (for N2 at 387, 607 nm and H2O at 408nm) as well as broad-band fluorescence channel (470 nm). The temporal resolution was set at 1 min and and spatial resolution to 3.75 m. The overlap between the laser beam and the full field of view of the telescope was at ~250 m a.g.l. EMORAL lidar is a state-of-the-art lidar system developed through a collaborative effort involving the University of Warsaw (UW, Poland; leader and operator), Ludwig Maximilian University of Munich (LMU, Germany), National Observatory of Athens (NOA, Greece), Poznan University of Life Sciences (PULS, Poland), and companies Raymetrics (Greece; core manufacturer), Licel (Germany), and InnoLas Laser (Germany). This complex instrument, part of ESA’s Opto-Electronics section (TEC-MME) at the European Space Research and Technology Centre (ESA-ESTEC, The Netherlands), is designed to perform precise atmospheric measurements. EMORAL lidar was validated by the ACTRIS Centre for Aerosol Remote Sensing (CARS) at the Măgurele Center for Atmosphere and Radiation Studies (MARS) of National Institute of R&D for Optoelectronics (INOE, Romania).PM counter GrayWolf PC-3500, GRAYWOLF Graywolf Sensing Solutions (USA) https://graywolfsensing.com/wp-content/pdf/GrayWolfPC-3500Brochure-818.pdf (last access 25/2/2025)Model 540 Microtops II® Sunphotometer, Solar Light Company, LLC (USA) https://www.solarlight.com/product/microtops-ii-sunphotometer (last access 25/2/2025)The dataset contain following items:1) EMORAL lidar data files The data contain of two files LiLi_IPOS.zip and LiLi_IPOS_quicklooks.zip. Both are described in detail below.The LiLi_IPOS.zip file is a folder that contains the high-resolution data obtained using the Lidar, Radar, Microwave radiometer algorithm (LiRaMi; more in Wang et al., 2020). The results were obtained only from the lidar data (referred to as Limited LiRaMi, i.e. LiLi algorithm version). The folder contains files in netcdf4 format for each day of observations. The data products are calculated from the analog channels only.Each of the .nc file has a structure, which contains Variables:Location (string)Latitude (size: 1x1 [deg])Longitude (size: 1x1 [deg])Altitude (size: 1x1 [m a.g.l.])time vector (size: 1 x time, [UTC])range vector (size: range x 1, [m])RCS532p matrix (size: range x time, [V m2]), which contains the data of the range-corrected signal at 532nm, parallel polarizationRCS532s matrix (size: range x time, [V m2]), which contains the data of the range-corrected signal at 532nm, perpendicular polarizationRCS1064 matrix (size: range x time, [V m2]), which contains the data of the range-corrected signal at 1064nmSR532 matrix (size: range x time, [unitless]), which contains the data of the scattering ratio at 532nmATT_BETA532 matrix (size: range x time, [m2/sr]), which contains the data of the attenuated backscatter coefficient at 532nm, parallel polarizationC532 constant (size: 1x1, [V sr]), which is the instrumental factor for 532nmSR1064 matrix (size: range x time, [au]), which contains the data of the scattering ratio at 1064nmATT_BETA1064 matrix (size: range x time, [m2/sr]), which contains the data of the attenuated backscatter coefficient at 1064nmC1064 constant (size: 1x1, [V sr]), which is the instrumental factor for 1064nmCOLOR_RATIO matrix (size: range x time, [au]), which contains the data of color ratio of 532nm and 1064nm.PARTICLE_DEPOLARIZATIO_RATIO matrix (size: range x time, [au]), which contains the data of particle depolarization ratio at 532nmC constant (size: 1x1, [au]), which is the depolarization constant for 532nm.The LiLi_IPOS_quicklooks.zip file contains high-resolution figures representing the data in the form of quicklooks of following parameters:Range-corrected signal at 1064nmScattering ratio at 532nmColor ratio of 532 and 1064nmParticle depolarization ratio at 532nmAerosol target classification from LiLi algorithmWang, D., Stachlewska, I. S., Delanoë, J., Ene, D., Song, X., and Schüttemeyer D., (2020). Spatio-temporal discrimination of molecular, aerosol and cloud scattering and polarization using a combination of a Raman lidar, Doppler cloud radar and microwave radiometer, Opt. Express 28, 20117-20134 (2020).2) PM counterThe PM_counter.zip file contains a folder with data from measurements of atmospheric particulate matter collected using the GrayWolf PC-3500 particle counter from June 15 (16:16:21 CEST) to June 20 (07:06:21 CEST), 2024, at the CESAR station (51°58'04.0"N, 4°55'46.4"E). The data were processed using WolfSense PC software for validation and analysis. The