This dataset is a subset of a much larger data collection and used for the analysis of speed- and time-profiles of trajectories crossing different selected intersections. Resulting findings can be used for the intersection categorization according to traffic regulation types.

The six selected intersections (A - F) and the crossing trajectory samples (green) can be seen in the subsequent figure: https://data.uni-hannover.de/dataset/1a97c0df-4659-43bd-99e9-d2573d31f5cd/resource/0571e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png" alt="Intersections">

Data Acquisition

The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Hannover, Germany. The acquisition period was from December 2017 to March 2019 by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

OSM point features in Europe with tags categorized as POI in the Geofabrik taxonomy. Extracted from original OpenStreetMap database (osm.pbf) in 09.2019 (https://www.openstreetmap.org/#map=7/46.825/8.224)

Overview

This dataset provides high-resolution, georeferenced vehicle trajectories collected via drone footage at three roundabouts located in the municipalities of Frick and Laufenburg, Canton of Aargau, Switzerland. The data were collected as part of a collaborative drone campaign organized by the Urban Transport Systems Laboratory (LUTS), EPFL, within the framework of NCCR Automation, in cooperation with the cantonal traffic planning department of Aargau. The collection took place on Monday, 23rd October 2023, during peak morning and afternoon hours, resulting in nearly 11 hours of 4K video data.

Dataset Composition

This dataset contains CSV files structured with consistent data fields representing georeferenced trajectories, vehicle types (car, bus, truck), and timestamps, capturing detailed vehicle movements within roundabout environments.

File Organization

File names follow the convention:

D{X}_{TP}{N}_{S}.csv

D{X} — the drone identifier, where {X} is a number (e.g., 1, 2) indicating which drone captured the data.→ Example: D1 = data collected by Drone 1.

{TP}{N} — the time period and session number, where {TP} is either AM (morning) or PM (afternoon), and {N} is an integer indicating the session number.→ Example: AM2 = second morning session.

{S} — the site identifier, corresponding to one of the monitored sites:→ F1 = Roundabout F1 (Frick)→ F2 = Roundabout F2 (Frick)→ L1 = Roundabout L1 (Laufenburg)

CSV File Structure

Each CSV file includes:

Column Name Description Format / Units

track_id Unique vehicle identifier (per file) Integer

type Vehicle type (Car, Bus, Truck) Categorical

lon WGS84 geographic longitude Decimal degrees (15 d.p.)

lat WGS84 geographic latitude Decimal degrees (15 d.p.)

time Local timestamp in ISO 8601 format String (hh:mm:ss.ss)

Data Collection and Processing

Collection Method: Two drones flying at an altitude of 120 meters above ground level, capturing videos at 4K resolution (3840×2160 pixels) at 29.97 FPS.

Locations:

Roundabout F1 (Frick): Intersection of Bahnhofstrasse and Hauptstrasse 3 (Urban)

Roundabout F2 (Frick): Intersection of Hauptstrasse 3 with Gänsacker and Stöcklimattstrasse (Urban)

Roundabout L1 (Laufenburg): Intersection at Hauptstrasse 7 near the German border (Rural)

Data Processing: The detection, tracking, and trajectory stabilization were performed using the early version of the Geo-trax framework (v0.1.0), an advanced computer vision pipeline tailored for drone-captured traffic footage. The resulting trajectories are precisely represented in stabilized pixel coordinates, which are subsequently transformed into geographic coordinates (WGS84). This georeferencing process follows a procedure similar to that described in Espadaler-Clapés et al., 2023, and includes:

Identification and extraction of Ground Control Points (GCPs) in the first stabilized video frame using QGIS Georeferencer, linking pixel coordinates to UTM coordinates.

Linear regression modeling between stabilized pixel coordinates and corresponding UTM coordinates derived from GCPs to estimate transformation parameters.

Projection to WGS84, converting UTM coordinates into global geographic coordinates using a standard GIS transformation (EPSG:4326).

