Work in progress: data might be changed The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies. Vehicle Detections Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock. The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides. Figure 1: Overview map of detected vehicles Parking Areas

QUT Research Data Respository Dataset and Resources

The datasets are original and specifically collected for research aimed at reducing registration errors between Camera-LiDAR datasets. Traditional methods often struggle with aligning 2D-3D data from sources that have different coordinate systems and resolutions. Our collection comprises six datasets from two distinct setups, designed to enhance versatility in our approach and improve matching accuracy across both high-feature and low-feature environments.Survey-Grade Terrestrial Dataset:Collection Details: Data was gathered across various scenes on the University of New Brunswick campus, including low-feature walls, high-feature laboratory rooms, and outdoor tree environments.Equipment: LiDAR data was captured using a Trimble TX5 3D Laser Scanner, while optical images were taken with a Canon EOS 5D Mark III DSLR camera.Mobile Mapping System Dataset:Collection Details: This dataset was collected using our custom-built Simultaneous Localization and Multi-Sensor Mapping Robot (SLAMM-BOT) in several indoor mobile scenes to validate our methods.Equipment: Data was acquired using a Velodyne VLP-16 LiDAR scanner and an Arducam IMX477 Mini camera, controlled via a Raspberry Pi board.

These data provide an accurate high-resolution shoreline compiled from imagery of PORT OF MOBILE, AL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

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

A digital elevation model (DEM) of a portion of the Mobile-Tensaw Delta region and Three Mile Creek in Alabama was produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over the area (bathymetry was irresolvable) using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 50 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of +/-15 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development. For more information on Lidar science and the Experimental Advanced Airborne Research Lidar (EAARL) system and surveys, see http://ngom.usgs.gov/dsp/overview/index.php and http://ngom.usgs.gov/dsp/tech/eaarl/index.php .

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.

Mobile Map Packages (MMPK’s) can be used in the ESRI Field Maps app (no login required), either by direct download in the Field Maps app or by sideloading from your PC. They can also be used in desktop applications that support MMPK’s such as ArcGIS Pro, and ArcGIS Navigator. MMPK’s will expire quarterly and have a warning for the user at that time but will still function afterwards. They are updated quarterly to ensure you have the most up to date data possible. These mobile map packages include the following national datasets along with others: Surface Management Agency, Public Land Survey System (PLSS), BLM Recreation Sites, National Conservation Lands, ESRI’s Navigation Basemap and Vector Tile Package. Last updated 20250321. Contact jlzimmer@blm.gov with any questions.

The dataset includes 6 3D point cloud files collected with Velodyne VLP-16 mobile LiDAR (*.las) belonging to 4 cultural and natural heritage structures located in Cappadocia, Türkiye. The structures are:

1- St. Theodore Church (Interior & Exterior): The Church of St. Theodore is located in Yeşilöz Village in Ürgüp district of Nevşehir. Formerly known as Tagar, now known as Yesiloz Village is approximately 16 km from the center of Urgup district and is a settlement area built on the slope of the valley. The church was carved into a large rock mass on the hill northwest of the village. As a result of excavations near the village, a monastery with a courtyard on three sides was discovered. It is thought that the church belonged to this monastery. The church is called both St. Theodore and Tagar Church. Although it is not known where the name Theodore comes from, it is estimated that this name may have been given because the church was built in the name of St. Theodore.

2- Mustafa Efendi Mosque (Interior & Exterior): The masonry Mustafa Efendi Mosque in Bahçeli Village of Ürgüp District of Nevşehir Province is the oldest of the 3 mosques built in the village. It is estimated that it was built about 50 years before the Osman Efendi Mosque, which was presented as a proposed building within the scope of the project, with a construction date of 1746. Although it is known to have a small inscription with the date of construction, this inscription was not found during the survey. According to this information, Mustafa Efendi Mosque is estimated to be a 17th-18th century work.

3- Fairy Chimney: The distance between Bahçeli Village where the fairy chimney is located and Urgup district is 15 kilometers and the formations between these two areas are generally natural formations without caps and in the late fairy chimney period. It shows that the fairy chimney is a natural formation without a cap and in the late fairy chimney period. The fairy chimney is in the 1st degree natural protected area.

4- Masonry House: Bahçeli Village, where the building examined within the scope of the project is located, is 15 km away from Ürgüp district and is a mixed settlement type. There are approximately 200 cove-carved and masonry historical buildings in the village. A large part of the village, including the structures examined in the village, is a 3rd degree natural protected area. The masonry-rock-carved civil architecture dwelling in Bahçeli Village, Ürgüp District, Nevşehir has not been in use since the 1980s and some of the spaces have been completely lost.