final dataset, provided in XLSX format, includes temporal evaluation in particle concentration from 0.3 to 10.0 µm (6 size ranges). The data is divided into three levels:[1] Level 0: Raw data in XLSX format with measurement data in 4 units (µg/m3, cnts/m3, cnts dif, cnts cum).File structure:Line 1: headers describing columns,Line 2-6646: concentration of PM,Column 1: date and time in format DD-MMM-YY HH:MM:SS AM/PM,Column 2-7: concentration of specific PM values: 0.3, 0.5, 1.0, 2.5, 5.0, 10.0 µm, respectively,Column 8: Temperature,Column 9: Carbon Dioxide (CO2),Column 10: Total Volatile Organic Compounds (TVOC),Column 11: pressure in measuring chamber,Missing data (Column 8-10) represented as zero value (0).[2] Level 1: Tables with validated data in 4 units (µg/m3, cnts/m3, cnts dif, cnts cum) in XLSX format.File structure:Line 1: headers describing columns,Line 2-6646: concentration of PM,Column 1: date and time in format DD-MMM-YY HH:MM:SS AM/PM,Column 2-7: concentration of specific PM values: 0.3, 0.5, 1.0, 2.5, 5.0, 10.0 µm, respectively,Column 8: pressure in measuring chamber,Column 9: assembly method, where: [1] measurement at a height of 60 cm during rain (instrument protected by the table), [2] measurement at a height of 160 cm when there is no rain.[3] Level 2: Tables with post-processed data in XLSX format, and graphs in PNG format visualizing the received data.XLSX file structure:PM counter - level 2 (daily average concentrations), PM counter - level 2 (hourly average concentrations) sheets: structure of columns same as in level 1.PM counter - level 2 (data comparison) sheet: Column 1 - Date in format DD.MM.YYYY; Column 2 - PM2.5 concentration measured within IPOS; Column 3 - PM10.0 concentration measured within IPOS; Column 4 - PM2.5 concentration measured at Cabauw-Wielsekade (RIVM), Column 5 - PM10.0 concentration measured Cabauw-Wielsekade (RIVM).General information for all level files:Decimal separator: coma (,).3) SunphotometerThe MICROTOPS_IPOS.zip file is a folder that contains data from measurements of aerosol optical thickness at wavelengths 380, 500, 675, 870, and 1020 nm done with Microtops II hand-held sunphotometer. The final, quality assured dataset, provided in XLSX format, consists of measurement data for: temperature, pressure, solar zenith angle, signal strength at different wavelengths (340, 380, 500, 936, 1020 nm), standard deviation at specific wavelengths, ratio between signals at two different wavelengths (340/380, 380/500, 500/936, 936/1020), and atmospheric optical thickness at different wavelengths.During the IPOS TNA campaign, in total 29 measurements were taken. Each measurement is composed of 6 scans, whereas the first one is a dark scan. The days when a measurement took place were: 13, 23, 24, and 25 of June 2024. Level 0 of data means raw data converted from dbf to xslx format file. Level 1 of data mean raw data converted from dbf to xslx file format, without the dark scans.Files structure:Line 1: Headers describing columns,Column 1: Serial number of the instrumentColumn 2-3: Date and Time in format YYYY-MM-DD; HH:MM:SS,Column 4-8: Data desciprtion of the camapign; Location (decimal); Latitude; Longitude (decimal), AltitudeColumn 9-14: Atmospheric Pressure; Solar Zenith Angle; Air Mass; Standard Deviation Correction; Temperature; ID of the measurement, Column 15-24: Signal strength at specific wavelength and Standard Deviation,Column 25-28: Ratio between signals at two different wavelengths,Column 29-33: Atmospheric Optical Thickness,Column 34-39: Columnar Water Vapour and Natural Logarithm of Voltage,Column 40-47: Calibration coefficients,Column 48-49: Pressure offset and Pressure scale factor,READ ME sheet: Describing the file content and measurement location.4) readme fileATTENTION:We offer a free access to this dataset. The user is however encouraged to share the information on the data use by sending an e-mail to rslab@fuw.edu.plIn the case this dataset is used for a scientific communication (publication, conference contribution, thesis) we would like to kindly ask for considering to acknowledge data provision by citing this dataset.------------------------------------PI of IPOS TNA Iwona Stachlewska and IPOS team members Maciej Karasewicz, Anna Abramowicz, Kinga Wiśniewska, Zuzanna Rykowska, and Afwan Hafiz acknowledge that the published dataset was prepared within the Trans-National Access grant (IPOS TNA no. ATMO-TNA-7-0000000056) within the ATMO-ACCESS grant financed by European Commission Horizon 2020 program (G.A.

