Components: Hardware: Includes mobile mapping systems, sensors, and other equipment Software: Includes software for data collection, processing, and visualization Services: Includes data collection, processing, and analysis servicesSolutions: Location-based: Provides location-based information and services Indoor mapping: Creates maps of indoor spaces Asset management: Helps manage assets and track their location 3D mapping: Creates 3D models of buildings and infrastructureApplications: Land surveys: Used for surveying land and creating maps Aerial surveys: Used for surveying areas from the air Real estate & construction: Used for planning and designing buildings and infrastructure IT & telecom: Used for network planning and management Recent developments include: One of the pioneers in wearable mobile mapping technology, NavVis, revealed the NavVis VLX 3, their newest generation of wearable technology. As the name suggests, this is the third version of their wearable VLX system; the NavVis VLX 2 was released in July of 2021, which is over two years ago. In their news release, NavVis emphasises the NavVis VLX 3's improved accuracy in point clouds by highlighting the two brand-new, 32-layer lidars that have been "meticulously designed and crafted" to minimise noise and drift in point clouds while delivering "high detail at range.", According to the North American Mach9 Software Platform, mobile Lidar will produce 2D and 3D maps 30 times faster than current systems by 2023., Even though this is Mach9's first product launch, the business has already begun laying the groundwork for future expansion by updating its website, adding important engineering and sales professionals, relocating to new headquarters in Pittsburgh's Bloomfield area, and forging ties in Silicon Valley., In order to make search more accessible to more users in more useful ways, Google has unveiled a tonne of new search capabilities for 2022 spanning Google Search, Google Lens, Shopping, and Maps. These enhancements apply to Google Maps, Google Shopping, Google Leons, and Multisearch., A multi-year partnership to supply Velodyne Lidar, Inc.'s lidar sensors to GreenValley International for handheld, mobile, and unmanned aerial vehicle (UAV) 3D mapping solutions, especially in GPS-denied situations, was announced in 2022. GreenValley is already receiving sensors from Velodyne., The acquisition of UK-based GeoSLAM, a leading provider of mobile scanning solutions with exclusive high-productivity simultaneous localization and mapping (SLAM) programmes to create 3D models for use in Digital Twin applications, is expected to close in 2022 and be completed by FARO® Technologies, Inc., a global leader in 4D digital reality solutions., November 2022: Topcon donated to TU Dublin as part of their investment in the future of construction. Students learning experiences will be improved by instruction in the most cutting-edge digital building techniques at Ireland's first technical university., October 2022: Javad GNSS Inc has released numerous cutting-edge GNSS solutions for geospatial applications. The TRIUMPH-1M Plus and T3-NR smart antennas, which employ upgraded Wi-Fi, Bluetooth, UHF, and power management modules and integrate the most recent satellite tracking technology into the geospatial portfolio, are two examples of important items.. Key drivers for this market are: Improvements in GPS, LiDAR, and camera technologies have significantly enhanced the accuracy and efficiency of mobile mapping systems. Potential restraints include: The initial investment required for mobile mapping equipment, including sensors and software, can be a barrier for small and medium-sized businesses.. Notable trends are: Mobile mapping systems are increasingly integrated with cloud platforms and AI technologies to process and analyze large datasets, enabling more intelligent mapping and predictive analytics.

This project aims to improve the position estimation of mobile mapping platforms. Mobile Mapping (MM) is a technique to obtain geo-information on a large scale using sensors mounted on a car or another vehicle. Under normal conditions, accurate positioning is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, especially in urban areas, where building structures impede a direct line-of-sight to navigation satellites or lead to multipath effects, MM derived products, such as laser point clouds or images, lack the expected reliability and contain an unknown positioning error. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary data, such as digital maps, ortho images or airborne LiDAR. These data serve as a reliable source of orientation and are being used subsidiarily or as the basis for adjustment. However, these approaches show limitations regarding the achieved accuracy, the correction of error in height, the availability of tertiary data and their feasibility in difficult areas. This project is addressing the aforementioned problem by employing high resolution aerial nadir and oblique imagery as reference data. By exploiting the MM platform?s approximate orientation parameters, very accurate matching techniques can be realised to extract the MM platform?s positioning error. In the form of constraints, they serve as a corrective for an orientation update, which is conducted by an estimation or adjustment technique. In total, it is 35 GB of data currently uploaded to SURFfilesender with dans-itc@utwente.nl as the recipient

These files are to support the published journal and thesis about the IMU and LIDAR SLAM for indoor mapping. They include datasets and functions used for point clouds generation. Date Submitted: 2022-02-21

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.