Dataset Statistics

Roundabout Videos Avg. Duration (min) Total Duration (min) Vehicles (total) Cars Buses Trucks

F1 8 18.63 149.04 4,283 3,967 72 244

F2 6 19.24 115.44 2,528 2,205 26 297

L1 4 20.39 81.56 2,130 1,980 24 126

Potential Applications

This dataset is well-suited for:

Gap acceptance behavior studies at roundabouts (e.g., Pascual Anglès et al., 2025)

Traffic flow analysis and modeling

Safety assessments using surrogate safety measures (SSMs)

Validation of traffic simulation models

This dataset is used for the classification of traffic intersections regulation rules using car GPS trajectories. In order to apply supervised classification methods, the ground-truth information is also contained in this dataset. The regulators are annotated based on the intersection arms at each intersection.

An overview of the dataset's trajectories can be seen in the subsequent figure: https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c5fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png" alt="Overview of the dataset containing the trajectories collected in Edessa.">

An overview of the related intersection regulator ground-truth rules can be seen in the following figure: https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c8d75ea3-83ed-445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png" alt="">

Data Acquisition

The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Edessa, Greece. The acquisition period was from March 2018 to September 2018 (6 month) by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second. Additionally to the GPS trajectories, the ground-truth regulator rules of the traffic intersections were annotated via street view images.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning ApproachesJinmeng Rao, Song Gao , Sijia ZhuGeoDS Lab, Department of Geography, University of Wisconsin-Madison, WIInternational Journal of Geographical Information ScienceAbstract: The prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility and GeoAI research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based methodological framework for privacy-preserving trajectory data publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of aggregated human mobility, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k trajectories show that our method has a better performance in privacy preservation, characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research and explores data ethics issues in GIScience.Source code for the work entitled "CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches".Due to the non-disclosure agreement with the data provider, we are not releasing the original individual-level GPS trajectory data but sharing the mocked individual GPS trajectory data with the same data structure and also the k-anonymized aggregated human mobility data used in the experiments.Content Introduction- aggregate_mobility_matrix.py: generate the aggregated mobility distribution file from individual GPS trajectories- cats.py: provides a PyTorch implementation of CATS, including a TrajGenerator (CatGen) class and a TrajDiscriminator (CatCrt) class.- train_cats.py: provides a training example code for CATS.- run_cats.py: provides a inference example code for CATS.- log_util.py: logging class.- stmm_dataset.py: provides a Dataset class used for training the CATS.- stmm_data/: k-anonymized aggregated mobility matrix data.- mocked_individual_gps_data: the mocked individual GPS trajectory data samples with the same data structure with raw data

A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks

Overview

The Songdo Traffic dataset delivers precisely georeferenced vehicle trajectories captured through high-altitude bird's-eye view (BeV) drone footage over Songdo International Business District, South Korea. Comprising approximately 700,000 unique trajectories, this resource represents one of the most extensive aerial traffic datasets publicly available, distinguishing itself through exceptional temporal resolution that captures vehicle movements at 29.97 points per second, enabling unprecedented granularity for advanced urban mobility analysis.

📌 Citation: If you use this dataset in your work, kindly acknowledge it by citing the following article:

Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis (2025). Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery, Transportation Research Part C: Emerging Technologies, vol. 178, 105205. DOI: 10.1016/j.trc.2025.105205.

🔗 Companion dataset: For high-resolution annotated images with vehicle bounding boxes supporting aerial detection research, see Songdo Vision: 10.5281/zenodo.13828408.

Dataset Composition

The dataset consists of four primary components:

• Trajectory Data: 80 ZIP archives containing high-resolution vehicle trajectories with georeferenced positions, speeds and acceleration profiles, and other metadata.
• Orthophoto Cut-Outs: High-resolution (8000×8000 pixel) orthophoto images for each monitored intersection, used for georeferencing and visualization.
• Road and Lane Segmentations: CSV files defining lane polygons within road sections, facilitating mapping of vehicle positions to road segments and lanes.
• Sample Videos: A selection of 4K UHD drone video samples capturing intersection footage during the experiment.