We seek to mitigate the challenges with web-scraped and off-the-shelf POI data, and provide tailored, complete, and manually verified datasets with Geolancer. Our goal is to help represent the physical world accurately for applications and services dependent on precise POI data, and offer a reliable basis for geospatial analysis and intelligence.

Our POI database is powered by our proprietary POI collection and verification platform, Geolancer, which provides manually verified, authentic, accurate, and up-to-date POI datasets.

Enrich your geospatial applications with a contextual layer of comprehensive and actionable information on landmarks, key features, business areas, and many more granular, on-demand attributes. We offer on-demand data collection and verification services that fit unique use cases and business requirements. Using our advanced data acquisition techniques, we build and offer tailormade POI datasets. Combined with our expertise in location data solutions, we can be a holistic data partner for our customers.

KEY FEATURES - Our proprietary, industry-leading manual verification platform Geolancer delivers up-to-date, authentic data points

• POI-as-a-Service with on-demand verification and collection in 170+ countries leveraging our network of 1M+ contributors

• Customise your feed by specific refresh rate, location, country, category, and brand based on your specific needs

• Data Noise Filtering Algorithms normalise and de-dupe POI data that is ready for analysis with minimal preparation

DATA QUALITY

Quadrant’s POI data are manually collected and verified by Geolancers. Our network of freelancers, maps cities and neighborhoods adding and updating POIs on our proprietary app Geolancer on their smartphone. Compared to other methods, this process guarantees accuracy and promises a healthy stream of POI data. This method of data collection also steers clear of infringement on users’ privacy and sale of their location data. These purpose-built apps do not store, collect, or share any data other than the physical location (without tying context back to an actual human being and their mobile device).

USE CASES

The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

• Navigation and mapping for digital marketplaces and apps.
• Logistics for online shopping, food delivery, last-mile delivery, and more.
• Improving operational efficiency for rideshare and transportation platforms.
• Demographic and human mobility studies for market consumption and competitive analysis.
• Market assessment, site selection, and business expansion.
• Disaster management and urban mapping for public welfare.
• Advertising and marketing deployment and ROI assessment.
• Real-estate mapping for online sales and renting platforms.About Geolancer

ABOUT GEOLANCER

Quadrant's POI-as-a-Service is powered by Geolancer, our industry-leading manual verification project. Geolancers, equipped with a smartphone running our proprietary app, manually add and verify POI data points, ensuring accuracy and authenticity. Geolancer helps data buyers acquire data with the update frequency suited for their specific use case.

This digital elevation model (DEM) is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Coastal Services Center's Sea Level Rise and Coastal Flooding Impacts Viewer (www.csc.noaa.gov/slr/viewer). This metadata record describes the DEM for Mobile County in Alabama and Escambia, Santa Rosa, and Okaloosa (southern coastal portion only) Counties in Florida. The DEM includes the best available lidar data known to exist at the time of DEM creation for the coastal areas of Mobile County in Alabama and Escambia, Santa Rosa, and Okaloosa (portion) counties in Florida, that met project specification.This DEM is derived from the USGS National Elevation Dataset (NED), US Army Corps of Engineers (USACE) LiDAR data, as well as LiDAR collected for the Northwest Florida Water Management District (NWFWMD) and the Florida Department of Emergency Management (FDEM). NED and USACE data were used only in Mobile County, AL. NWFWMD or FDEM data were used in all other areas. Hydrographic breaklines used in the creation of the DEM were obtained from FDEM and Southwest Florida Water Management District (SWFWMD). This DEM is hydro flattened such that water elevations are less than or equal to 0 meters.This DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 5 meters. This DEM does not include licensed data (Baldwin County, Alabama) that is unavailable for distribution to the general public. As such, the extent of this DEM is different than that of the DEM used by the NOAA Coastal Services Center in creating the inundation data seen in the Sea Level Rise and Coastal Impacts Viewer (www.csc.noaa.gov/slr/viewer).The NOAA Coastal Services Center has developed high-resolution digital elevation models (DEMs) for use in the Center's Sea Level Rise And Coastal Flooding Impacts internet mapping application. These DEMs serve as source datasets used to derive data to visualize the impacts of inundation resulting from sea level rise along the coastal United States and its territories.The dataset is provided "as is," without warranty to its performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of this dataset is assumed by the user. This dataset should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