Cloud-Based Mapping Service Market Outlook

The global cloud-based mapping service market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 8.9 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 11.2% during the forecast period. This remarkable growth is primarily driven by the increasing demand for real-time data access and navigation services across various sectors. Businesses and governments worldwide are increasingly leveraging cloud-based mapping services to optimize operations, improve customer experience, and enhance decision-making processes. The seamless integration of advanced technologies such as Artificial Intelligence (AI) and Internet of Things (IoT) in mapping services is further boosting this market's expansion.

The integration of AI with cloud-based mapping services is one of the key growth factors for this market. AI technologies enhance the capabilities of cloud-based mapping services by providing intelligent insights and predictive analytics. For instance, AI can analyze traffic patterns and predict congestion, offering alternative routes and optimal travel paths. This is particularly beneficial for the transportation and logistics sectors, where time is of the essence. Furthermore, AI-driven mapping services can assist businesses in understanding consumer behavior and preferences, allowing for targeted marketing strategies and improved customer engagement. The ability of AI to process massive datasets quickly and accurately makes it a valuable tool in the cloud-based mapping service industry.

Another significant factor contributing to market growth is the rising adoption of IoT devices. IoT devices generate a vast amount of location-based data that can be effectively managed and utilized through cloud-based mapping services. These services enable businesses to track and monitor assets, vehicles, and personnel in real-time, leading to improved operational efficiency and reduced costs. For example, in the logistics sector, companies can use cloud-based mapping services to optimize delivery routes and monitor vehicle conditions, thereby minimizing fuel consumption and enhancing customer satisfaction. The continuous evolution and proliferation of IoT devices are expected to drive further demand for cloud-based mapping services in the coming years.

The increasing reliance on mobile devices and the proliferation of high-speed internet connectivity are also significant growth drivers for the cloud-based mapping service market. With the widespread use of smartphones and tablets, consumers and businesses alike are accessing mapping services on-the-go, necessitating reliable cloud-based solutions. The availability of high-speed internet ensures seamless connectivity and real-time updates, enhancing user experience. This trend is particularly prominent in urban areas, where demand for navigation and location-based services is high. As mobile technology continues to evolve and internet infrastructure improves worldwide, the cloud-based mapping service market is poised for substantial growth.

The rise of URL Shortening Services has become increasingly relevant in the context of cloud-based mapping services. These services allow users to condense lengthy URLs into shorter, more manageable links, which is particularly useful for sharing location-based information. In industries such as logistics and transportation, where quick access to precise location data is crucial, URL shortening can streamline communication and improve efficiency. By integrating URL shortening with mapping services, businesses can enhance their digital marketing strategies and facilitate easier sharing of maps and navigation routes. This integration not only improves user experience but also supports the growing demand for seamless digital interactions in the mapping service market.

Service Type Analysis

The cloud-based mapping service market is segmented into several service types, each offering unique features and benefits to users. Mapping and navigation services are perhaps the most widely recognized and utilized among these. They provide users with detailed maps, directions, and navigation assistance, which are crucial for both consumers and businesses. These services cater to a wide array of applications, from personal navigation to complex logistics operations. As the demand for precise, real-time navigation grows, mapping and navigation services continue to be at the forefront of the cloud-based mapping industry. Their integrat

