Learning Environmental Maps - Integrating Participatory Sensing and Human Perception

个人传感器越来越受欢迎，许多社区正在努力提供移动、低成本的传感器解决方案，以便测量他们的个人环境，绘制他们周围的地图，验证官方来源，并最终影响政策制定。这种公共利益可能有助于建立具有更大空间复盖率的传感器网络，并允许进行高效的大规模案例研究。然而，与此同时，低成本的传感器大多不准确，所应用的测量协议通常与官方规定不兼容。此外，移动传感器的时间覆盖范围不像固定传感器那样高，个性化通常会导致较不定期的测量。更重要的是，低质量的设备和特殊利益群体的测量偏差可能会导致对数据的误解，最终导致对现实的错误感知。因此，本项目致力于三个相互交织的问题：1)我们正在参与式感知的背景下分析用户群体和个人的感知、主观意见、行为和不同的动机。2)在相同的背景下，我们正在研究如何优化个性化、低成本和移动传感器的适用性。特别是，这意味着一方面通过不同的高级校准机制来优化传感器测量，另一方面提供适当的信息来正确解释测量结果。3)，我们的目标是构建包含传感器值、相应预测以及感知和主观数据的集成视图的地图。这将有助于一方面对收集的数据进行聚合查看，另一方面为解释测量结果提供有意义的信息。总体而言，我们的目标是利用先进的机器学习方法将用户分析的结果和传感器特征结合起来，以便在更准确的地图和感知反馈领域实现新的协同效应。为此，我们将整合来自官方来源、不同类型的设备和用户研究的数据，明确侧重于噪音和空气质量背景下的感知和其他主观数据。该项目的预期成果是：1)在参与性遥感的背景下更深入地了解感知和主观印象；2)如何利用收集的数据和用户信息以提取可用的统计数据；3)可视化，例如地图，以便对收集的数据和环境作出知情的解释。

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.