The creation of this dataset was funded by the Scientific and Technological Research Council of Türkiye (TUBITAK) 1001 program under Project no. 122Y017.

The data set includes data on the territorial distribution, species composition, nesting and behaviors of birds during the breeding season in permanent sample plots (areas for the bird census). All bird observations are marked on a map in WGS 84 coordinates determined using a mobile device with OS Android. Surveys were conducted from 2018 to 2020 in season of the birds nesting between end of April and the middle of July. The permanent sample plots are located in typical forest habitats of the strict nature reserve (category IUCN Ia) – Core area Prioiksko-Terrasnyi Biosphere Reserve. There are 3 sample plots in different type of forest: pine forest (sample plot 35, square 45 ha), oakwood (sample plot 41, square 25 ha), and mixed forest (sample plot 18, square 40 ha). During survey, 66 species of birds were recorded in total. Each census is a map of the distribution of birds on the site on the day of visit. The collected data make it possible to create a map of the distribution of the individual territories of birds on permanent areas in different years.

Набор данных включает данные о территориальном распределении, видовом составе, гнездовании и поведении птиц в период размножения на постоянных пробных площадках (площадки для учета птиц). Все наблюдения за птицами отмечены на карте в координатах WGS 84, определенных при помощи мобильного устройства c OC Андроид. Исследования проводились с 2018 по 2020 год в сезон гнездования птиц с конца апреля по середину июля. Постоянные пробные участки расположены в типичных лесных местообитаниях природного заповедника (категория IUCN Ia) – ядра Приокско-Террасного биосферного резервата. Обследование проведено на 3-х пробных площадях в различных типах спелого леса (возраст 70-120 лет): сосновый лес (plot 35, площадь 35 га), дубовый лес (plot 41, площадь 25 га) и смешанный лес (plot 18, площадь 40 га). Всего в ходе обследований было зафиксировано 66 видов птиц. Каждый учет представляет собой карту распределения птиц на площадке в день учета. Собранные данные позволяют составить карту распределения мест гнездования птиц на постоянных площадях в разные годы.

The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 Mobile County, Alabama lidar survey. Beach width is included and is defined as the distance between the dune toe and shoreline along a cross-shore profile. The beach slope is calculated using this beach width and the elevation of the shoreline and dune toe.

These data provide an accurate high-resolution shoreline compiled from imagery of WESTERN MOBILE BAY, AL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribu...

Beaver Lake was constructed in 1966 on the White River in the northwest corner of Arkansas for flood control, hydroelectric power, public water supply, and recreation. The surface area of Beaver Lake is about 27,900 acres and approximately 449 miles of shoreline are at the conservation pool level (1,120 feet above the North American Vertical Datum of 1988). Sedimentation in reservoirs can result in reduced water storage capacity and a reduction in usable aquatic habitat. Therefore, accurate and up-to-date estimates of reservoir water capacity are important for managing pool levels, power generation, water supply, recreation, and downstream aquatic habitat. Many of the lakes operated by the U.S. Army Corps of Engineers are periodically surveyed to monitor bathymetric changes that affect water capacity. In October 2018, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed one such survey of Beaver Lake using a multibeam echosounder. The echosounder data was combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table. Collection of bathymetric data in October 2018 at Beaver Lake near Rogers, Arkansas, used a marine-based mobile mapping unit that operates with several components: a multibeam echosounder (MBES) unit, an inertial navigation system (INS), and a data acquisition computer. Bathymetric data were collected using the MBES unit in longitudinal transects to provide complete coverage of the lake. The MBES was tilted in some areas to improve data collection along the shoreline, in coves, and in areas that are shallower than 2.5 meters deep (the practical limit of reasonable and safe data collection with the MBES). Two bathymetric datasets collected during the October 2018 survey include the gridded bathymetric point data (BeaverLake2018_bathy.zip) computed on a 3.28-foot (1-meter) grid using the Combined Uncertainty and Bathymetry Estimator (CUBE) method, and the bathymetric quality-assurance dataset (BeaverLake2018_QA.zip). The gridded point data used to create the bathymetric surface (BeaverLake2018_bathy.zip) was quality-assured with data from 9 selected resurvey areas (BeaverLake2018_QA.zip) to test the accuracy of the gridded bathymetric point data. The data are provided as comma delimited text files that have been compressed into zip archives. The shoreline was created from bare-earth lidar resampled to a 3.28-foot (1-meter) grid spacing. A contour line representing the flood pool elevation of 1,135 feet was generated from the gridded data. The data are provided in the Environmental Systems Research Institute shapefile format and have the common root name of BeaverLake2018_1135-ft. All files in the shapefile group must be retrieved to be useable.

