ML

Inferring Context of Mobile Data Crowdsensed in the Wild

Understanding the sensing context of raw data is crucial for assessing the quality of large crowdsourced spatio-temporal datasets. Accelerometer’s precision can vary considerably depending on whether the phone is in-pocket or out-pocket, i.e., held …

Mobile crowd-sensing as a resource for contextualized urban public policies: a study using three use cases on noise and soundscape monitoring

Environmental noise is a major pollutant in contemporary cities and calls for the active monitoring of noise levels to spot the locations where it most affects the people's health and well-being. However, due to the complex relationship between …

Detecting Mobile Crowdsensing Context in the Wild

Understanding the sensing context of raw data is crucial for assessing the quality of large crowdsourced spatio-temporal datasets. Detecting sensing contexts in the wild is a challenging task and requires features from smartphone sensors that are not …