text stringlengths 0 80 |
|---|
This dataset was used in [1] and donated by the authors of that |
paper. Accelerometer data was collected on a smartphone installed |
inside a vehicle using a flexible suction holder near the |
dashboard. An expert was responsible for driving the vehicle while |
the device ran an Android application called Asfault [2], developed |
specifically to store the current asphalt condition continuously |
over time. Asfault stores the time-stamp of the collected data, |
acceleration forces in along the three physical axes, latitude, |
longitude, and velocity. |
The acceleration forces are given by the accelerometer sensor of |
the device and are the data used for the classification task. |
Latitude, longitude, and velocity are given by the GPS and are used |
to locate the collected/classified data. Each one of this |
information is a different continuous time series. A sampling rate |
of 100 Hz was used for each time series. It is important to note |
that the Android system does not guarantee a perfect precision on |
the interval between readings. This means that when we choose a |
sampling rate of 100 Hz, the device should output between 95 and |
105 observations per second. |
For this data, the (X,Y,Z) acceleration time series are converted |
into a univariate time series that represents the acceleration |
magnitude. |
In order to obtain labeled data, the Asfault application allows the |
expert to inform the pavement condition before collecting the data. |
To guarantee the integrity of data, Asfault also records videos of |
the road over the data collection using the built-in camera. Thus, |
it is possible to perform the analysis of these videos to confirm |
the class labels assigned by the expert. |
The datasets were collected in the Brazilian cities of Sao Carlos, |
Ribeirao Preto, Araraquara, and Maringa using a medium sized |
hatchback car (Hyundai i30) and two different devices (Samsung |
Galaxy A5 and Samsung S7). |
The problem AsphaltObstacles involves the identification of four |
common obstacles in the region of data collection. It has the |
following class labels: raised_crosswalk (160 cases); |
raised_markers (187 cases); speed_bump (212 cases); and |
vertical_patch (222 cases). Data is variable length, minimum 111 |
observations, maximum 736. The data is split randomly into 50/50 |
default train series. The data can be resampled without bias. |
The best result for the Asphalt-Obstacles dataset was achieved by |
DTW distance with 81.13\% accuracy (Table 9 of [1]) |
[1] Souza V.M.A. Asphalt pavement classification using smartphone |
accelerometer and Complexity Invariant Distance. Engineering |
Applications of Artificial Intelligence Volume 74, pp. 198-211. |
https://www.sciencedirect.com/science/article/pii/S0952197618301349 |
[2] Souza V.M.A., Cherman E.A., Rossi R.G., Souza R.A. Towards |
automatic evaluation of asphalt irregularity using smartphones |
sensors International Symposium on Intelligent Data Analysis |
(2017), pp. 322-333 |
This dataset was used in [1] and donated by the authors of that |
paper. Accelerometer data was collected on a smartphone installed |
inside a vehicle using a flexible suction holder near the |
dashboard. An expert was responsible for driving the vehicle while |
the device ran an Android application called Asfault [2], developed |
specifically to store the current asphalt condition continuously |
over time. Asfault stores the time-stamp of the collected data, |
acceleration forces in along the three physical axes, latitude, |
longitude, and velocity. |
The acceleration forces are given by the accelerometer sensor of |
the device and are the data used for the classification task. |
Latitude, longitude, and velocity are given by the GPS and are used |
to locate the collected/classified data. Each one of this |
information is a different continuous time series. A sampling rate |
of 100 Hz was used for each time series. It is important to note |
that the Android system does not guarantee a perfect precision on |
the interval between readings. This means that when we choose a |
sampling rate of 100 Hz, the device should output between 95 and |
105 observations per second. |
For this data, the (X,Y,Z) acceleration time series are treated as |
separate dimensions. A univariate version that combines the data is |
also in the archive. |
In order to obtain labeled data, the Asfault application allows the |
expert to inform the pavement condition before collecting the data. |
To guarantee the integrity of data, Asfault also records videos of |
the road over the data collection using the built-in camera. Thus, |
it is possible to perform the analysis of these videos to confirm |
the class labels assigned by the expert. |
The datasets were collected in the Brazilian cities of Sao Carlos, |
Ribeirao Preto, Araraquara, and Maringa using a medium sized |
hatchback car (Hyundai i30) and two different devices (Samsung |
Galaxy A5 and Samsung S7). |
The problem AsphaltObstacles involves the identification of four |
common obstacles in the region of data collection. It has the |
following class labels: raised_crosswalk (160 cases); |
raised_markers (187 cases); speed_bump (212 cases); and |
End of preview. Expand
in Data Studio
No dataset card yet
- Downloads last month
- 80