Transportation

The dataset consists of vessel tracking data in the form of AIS observations in the Baltic Sea during years 2017-19. The AIS observations have been enriched with vessel metadata such as power, max speed and draft. The data has been collected for master’s thesis work and the data has been splitter into training and validation sets. The AIS observations do not cover all months of the collection period. 

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  • Transportation
  • Last Updated On: 
    Tue, 03/24/2020 - 11:06

    Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance.

    61 views
  • Artificial Intelligence
  • Last Updated On: 
    Wed, 02/26/2020 - 09:13

    The dataset has been colected around the Valencia Seaport. This dataset contains AIS raw data from January, February, March 2017. This dataset has been used in the testing of the Seaport Data Space proposed in the journal article titled "Seaport Data Space for Improving Logistic Maritime Operations".

    24 views
  • Transportation
  • Last Updated On: 
    Mon, 02/24/2020 - 11:58

    Dataset of GPS, inertial and WiFi data collected during road vehicle trips in the district of Porto, Portugal. It contains 40 trip datasets collected with a smartphone fixed on the windshield or dashboard, inside the road vehicle. The dataset was collected and used in order to develop a proof-of-concept for "MagLand: Magnetic Landmarks for Road Vehicle Localization", an approach that leverages magnetic anomalies created by existing road infrastructure as landmarks, in order to support current vehicle localization system (e.g. GNSS, dead reckoning).

    130 views
  • Machine Learning
  • Last Updated On: 
    Thu, 02/27/2020 - 11:58

    This is the image of data.

    128 views
  • Machine Learning
  • Last Updated On: 
    Wed, 01/29/2020 - 09:02

    Pedestrian detection has never been an easy task for computer vision and automotive industry. Systems like the advanced driver assistance system (ADAS) highly rely on far infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what is the performance in adverse weather conditions.

    134 views
  • Computer Vision
  • Last Updated On: 
    Wed, 01/22/2020 - 18:00

    Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection.

    651 views
  • Artificial Intelligence
  • Last Updated On: 
    Tue, 01/21/2020 - 14:54

    The file 'GPS_P2.zip' is the dataset collected from the GNSS sensor of "Xinda" autonomous vehicle in the Connected Autonomous Vehicles Test Fields (the CAVs Test Fields) Weishui Campus,Chang'an University.

    The file 'fault.zip' is the simulated faults in the healthy data in '.mat' format, where X_abrupt, X_noise and X_drift represent abrupt faults, noise and drift in the long run are added into the healthy data, respectively.

    147 views
  • Machine Learning
  • Last Updated On: 
    Thu, 02/20/2020 - 03:50

    This is the data supporting the research of "driving cycle of Haikou bus"

    50 views
  • Transportation
  • Last Updated On: 
    Mon, 11/25/2019 - 20:37

    Dataset consists of various open GIS data from the Netherlands as Population Cores, Neighbhourhoods, Land Use, Neighbourhoods, Energy Atlas, OpenStreetMaps, openchargemap and charging stations. The data was transformed for buffers with 350m around each charging stations. The response variable is binary popularity of a charging pool.

    132 views
  • Machine Learning
  • Last Updated On: 
    Thu, 10/31/2019 - 07:05

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