sktime/tsc-datasets · Datasets at Hugging Face

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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 reported accuracy using combining rules with the XYZ data
was achieved by the distance measure LCSS combined with the
Complexity Invariant Distance (CID-LCSS) which achieved 79.44\%
accuracy (measured with a 5x2 CV). See Table 9in [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 AsphaltPavementType involves three class labels: flexible pavement,
cobblestone streets, and dirt roads. Flexible pavement can be
defined as the one consisting of a mixture of asphaltic or
bituminous material and aggregates placed on a bed of compacted
granular material of appropriate quality in layers over the
subgrade. Flexible pavements are preferred over cement concrete
roads because they can be strengthened and improved in stages with
the growth of traffic.

There are 2111 cases: flexible (816 cases); cobblestone (527
cases); and dirt road (768 cases). Data is variable length, minimum
66 observations, maximum 2371. The data is split randomly into
50/50 default train series. The data can be resampled without bias.

The best reported accuracy with this univariate data was achieved
by the distance measure LCSS combined with the Complexity Invariant
Distance (CID-LCSS) which achieved 88.27\% accuracy (measured with
a 5x2 CV, see Table 7 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

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 reported accuracy using combining rules with the XYZ data

was achieved by the distance measure LCSS combined with the

Complexity Invariant Distance (CID-LCSS) which achieved 79.44\%

accuracy (measured with a 5x2 CV). See Table 9in [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 AsphaltPavementType involves three class labels: flexible pavement,

cobblestone streets, and dirt roads. Flexible pavement can be

defined as the one consisting of a mixture of asphaltic or

bituminous material and aggregates placed on a bed of compacted

granular material of appropriate quality in layers over the

subgrade. Flexible pavements are preferred over cement concrete

roads because they can be strengthened and improved in stages with

the growth of traffic.

There are 2111 cases: flexible (816 cases); cobblestone (527

cases); and dirt road (768 cases). Data is variable length, minimum

66 observations, maximum 2371. The data is split randomly into

50/50 default train series. The data can be resampled without bias.

The best reported accuracy with this univariate data was achieved

by the distance measure LCSS combined with the Complexity Invariant

Distance (CID-LCSS) which achieved 88.27\% accuracy (measured with

a 5x2 CV, see Table 7 of [1]).