These data provide an accurate high-resolution shoreline compiled from imagery of Northern Mobile Bay, Tensaw River to Montrose, AL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal...

Mobile Map Packages (MMPK’s) can be used in the ESRI Field Maps app (no login required), either by direct download in the Field Maps app or by sideloading from your PC. They can also be used in desktop applications that support MMPK’s such as ArcGIS Pro, and ArcGIS Navigator. MMPK’s will expire quarterly and have a warning for the user at that time but will still function afterwards. They are updated quarterly to ensure you have the most up to date data possible. These mobile map packages include the following national datasets along with others: Surface Management Agency, Public Land Survey System (PLSS), BLM Recreation Sites, National Conservation Lands, ESRI’s Navigation Basemap and Vector Tile Package. Last updated 20250321. Contact jlzimmer@blm.gov with any questions.

Mobile Map Packages (MMPK’s) can be used in the ESRI Field Maps app (no login required), either by direct download in the Field Maps app or by sideloading from your PC. They can also be used in desktop applications that support MMPK’s such as ArcGIS Pro, and ArcGIS Navigator. MMPK’s will expire quarterly and have a warning for the user at that time but will still function afterwards. They are updated quarterly to ensure you have the most up to date data possible. These mobile map packages include the following national datasets along with others: Surface Management Agency, Public Land Survey System (PLSS), BLM Recreation Sites, National Conservation Lands, ESRI’s Navigation Basemap and Vector Tile Package. Last updated 20250321. Contact jlzimmer@blm.gov with any questions.

This dataset contains raw, processed, and auxiliary data collected as part of a four-year mobile methane measurement campaign in Montreal, Canada, covering 2019, 2022, 2023, and 2024. The goal of the project was to identify and quantify methane emission hotspots from landfills, natural gas infrastructure, and other urban sources, and to inform spatially resolved emissions assessments for urban climate mitigation.Measurements were collected using vehicle-mounted gas analyzers, and emissions were estimated using a Gaussian plume inversion model.

1. Introduction
The main idea of ​​the project is to obtain traffic sign locations by analyzing videos using a combination of artificial intelligence and image recognition methods. Each video file also includes a geolocation file that has the same name as the video file and contains latitude, longitude, and timestamp attributes from the beginning of the video. A total of 3350 videos with a total range of 1040 km are used in the area of ​​the Berlin S-Bahn ring. The result file contains longitude and latitude (WGS84, EPSG:4326) of traffic sign locations and their types in 43 categories.

2. Data sets
To train AI networks, two publicly available data sets are used: for traffic sign recognition “German Traffic Sign Detection Benchmark Dataset[1]” and for traffic sign classification “German Traffic Sign Recognition Benchmark Dataset[1 ]". You can find more information here: Detection Dataset, Classification Dataset

3. Methodology and models
The TensorFlow[2] framework is used to analyze videos. An object detection[3] model for traffic sign recognition is trained using the transfer learning method[4]. To improve the accuracy of traffic sign classification, a custom image classification[5] model for categorizing traffic sign types is trained. The output of the traffic sign recognition model is used as input of the traffic sign classification model.

4. Source
[1] Houben, S., Stallkamp, ​​J., Salmen, J., Schlipsing, M. and Igel, C. (2013). "Detection of traffic signs in real-world images: the German traffic sign detection benchmark", in Proceedings of the International Joint Conference on Neural Networks.10.1109/IJCNN.2013.6706807

[2] Martín A., Paul B ., Jianmin C, Zhifeng C, Andy D, Jeffrey D, .... Xiaoqiang Zheng. (2016). "TensorFlow: a system for large-scale machine learning", in Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation(OSDI'16). USENIX Association, USA, 265–283

[3] Girshick R, Donahue J, Darrell T, Malik J (2014). "Rich feature hierarchies for accurate object detection and semantic segmentation", 2014 IEEE Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.2014.81

[4] Tan C, Sun F, Kong T, Zhang W, Yang C and Liu C (2018) “A survey on deep transfer learning”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11141 LNCS, pp. 270–279. doi: 10.1007/978-3-030-01424-7_27.

[5] Sultana, F., Sufian, A. and Dutta, P. (2018). “Advancements in image classification using convolutional neural network”, in Proceedings - 2018 4th IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2018, pp. 122–129. doi: 10.1109/ICRCCICN.2018.8718718.

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

This collection contains data sets from Mobile, Terrestial and Aerial LiDAR Surveys carried out across Queensland road corridors.