Data Collection

The dataset was collected as part of a collaborative multi-drone experiment conducted by KAIST and EPFL in Songdo, South Korea, from October 4–7, 2022.

• A fleet of 10 drones monitored 20 busy intersections, executing advanced flight plans to optimize coverage.
• 4K (3840×2160) RGB video footage was recorded at 29.97 FPS from altitudes of 140–150 meters.
• Each drone flew 10 sessions per day, covering peak morning and afternoon periods.
• The experiment resulted in 12TB of 4K raw video data.

More details on the experimental setup and data processing pipeline are available in [1].

Data Processing

The trajectories were extracted using 🚀 Geo-trax, an advanced deep learning framework designed for high-altitude UAV-based traffic monitoring. This state-of-the-art pipeline integrates vehicle detection, tracking, trajectory stabilization, and georeferencing to extract high-accuracy traffic data from drone footage.

🎥 A demonstration of the Geo-trax framework in operation is available at: https://youtu.be/gOGivL9FFLk

Key Processing Steps:

1. Vehicle Detection & Tracking: Vehicles were detected and tracked across frames using a deep learning-based detector and motion-model-based tracking algorithm.
2. Trajectory Stabilization: A novel track stabilization method was applied using detected vehicle bounding boxes as exclusion masks in image registration.
3. Georeferencing & Coordinate Transformation: Each trajectory was transformed into global (WGS84), local Cartesian, and orthophoto coordinate systems.
4. Vehicle Metadata Estimation: In addition to time-stamped vehicle trajectories, various metadata attributes were also extracted, including vehicle dimensions and type, speed, acceleration, class, lane number, road section, and visibility status.

More details on the extraction methodology are available in [1].

File Structure & Formats

1. Trajectory Data (Daily Intersection ZIPs, 16.2 MB ~ 360.2 MB)

The trajectory data is organized into 80 ZIP files, each containing traffic data for a specific intersection and day of the experiment.

File Naming Convention:

YYYY-MM-DD_intersectionID.zip

• YYYY-MM-DD represents the date of data collection (2022-10-04 to 2022-10-07).
• intersectionID is a unique identifier for one of the 20 intersections where data was collected (A, B, C, E, …, U). The letter D is reserved to denote "Drone".

Each ZIP file contains 10 CSV files, each corresponding to an individual flight session:

YYYY-MM-DD_intersectionID.zip
│── YYYY-MM-DD_intersectionID_AM1.csv
├── …
│── YYYY-MM-DD_intersectionID_AM5.csv
│── YYYY-MM-DD_intersectionID_PM1.csv
├── …
└── YYYY-MM-DD_intersectionID_PM5.csv

Here, AM1-AM5 and PM1-PM5 denote morning and afternoon flight sessions, respectively. For example, 2022-10-04_S_AM1.csv contains all extracted trajectories from the first morning session of the first day at the intersection 'S'.

CSV File Example Structure:

Each CSV file contains high-frequency trajectory data, formatted as follows (d.p. = decimal place):

* These columns may be empty under certain conditions, see [1] for more details.

2. Orthophoto Cut-Outs (orthophotos.zip, 1.8 GB)

For each intersection, we provide the high-resolution orthophoto cut-outs that were used for georeferencing. These 8000×8000 pixel PNG images cover specific areas, allowing users to overlay orthophoto trajectories within the road network.

orthophotos/
│── A.png
│── B.png
│── …
└── U.png

For more details on the orthophoto generation process, refer to [1].

3. Orthophoto Segmentations (segmentations.zip, 24.9 KB)

We provide the road and lane segmentations for each orthophoto cut-out, stored as CSV files where each row defines a lane polygon within a road section.

Each section (N_G) groups lanes moving in the same

This dataset contains the ground-truth intersection regulators for all intersections crossed by the open GPS trajectory data of Chicago, published on Mapconstruction.org.