The French National Mapping Agency (Institut National de l'Information Géographique et Forestière - IGN) is responsible for producing and maintaining the spatial data sets for all of France. At the same time, they must satisfy the needs of different stakeholders who are responsible for decisions at multiple levels from local to national. IGN produces many different maps including detailed road networks and land cover/land use maps over time. The information contained in these maps is crucial for many of the decisions made about urban planning, resource management and landscape restoration as well as other environmental issues in France. Recently, IGN has started the process of creating a high-resolution land use land cover (LULC) maps, aimed at developing smart and accurate monitoring services of LULC over time. To help update and validate the French LULC database, citizens and interested stakeholders can contribute using the Paysages mobile and web applications. This approach presents an opportunity to evaluate the integration of citizens in the IGN process of updating and validating LULC data.

Dataset 1: Change detection validation 2019

This dataset contains web-based validations of changes detected by time series (2016 – 2019) analysis of Sentinel-2 satellite imagery. Validation was conducted using two high resolution orthophotos from respectively 2016 and 2019 as reference data. Two tools have been used: Paysages web application and LACO-Wiki. Both tools used the same validation design: blind validation and the same options. For each detected change, contributors are asked to validate if there is a change and if it is the case then to choose a LU or LC class from a pre-defined list of classes.

The dataset has the following characteristics:

Time period of the change detection: 2016-2019.

Time period of data collection: February 2019-December 2019

Total number of contributors: 105

Number of validated changes: 1048; each change was validated by between 1 to 6 contributors.

Region of interest: Toulouse and surrounding areas

Associated files: 1- Change validation locations.png, 1-Change validation 2019 – Attributes.csv, 1-Change validation 2019.csv, 1-Change validation 2019.geoJSON

This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the LandSense Citizen Observatory, IGN-France, and GeoVille.

Dataset 2: Land use classification 2019

The aim of this data collection campaign was to improve the LU classification of authoritative LULC data (OCS-GE 2016 ©IGN) for built-up area. Using the Paysages web platform, contributors are asked to choose a land use value among a list of pre-defined values for each location.

The dataset has the following characteristics:

Time period of data collection: August 2019

Types of contributors: Surveyors from the production department of IGN

Total number of contributors: 5

Total number of observations: 2711

Data specifications of the OCS-GE ©IGN

Region of interest: Toulouse and surrounding areas

Associated files: 2- LU classification points.png, 2-LU classification 2019 – Attributes.csv, 2-LU classification 2019.csv, 2-LU classification 2019.geoJSON

This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the LandSense Citizen Observatory, IGN-France and the International Institute for Applied Systems Analysis.

Dataset 3: In-situ validation 2018

The aim of this data collection campaign was to collect in-situ (ground-based) information, using the Paysages mobile application, to update authoritative LULC data. Contributors visit pre-determined locations, take photographs, of the point location and in the four cardinal directions away from the point and answer a few questions with respect with the task. Two tasks were defined:

Classify the point by choosing a LU class between three classes: industrial (US2), commercial (US3) or residential (US5).

Validate changes detected by the LandSense Change Detection Service: for each new detected change, the contributor was requested to validate the change and choose a LU and LC class from a pre-defined list of classes.

