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Participatory sensing-based geospatial localization of distant objects for disaster preparedness in urban built environments

机译：基于参与式感知的远距离物体的地理空间定位，用于城市建成环境中的备灾

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摘要

Although the benefit of participatory sensing for collecting local data over large areas has long been recognized, it has not been widely used for various applications such as disaster preparation due to a lack of geospatial localization capability with respect to a distant object. In such applications, objects of interest need to be robustly localized and documented for supporting data-driven decision-making in site inspection and resource mobilization. However, participatory sensing is inappropriate to localize a distant object due to the absence of ranging sensors in citizens' mobile devices; thus, the localization accuracy varies to a large extent. To address this issue, this study presents a novel geospatial localization method for distant objects based on participatory sensing. The proposed geospatial localization process consists of multiple computational modules a geographic coordinate conversion, mean-shift clustering, deep learning-based object detection, magnetic declination adjustment, line of sight equation formulation, and the Moore-Penrose generalized inverse method to improve the localization accuracy in participatory sensing environments. The experiments were conducted in Houston and College Station in Texas to evaluate the proposed method, and the experimental results demonstrated a reasonable localization accuracy, recording the distance errors of 1.5 m to 27.8 m when the distance from observers to the objects of interest were 17 m to 296 m. The proposed method is expected to contribute to rapid data collection over large urban areas, thereby facilitating disaster preparedness that needs to identify locations of distant objects at risk.
机译：尽管人们早已认识到参与式感测在大范围内收集本地数据的好处，但由于缺乏相对于远处物体的地理空间定位能力，因此尚未广泛用于各种应用，例如灾难准备。在此类应用中，需要对感兴趣的对象进行可靠的本地化和记录，以支持在现场检查和资源调动中以数据为依据的决策。但是，由于市民的移动设备中缺少测距传感器，因此参与式感应不适用于定位远处的物体。因此，定位精度在很大程度上变化。为了解决这个问题，本研究提出了一种基于参与感测的新颖的地理空间定位方法。拟议的地理空间定位过程由多个计算模块组成，包括地理坐标转换，均值平移聚类，基于深度学习的目标检测，磁偏角调整，视线方程公式化以及Moore-Penrose广义逆方法，以提高定位精度在参与式感应环境中。实验在德克萨斯州的休斯顿和大学城进行，以评估所提出的方法，实验结果证明了合理的定位精度，当观察者到目标物体的距离为17 m时记录了1.5 m至27.8 m的距离误差。至296 m。预期所提出的方法将有助于在大城市范围内快速收集数据，从而促进需要识别远处处于危险中的物体位置的备灾工作。

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