This dashboard is best viewed using a mobile device. For an enhanced viewing experience on a desktop or laptop computer please use the NV Wildfire Info desktop version dashboardAll data displayed on this map is near real-time. There are two ways in which this happens: Web service based data and a mobile mapping application called Field Maps. Web services are updated regularly ranging from every minute to once a month. All web services in this map are refreshed automatically to ensure the latest data being provided is displayed. Data collected through the use of Field Maps is done so by firefighters on the ground. The Field Maps application is consuming, creating, and editing data that are stored in ArcGIS Online. These data are then fed directly in to this map. To learn more about these web mapping technologies, visit the links below:Web ServicesArcGIS Field MapsArcGIS OnlineWeb Services used in this map:(visit link to learn more about each service)IRWIN - A central hub that orchestrates data between various fire reporting applications. When a new incident is created and/or updated by a dispatch center or other fire reporting system, it is then displayed on the map using the Integrated Reporting of Wildland-Fire Information (IRWIN) service. All layers below are derived from the same IRWIN service and automatically refresh every five minutes:New Starts (last 24hrs) - Any incident that has occurred within the last rolling 24 hour time period.Current Large Incidents - Incidents that have created an ICS 209 document at the type 3 Incident Commander (IC) level and above and are less than 100% contained.Ongoing - Incidents that do not have a containment, control, or out date.Contained - Incidents with a containment date but no control or out date.Controlled/Out (last 24hrs) - Incidents with a containment, control, and/or out date within the last rolling 24 hour time period.Controlled/Out - Incidents with a containment, control, and/or out date. Layer turned off by default.Season Summary - All incidents year to date. Layer turned off by default.ArcGIS Online/Field Maps - Part of the Esri Geospatial Cloud, ArcGIS Online and Collector enables firefighters to use web maps created in ArcGIS Online on mobile devices using the Collector application to capture and edit data on the fireline. Data may be captured and edited in both connected and disconnected environments. When data is submitted back to the web service in ArcGIS Online, it is then checked for accuracy and approved for public viewing.Fire Perimeter - Must be set to 'Approved' and 'Public' to be displayed on the map. Automatically refreshes every five minutes.NOAA nowCOAST - Provides web services of near real-time observations, analyses, tide predictions, model guidance, watches/warnings, and forecasts for the coastal United States by integrating data and information across NOAA, other federal agencies and regional ocean and weather observing systems (source). All layers below automatically refresh every five minutes.Tornado Warning - National Weather Service warning for short duration hazard.Severe Thunderstorm Warning - National Weather Service warning for short duration hazard.Flash Flood Warning - National Weather Service warning for short duration hazard.Red Flag Warning - National Weather Service warning for long duration hazard.nowCOAST Lightning Strike Density - 15-minute Satellite Emulated Lightning Strike Density imagery for the last several hours.nowCOAST Radar - Weather Radar (NEXRAD) Reflectivity Mosaics from NOAA MRMS for Alaska, CONUS, Puerto Rico, Guam, and Hawaii for last several hours.

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">

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.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

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. Combined online panel and snowball survey. The Online Panel Survey Design and Field Research. Universe: Mobile and non-mobile young people between 18 and 29 years of age, nationals of at least one of the consortium partner, or those who obtained the secondary school certificate/diploma in any of the six participating countries. Sample error: n=1,000 interviews, +/- 3.2%; n=750 interviews +/- 3.7% confidence inter- val 95%. Quality standards: ISOMAR, ISO, AENOR, IQNet. Sample size: 5,769 questionnaires. Languages: The online survey was available in, French, German, Hungarian, Norwegian (Nynorsk and Bokm˚al), Luxembourgish, German for Luxembourg, Romanian and Spanish. Fieldwork dates: 23rd of November 2016 to 30th of January 2017, accounting for 8 weeks. Pre-test: The questionnaire was submitted to a pre-test, and amendments were introduced to improve the final results. The Online Snowball Survey The online survey panel was complemented with a snowball sampling, self-selected, online survey targeting only young people involved, in the past or currently, in a mobility process (n=3,207). Furthermore, as presented in D.4.4, snowball sampling (Goodman 1961), is the most efficient way to obtain respondents through referrals amongst people sharing the same features, which includes hidden populations amongst migrants. Design and Field Research The questionnaire design process followed the same work flow as the online panel survey questionnaire, using the same set of questions, except those related to the non-mobile questions which were deleted. The survey design and field research were unfolded as follows: Universe: people living abroad or people with mobility experience 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. Methodology: non-probabilistic snowball Sample size: n=3,207. Languages: French, German, Hungarian, Norwegian (Nynorsk and Bokm˚al), Luxembourgish, German for Luxembourg, Romanian and Spanish. Duration: 15 to 25 minutes. Fieldwork dates: 7th of December 2016, reaching peak activity from 19th of December 2016 to 31st of January 2017, and finished on 5th of February 2017. Sample per country: A questionnaire was assigned to a consortium country whenever the respondent was a national, had obtained his/her secondary school certificate or had carried out the last year of studies before dropping out in the said country.