Data Acquisition

The ground-truth data is generated by manually inspecting images at the intersections crossed by the published trajectory dataset on Mapconstruction.org. These images are derived from mapillary.com at the respective intersections. Satellite images are investigated as an additional data source, if needed. This procedure result in labels for each intersection arm.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

This study extends a prior National Institute (NIJ) funded study on mirco level places that examined the concentration of crime at places over time. The current study links longitudinal crime data to a series of other databases. The purpose of the study was to examine the possible correlates of variability in crime trends over time. The focus was on how crime distributes across very small units of geography. Specifically, this study investigated the geographic distribution of crime and the specific correlates of crime at the micro level of geography. The study reported on a large empirical study that investigated the "criminology of place." The study linked 16 years of official crime data on street segments (a street block between two intersections) in Seattle, Washington, to a series of datasets examining social and physical characteristics of micro places over time, and examined not only the geography of developmental patterns of crime at place but also the specific factors that are related to different trajectories of crime. The study used two key criminological perspectives, social disorganization theories and opportunity theories, to inform their identification of risk factors in the study and then contrast the impacts of these perspectives in the context of multivariate statistical models.

Interesting sequential patterns in human movement trajectories can provide valuable knowledge for urban management, planning, and location-based business. Existing methods for mining such patterns, however, tend not to consider the reduced likeliness of trips with increasing travel cost. Consequently, it is difficult to differentiate the patterns emerging from people’s specific travel interests from those simply due to travel convenience. To solve this problem, this article presents Geo-SigSPM for mining geographically interesting and statistically significant sequential patterns from trajectories. Here, ‘geographically interesting’ patterns are those more frequent than their expected frequencies which consider both the travel cost and non-redundancy of any place in the patterns. To achieve this, Geo-SigSPM formulates the expected frequencies of the patterns based on doubly-constrained human mobility models and the frequencies of their subsequences. A set of statistical tests is also developed to evaluate the identified interesting patterns. Experiments with synthetic and Foursquare check-in datasets demonstrate the efficacy of Geo-SigSPM in discovering geographically interesting patterns, controlling the spurious pattern rate, and discovering patterns that better reflect people’s specific travel interests than the conventional frequency-based pattern mining approach. Geo-SigSPM is a promising solution to improving relevant decision-making when people’s travel preference beyond travel cost is concerned.

HSR image, POI data, taxi GPS data for Zhuhai UFZ classification project.

This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings from 2000-2010. This work builds upon Silver et al. (2022a) ( https://doi.org/10.5281/zenodo.6436380) which contained Warm Core Ring trajectory information from 2011 to 2020. Combining the two datasets a total of 21 years of weekly Warm Core Ring trajectories can be obtained. An example of how to use such a dataset can be found in Silver et al. (2022b).

The format of the dataset is similar to that of Silver et al. (2022a), and the following description is adapted from their dataset. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 374 WCRs present between January 1, 2000 and December 31, 2010. Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2002 having a trailing alphabet of 'F' indicates that five rings were carried over from 2001 which were still observed on January 1, 2002. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km², and “Date” in days - representing the days since Jan 01, 0000.

The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2000-2010. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986).

Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022a). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380

Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. Journal of Geophysical Research: Oceans, 127(8), e2022JC018737. https://doi.org/10.1029/2022JC018737

Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.

Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.

QGIS Development Team. QGIS Geographic Information System (2016).

Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).

This dataset contains the ground-truth intersection regulators for a majority of intersections of the city of Hannover, Germany. The ground-truth information is used in order to apply machine learning techniques on (car) GPS trajectory data in order to automatically detect the intersection regulation.

https://data.uni-hannover.de/dataset/1123552a-7946-4924-bbbc-aa7fbc6a800f/resource/0d5185cf-1a67-4374-97c7-397b65dad394/download/hannover_rules_1.png" alt="Rules">

The GPS trajectories related to specifically this dataset are (also) available under: https://doi.org/10.25835/9bidqxvl

Data Acquisition

The ground-truth information are acquired by visiting them on-site and apply manual labeling of each intersection arm individually. Furthermore, satellite images and street-level images were considered but only on a minor degree as on-site labeling is found to be more precise and up-to-date.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

Title: Attributing pedestrian networks with semantic information based on multi-source spatial dataAbstract: The lack of associating pedestrian networks, i.e., the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map.

