Understanding the sensing context of raw data is crucial for assessing the quality of large crowdsourced spatio-temporal datasets and supporting context-augmented personal trajectories. Detecting sensing contexts in the wild is a challenging task and requires features from smartphone sensors that are not always available. In this paper, we propose three heuristic algorithms for detecting sensing contexts such as in/out-pocket, under/over-ground, and in/out-door for crowdsourced spatio-temporal datasets. These are unsupervised binary classifiers with a small memory footprint and execution time. Using a segment of the Ambiciti real dataset-a feature-limited crowdsourced dataset-we report that our algorithms perform equally well in terms of balanced accuracy (within 4.3%) when compared to machine learning (ML) models reported by an AutoML tool.