Start.io's mobile IP database is one of the largest and most comprehensive out there. Used by some of the largest location and device-graph companies in the world, this data is linked with MAIDs and timestamps, offering insights into billions of devices and events.

Use cases : - Device graph enrichment - Fraud detection - Geolocation services - Customer journey mapping - Ad-targeting

This open data site is for exploring, accessing and downloading Kentucky-specific GIS data and discovering mapping apps. It provides simple access to information and tools that allow users to understand geospatial data. You can analyze and combine datasets using maps, as well as develop new web and mobile applications. Explore data by category, interact with web mapping applications, use Story Maps, or access our services directly. All data on the site is fed from a variety of authoritative sources.DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this site, please visit https://hub.arcgis.com/admin/

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, and the National Park of American Samoa. The classifications available for download from the Digital Coast are: 1. Ground (2) 2. Low Vegetation (3) 3. Medium Vegetation (4) 4. High Vegetation (5) 5. Building (6) 6. Model Key Point (8) A smooth surface can be generated from model key points.

NZ Parcel Boundaries Wireframe provides a map of land, road and other parcel boundaries, and is especially useful for displaying property boundaries. This map service is for visualisation purposes only and is not intended for download. You can download the full parcels data from the NZ Parcels dataset. This map service provides a dark outline and transparent fill, making it perfect for overlaying on our basemaps or any map service you choose. Data for this map service is sourced from the NZ Parcels dataset which is updated weekly with authoritative data direct from LINZ’s Survey and Title system. Refer to the NZ Parcel layer for detailed metadata. To simplify the visualisation of this data, the map service filters the data from the NZ Parcels layer to display parcels with a status of 'current' only. This map service has been designed to be integrated into GIS, web and mobile applications via LINZ’s WMTS and XYZ tile services. View the Services tab to access these services. See the LINZ website for service specifications and help using WMTS and XYZ tile services and more information about this service.

The CDTFA Mobile app will enable you to find a sales and use tax rate, locate and contact our field offices, and conveniently access our website and online services.

FEATURES

• California Sales and Use Tax Rates: Find a tax rate by address, city, or your current location • CDTFA Field Offices: Get an office's address and other details, and with the tap of a button call an office or open its location in the Maps application to get driving directions • Website: View our website right within the app • Online Services: Access our online services directly with the tap of a button • And more features will be coming soon!

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

Aurora:GeoStudio® is a premier geospatial analysis platform that excels in supporting foot traffic data through its sophisticated Population Dynamics® analytic. Foot traffic data encompasses information about the number of people visiting specific locations or establishments, providing deep insights into customer behavior, patterns, and trends. This data is crucial for businesses looking to understand their audience and make data-driven decisions.

Core Features:

  1.    Data Collection Methods:
  •    Passive Sensors: Aurora:GeoStudio® integrates data collected from passive sensors deployed at various locations. These devices count the number of visitors, track their movement paths, and record the duration of their visits.
  •    Mobile Devices: The platform also leverages data from mobile devices, providing additional insights into foot traffic patterns through location-based services and applications.
  2.    Population Dynamics® Analytic:
  •    Aurora:GeoStudio®’s Population Dynamics® analytic processes foot traffic data to deliver comprehensive insights. This analytic tool helps visualize and understand visitor behavior, peak visiting times, and movement trends within specific areas.
  3.    Visualization and Mapping:
  •    The platform offers advanced visualization capabilities, displaying foot traffic data on customizable maps from providers like Google, Esri, Open, and Stamen. These visualizations help users understand spatial patterns and relationships, facilitating informed decision-making.

