Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
Int J Health Geogr. 2024 Nov 5;23(1):23. doi: 10.1186/s12942-024-00382-7.
The creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).
We trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.
The initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.
The results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.
灾害、冲突或其他形式的外部因素发生后，创建难民营往往会产生额外的健康问题。在一个高度紧张的环境中，人们密集居住，食品安全和水供应存在疑问，这为传染病的爆发提供了潜在的条件。这些营地也不是静态的数据事件，而是在规模、组成以及服务提供的水平和质量方面不断波动。虽然针对这些营地进行上下文相关的地理空间数据收集和制图对于理解这些营地的性质至关重要，但各种挑战，包括缺乏所需的空间或时间粒度的数据，以及可持续性问题，可能会成为主要的障碍。在这里，我们提出了使用空间视频（SV）进行动态制图的深度学习解决方案的第一步。
我们使用从刚果民主共和国戈马收集的 SV 数据集对卷积神经网络（CNN）模型进行了训练，以从视频图像中识别难民营。我们开发了一种空间滤波方法来解决与空间标记对象相关的挑战，例如全球定位系统的准确性和相机的定位。空间滤波方法生成了检测的平滑表面，这些表面可以通过应用技术（如栅格数学）进一步用于捕获微环境的变化。
初步结果表明，我们的模型可以从 SV 图像中以高精度、高召回率和物体定位检测临时物理住所。空间滤波方法有助于识别营地密度较高的区域，并且基于网络的工具可以帮助探索这些区域。基于对检测表面应用栅格数学的纵向分析揭示了在空间和时间上帐篷分布有较大变化的位置。
这些结果为从图像数据自动制图空间特征奠定了基础。我们预计，这项工作是 SV、物体识别和自动制图未来结合的基础，这种结合可为难民营或其他非正规住区等具有挑战性的环境提供可持续的数据生成可能性。
