EO4SDGs: Spatiotemporal poverty mapping using earth observation data and deep learning in Africa

EO4SDGs：利用地球观测数据和深度学习绘制非洲时空贫困图

• 批准号: 2890076
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890076
• 关键词: EO4SDGs; Spatiotemporal; poverty; mapping; using

This project will build spatiotemporal maps of poverty on a sub-national scale to support the implementation of the SDGs and enable evidence-based decision-making by combining EO data with local fine-resolution assessments. This task will involve understanding associations between EO metrics and socioeconomic conditions as well as the relationships between poverty and geospatial proxies in different countries, counties, and wards. Moreover, the high temporal resolution of the EO data will be used to track changes in SDGs metrics and identify spatial locations with unusual changes in patterns in the signal so that new surveys targeting those regions can be commissioned.Deep learning techniques, such as Convolutional Neural Networks (CNNs), are increasingly used for predictive analytics with remote sensing images and tasks such as ground object detection, population, land mapping, etc. This project will investigate deep learning techniques to fill spatial gaps in earth observation-based (EO) products. However, a drawback of using solely deep learning models to derive data-driven policy and geographic targeting across time and space is their lack of interpretability. Indeed, these models are well known to be black boxes, making the results not easily explained, justified or intuitive, therefore reducing their practicability for policy-making purposes. Statistical models, on the other hand, are designed such that the parameters reflect the relationship between different features of the data and therefore are interpretable and transferable. Although this level of interpretability is not possible in a black-box deep learning model, they are remarkably accurate for prediction purposes. To address this dichotomy, this project will develop a novel workflow that accurately reproduces SGD indicators while retaining the interpretability of statistical models. Previous studies established relationships between household poverty from household survey data and geospatial data for the surrounding area, but household data is available only partially for a specific ward. The assumption of homogeneity between wards is not valid in general, making the transferability an issue for wards with large variations in socioecological systems. Geostatistical models based on Gaussian processes will be investigated to address the problem of transferability and ultimately predict poverty even at locations where no data is available by borrowing information from neighboring regions. The approach will incorporate multiple EO satellite data and local fine-resolution assessments via spatiotemporal modeling. It is likely that the study will have a focus in East Africa.

该项目将在次国家规模上建立贫困的时空地图，以支持实施可持续发展目标，并通过将EO数据与当地的良好分辨率评估相结合来实现基于证据的决策。这项任务将涉及了解EO指标与社会经济条件之间的关联，以及不同国家，县和病房中贫困与地理空间代理之间的关系。此外，EO数据的高时间分辨率将用于跟踪可持续发展目标指标的变化，并确定信号中模式异常变化的空间位置，以便可以委托针对这些区域的新调查。深度学习技术，例如卷积神经神经网络（CNN）越来越多地用于具有遥感图像和任务（例如地面对象检测，人口，土地映射等）的预测分析。该项目将研究深度学习技术，以填补基于地球观测（EO）产品中的空间差距。但是，使用仅使用深度学习模型来得出数据驱动的政策和地理定位的缺点是它们缺乏可解释性。实际上，这些模型众所周知是黑匣子，使结果不容易解释，合理或直观，因此降低了其实用性。另一方面，统计模型的设计使参数反映了数据的不同特征之间的关系，因此可以解释和转移。尽管在黑盒深度学习模型中不可能使用这种可解释性水平，但出于预测目的，它们非常准确。为了解决这种二分法，该项目将开发出一种新型的工作流，该工作流可以准确地再现SGD指标，同时保留统计模型的可解释性。先前的研究确定了家庭调查数据中的家庭贫困与周围地区的地理空间数据之间的关系，但家庭数据仅适用于特定病房。一般而言，病房之间同质性的假设是无效的，这使得转移性成为社会生态系统中差异很大的病房的问题。将研究基于高斯流程的地理模型，以解决可转让性问题，并最终预测贫困，即使在没有通过借入邻近地区的信息获得的位置也无法获得数据。该方法将通过时空建模将多个EO卫星数据和局部精细分辨率评估。这项研究可能会在东非重点。