Atmospheric pressure sensor s are important devices for several applications, incl uding environment
monitoring and indoor positioning tracking systems. This paper proposes a method to enhance the quality of
data obtained from low-cost atmospheric pressure sensors using a mach ine learning algorithm to predict the
error behaviour. By using the extremely Randomized Trees algorithm, a model was trained with a reference
sensor data for temperature and humidity and with all low-cost sensor datasets that were co-located into an
artificial climatic chamber that simulated different climatic situations. Fifteen low-cost environmental sensor
units, composed by five different models, were considered. They measure – together – temperature, relative
humidity and atmospheric pressure. In the evaluation, three categories of output metrics were considered:
raw; trained by the independent sensor data; and traine d by the low-cost sensor data. The model trained by
the reference sensor was able to reduce the Mean Absolute Error (MAE) between atmospheric pressure sensor
pairs by up to 67%, while the same ensemble trained with all low-cost data was able to reduce the MAE by
up to 98%. These results suggest that low-cost environmental sensors can be a good asset if their data are
properly processed