Partical taxi trajectory data in Beijing

The water level data (modeled total water level and in situ measurements) and mapping data produced for the publication "Washout versus Washover: Distinct Trajectories of Barrier Reshaping" in the Journal of Geophysical Research: Earth Surface

We sampled the ground images from the picked videos with a sampling rate of 1/frame/10m to have some visual changes between the consecutive frames, but at the same time, the frames still look relatable to one another. The IMU/GPS data is captured at 1 sample/s. The distance moved in one second varies; the speed of the vehicles is not constant. In this dataset, we care about the visual changes, not the passage of time. So when the distance between every two consecutive locations is greater than 10 m, we resample the video. For more details check "Leveraging Cross-View Geo-Localization With Ensemble Learning And Temporal Awareness".

Description

This dataset contains fingerprint information of WLAN access points and BLE beacons with a known position and IMU sensor data. Data was collected on the floor of the Web and Information Systems Engineering (WISE) Lab at the VUB (Pleinlaan 9, 3rd floor) with 110 training reference points and 30 test data points. Each reference point was recorded for 20 seconds in four different orientations.

More information is given in the README.pdf file included in the dataset.

File Contents

/wlan_aps.csv: Wireless access point information

/ble_beacons.csv: BLE beacon positions

/misc: Miscelanieous resources

/misc/floorplan.png: PNG version of the floorplan

/misc/floorplan_medium.png: PNG version of the floorplan (medium quality)

/misc/datapoints.svg: Training and test data points visualisation

/misc/datapoints.csv: Training data points CSV

/misc/testdatapoints.csv: Test data points CSV

/misc/spaces.geo.json: GeoJSON feature collection of symbolic spaces

/misc/documentation.css: README documentation CSS (unrelated to the dataset)

/train: Training data points (110 in 4 orientations)

/train/raw: Raw unprocessed data points (not aggregated)

/train/raw/wlan_fingerprints.csv: Raw WLAN fingerprints

/train/raw/imu_fingerprints.csv: Raw IMU data collection

/train/raw/ble_fingerprints.csv: Raw BLE fingerprints

/train/aggregated: Processed aggregated data points

/train/aggregated/wlan_fingerprints.csv: WLAN fingerprints

/train/aggregated/ble_fingerprints.csv: BLE fingerprints

/train/aggregated/imu_fingerprints.csv: IMU data collection

/train/aggregated/wlan-ble_fingerprints.csv: WLAN and BLE fingerprints merged

/test: Test data points (30 in 4 orientations)

/test/raw: Raw unprocessed test data points (not aggregated)

/test/raw/wlan_fingerprints.csv: Raw WLAN fingerprints

/test/raw/imu_fingerprints.csv: Raw IMU data collection

/test/raw/ble_fingerprints.csv: Raw BLE fingerprints

/test/aggregated: Processed aggregated data points

/test/aggregated/wlan_fingerprints.csv: WLAN fingerprints

/test/aggregated/ble_fingerprints.csv: BLE fingerprints

/test/aggregated/imu_fingerprints.csv: IMU data collection

/test/aggregated/wlan-ble_fingerprints.csv: WLAN and BLE fingerprints merged

/trajectories: Test trajectories (10)

/trajectories/???: Trajectory directory, ??? is the name of the trajectory

/trajectories/???/???_ble.csv: BLE advertisements received during the trajectory

/trajectories/???/???_imu.csv: IMU data from the trajectory

/trajectories/???/???_wlan.csv: WLAN signals received during the trajectory

This dataset contains weekly trajectory information of Gulf Stream Warm Core Rings from 2011-2020. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 282 WCRs present between January 1, 2011 and December 31, 2020. Each Warm Core Ring and is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2017 having a trailing alphabet of 'E' indicates that four rings were carried over from 2016 which were still observed on January 1, 2017. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km2, and “Date” in days - representing the week since Jan 01, 0000.

The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2011-2020. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986).

Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.

Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.

QGIS Development Team. QGIS Geographic Information System (2016).

Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).