Applications:

  1.    Customer Behavior Analysis:
  •    Businesses can analyze foot traffic data to understand customer behavior, such as the number of visitors, the duration of their visits, and the paths they take within an establishment. This information is crucial for tailoring services and improving customer satisfaction.
  2.    Store Layout Optimization:
  •    Foot traffic data helps businesses optimize store layouts by identifying high-traffic areas and bottlenecks. By understanding how customers move through a space, businesses can rearrange products and displays to enhance flow and maximize sales opportunities.
  3.    Marketing Strategy Enhancement:
  •    Aurora:GeoStudio® enables businesses to refine their marketing strategies by providing insights into peak visiting times and customer demographics. This data supports targeted marketing campaigns, ensuring promotions reach the right audience at the right time.
  4.    Operational Efficiency:
  •    Understanding foot traffic patterns allows businesses to optimize staffing levels, manage inventory more effectively, and improve overall operational efficiency. By aligning resources with actual customer demand, businesses can enhance service delivery and reduce costs.
  5.    Urban Planning and Public Spaces:
  •    Foot traffic data is invaluable for urban planners and managers of public spaces. It helps in designing public areas that accommodate pedestrian flow efficiently and ensures that amenities are accessible and well-placed.

Aurora:GeoStudio®’s support for foot traffic data through the Population Dynamics® analytic offers businesses and urban planners a powerful tool for understanding and optimizing visitor behavior. By leveraging data from sensors, cameras, and mobile devices, the platform provides detailed insights into customer movements and trends. These insights enable businesses to enhance their marketing strategies, optimize store layouts, and improve operational efficiency. For urban planners, foot traffic data facilitates the design of more effective and accessible public spaces. Aurora:GeoStudio®’s advanced features empower users to make informed decisions and achieve a comprehensive understanding of foot traffic dynamics, leading to better strategic outcomes.

Helicopter position (latitude, longitude, altitude) and attitude (pitch, roll, true heading) were measured by an inertial measurement unit (IMU-57) as part of the combined global navigation satellite system (GNSS) and inertial navigation system (INS) Applanix AP60-Air (hdl:10013/sensor.a9fee346-91e7-4eed-9f2f-89f1368e53a0). The IMU received input signal from two AV39 GNSS antennae installed on the forward and aft cowlings on top of the main cabin of the helicopter. The IMU was mounted in the rear cargo compartment on a sensor plate together with the airborne laser scanner and the sensor plate was connected with dampeners to the helicopter airframe.The helicopter flights in this data set include surveys where the airborne laser scanner was operated along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. They are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle pattern, or transect flights. The position and attitude data were collected to aid the processing of data from the instruments onboard like the airborne laser scanner (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), the infrared camera (Thielke et al., 2022; doi:10.1594/PANGAEA.941017), and the RGB camera (Neckel et al., 2022; doi:10.1594/PANGAEA.949433). The GPS/INS data was post-processed using Applanix software POSPac Mobile Mapping Suite (MMS) 8.3 and resulted in the 200 Hz precise point positioning (PPP) solution. The post-processed positions correspond to the location of the IMU in the aircraft reference frame in the cargo compartment of the helicopter. For a set of high latitude flights, the post-processing failed due to the low signal-to-noise of the horizontal component of the Earth's rotation rate. In this case only the 10 Hz real time navigation (RTNav) solution is provided. The positioning and altitude error for the real time can be metres, while the post-processed 200 Hz solution has an accuracy of decimetres. The challenging nature of GNSS (limited satellite visibility, ionospheric interference) and inertial navigation means that the quality of the INS/GPS is degraded even after post-processing compared to lower latitude data.

This data consists of a composite inundation hazards layer for counties from TX to ME, which have a flood exposure snapshot. The dataset was developed from a union of FEMA flood hazard, USACE hurricane evacuation study, NOAA sea level rise of 3-ft above MHHW, and NOAA Shallow Coastal flooding georeferenced digital data. The source and date have been preserved for each source dataset in the attribution. A unique identifier for each hazard, a total number of hazards and hazard description list in the attribution provide coastal risk exposure for each polygon

This layer is sourced from maps.coast.noaa.gov.

This map service presents spatial information developed as part of the National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management’s Coastal Flood Exposure Mapper. The purpose of the online mapping tool is to provide coastal managers, planners, and stakeholders a preliminary look at exposures to coastal flooding hazards. The Mapper is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help communities initiate resilience planning efforts. Currently the extent of the Coastal Flood Exposure Mapper covers U.S. coastal areas along the Gulf of Mexico and Atlantic Ocean. NOAA provides the information “as-is” and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

© NOAA Office for Coastal Management