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

该项目将构建次国家尺度的贫困时空地图，以支持可持续发展目标的实施，并通过将观测数据与当地精细分辨率评估相结合，实现基于证据的决策。这项任务将涉及了解不同国家、县和地区的经济效益指标与社会经济状况之间的联系，以及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，并识别信号模式异常变化的空间位置，以便可以委托针对这些地区的新调查。深度学习技术，如卷积神经网络（cnn），越来越多地用于遥感图像和地面目标检测、人口、土地测绘等任务的预测分析。该项目将研究深度学习技术，以填补基于地球观测（EO）产品的空间空白。然而，仅使用深度学习模型来推导数据驱动的政策和跨时间和空间的地理目标的缺点是它们缺乏可解释性。事实上，众所周知，这些模型是黑盒子，使得结果不容易解释、证明或直观，因此降低了它们在决策目的方面的实用性。另一方面，统计模型的设计使参数反映数据的不同特征之间的关系，因此是可解释和可转移的。虽然这种程度的可解释性在黑盒深度学习模型中是不可能的，但它们在预测目的方面非常准确。为了解决这种二分法，该项目将开发一种新的工作流程，在保留统计模型的可解释性的同时准确地再现SGD指标。以往的研究通过家庭调查数据和周边地区的地理空间数据建立了家庭贫困之间的关系，但家庭数据只能部分地用于特定的病房。一般来说，病房之间同质性的假设是无效的，这使得社会生态系统差异很大的病房的可转移性成为一个问题。将研究基于高斯过程的地质统计模型，以解决可转移性问题，并通过借鉴邻近区域的资料，最终预测即使在没有数据的地点的贫困情况。该方法将结合多个EO卫星数据和通过时空建模进行的局部精细分辨率评估。这项研究很可能会把重点放在东非。

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.