The dataset has the following characteristics

Time period of data collection: June 2018 – October 2018

Types of contributors: students from the School of Agricultural and Life Sciences and citizens

Total number of contributors: 26

Total number of observations: 281

Total number of photos: 421

Region of interest: Toulouse and surrounding areas

Associated files: 3- Insitu locations.png, 3- Insitu validation 2018 – Attributes.csv, 3- Insitu validation 2018.csv, 3- Insitu validation 2018.geoJSON

This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the LandSense Citizen Observatory, IGN-France.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.

Change-To-Liabilities Time Series for Shanghai Huace Navigation Technology Ltd. Shanghai Huace Navigation Technology Ltd. engages in the research and development, manufacturing, and integration high-precision satellite navigation and positioning technologies in China and internationally. The company offers global navigation satellite system (GNSS) smart antennas and antennas, controllers and tablets, surveying and mapping software, GNSS sensors, total stations, and data links; handheld laser scanners, airborne LiDAR and mobile mapping systems, and UAV platforms and cameras; USV platforms and hydrographic sensors; SAR systems; and GNSS corrections for use in survey and engineering, 3D mobile mapping, marine surveying, monitoring and infrastructure, and positioning services. It also provides machine control systems for excavators, graders, and dozers; GNSS+INS and IMU sensors; and auto steering, manual guidance, land leveling, and GNSS systems. The company serves the geospatial, machine control, navigation, and agriculture industries. It also engages in property management, investing, and research and development activities. Shanghai Huace Navigation Technology Ltd. was founded in 2003 and is headquartered in Shanghai, China.

Ebitda Time Series for Shanghai Huace Navigation Technology Ltd. Shanghai Huace Navigation Technology Ltd. engages in the research and development, manufacturing, and integration high-precision satellite navigation and positioning technologies in China and internationally. The company offers global navigation satellite system (GNSS) smart antennas and antennas, controllers and tablets, surveying and mapping software, GNSS sensors, total stations, and data links; handheld laser scanners, airborne LiDAR and mobile mapping systems, and UAV platforms and cameras; USV platforms and hydrographic sensors; SAR systems; and GNSS corrections for use in survey and engineering, 3D mobile mapping, marine surveying, monitoring and infrastructure, and positioning services. It also provides machine control systems for excavators, graders, and dozers; GNSS+INS and IMU sensors; and auto steering, manual guidance, land leveling, and GNSS systems. The company serves the geospatial, machine control, navigation, and agriculture industries. It also engages in property management, investing, and research and development activities. Shanghai Huace Navigation Technology Ltd. was founded in 2003 and is headquartered in Shanghai, China.

Dividends-Paid Time Series for Hi target Navigation Tech Co. Hi-Target Navigation Tech Co.,Ltd manufactures professional high-precision surveying and mapping instruments worldwide. It offers GNSS RTK, optical, GIS handheld, hydrography and oceanography, CORS and precise positioning, software, laser and MMS, precision agriculture, and monitoring products under Hi-Target brand name. The company also provides GNSS infrastructure, autonomous driving, automatic monitoring, machine control, precision UAV kits, mobile mapping, and GNSS antenna solutions. It serves surveying and engineering, geographic information system, 3D scanning and aerial mapping, hydrography, global correction service, and monitoring industries. Hi-Target Navigation Tech Co.,Ltd was founded in 1999 and is headquartered in Guangzhou, China.

This data set contains three-dimensional mobile lidar elevation data for seven villages in American Samoa on the island of Tutuila. The seven villages are: Fagaalu, Fagotogo, Pago Pago, Vatia, Leone, Amanave, and Poloa. The data were collected by Sanborn Map Company on October 27 - 30, 2010

Partners in this effort were the NOAA Pacific Services Center, the American Samoa Department of Commerce...

This CityGML dataset contains road space models (over 50 building models) in the area of Ingolstadt. 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). In order to enable the modification of the datasets, SketchUp project files are also provided along with creation guidelines.

This data aims to analyze the intellectual structure of mobile money studies by examining bibliographic characteristics. A dataset of 165 documents from the Scopus database was used. This study explored various aspects, including annual publication counts, country coupling, source numbers, primary research areas, co-occurrence of keywords, bibliographic linkages between sources and documents, and co-citation patterns of references. Bibliographic network mapping techniques were applied to analyze the data. The analysis was performed using VOSviewer, a scientific mapping tool. The results showed four main themes of the mobile money dataset: mobile money in Africa, financial inclusion, electronic money, and digital financial services.