Learning Environmental Maps - Integrating Participatory Sensing and Human Perception
In the context of this project we offer bachelor and master theses, as well as practica and HiWi jobs. If you are interested please contact Martin Becker, Florian Lautenschlager or Andreas Hotho.
Personal sensors are increasingly popular and many communities are working on providing mobile, low-cost sensor solutions in order to measure their personal environment, to map their immediate surroundings, to validate official sources, and ultimately to impact policy making. Such public interest can be useful to build sensor networks with a much greater spatial coverage and allow for efficient large-scale case studies. However, at the same time low-cost sensor are mostly inaccurate and applied measuring protocols are usually not compatible with official regulations. Additionally, the temporal coverage for mobile sensors is not as high as for stationary ones and personalization often results in less regular measurements. Even more, low-quality devices together with measurement biases of special interest groups can lead to misinterpretations of the data and in the end to an erroneous perception of reality.
Thus, this project works on three intertwined problems: 1) We are analyzing perceptions, subjective opinions, behavior and different motivations of user groups and individuals in the context of participatory sensing. 2) In the same context we are investigating how to optimize the applicability of personalized, low-cost and mobile sensors. In particular this means optimizing sensor measurements by different advanced calibration mechanisms on the one hand and providing appropriate information to correctly interpret the measurements on the other hand. And 3), we aim to build maps with integrated views on sensor values, corresponding predictions, as well as perceptions, and subjective data. This will facilitate an aggregated view on the collected data on the one hand and provide meaningful information for interpreting the measurements on the other hand.
Overall, we aim to combine results from user analysis and sensor characteristics utilizing advanced machine learning methods in order to allow for emerging synergies in the area of more accurate maps and perceptual feedback. To this end, we will integrate data from official sources, different types of devices and user studies explicitly focusing on perceptions and other subjective data in the context of noise and air quality. The envisioned results of this project are 1) a deeper understanding of perception and subjective impressions in the context of participatory sensing, 2) how to leverage the collected data and user information in order to extract usable statistics, as well as 3) visualizations, e.g., maps, to allow for an informed interpretation of the collected data and the environment.
MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images. in ACM Trans. Spatial Algorithms Syst. (2020). 6(3)
Air Trails -- Urban Air Quality Campaign Exploration Patterns. (2018).
EveryAware Gears: A Tool to visualize and analyze all types of Citizen Science Data. D. Burghardt, Chen, S., Andrienko, G., Andrienko, N., Purves, R., Diehl, A. (eds.) (2018).
MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data. in Data Mining and Knowledge Discovery (2017).