Explainable Population Estimation Using Deep Learning from Satellite Imagery

使用卫星图像深度学习进行可解释的人口估计

• 批准号: 2890100
• 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-2890100
• 关键词: Explainable; Population; Estimation; Using; Deep

More than one-third of the Sustainable Development Goals (SDGs) indicators established by the United Nations (UN) are defined in terms of total population or a specific demographic sub-population [1]. Up-to-date population information of a region is crucial for decision making including access to services, distribution of vaccinations, disaster relief, and many others. Traditional population data, such as census, are not adequate for this purpose since censuses are typically conducted decennially and countries with the greatest need for up-to-date population counts conduct them even less frequently. Population estimation using alternative data sources such as satellite imagery has received significant attention in the recent years. Both census-dependent [2] and census-independent [3] approaches have been explored with some success, and many of these methods have utilized advanced image analysis methods such as deep convolutional neural networks with promising results. This project will develop these methods further, to produce sustainable, interpretable and reliable machine learning models estimating population more effectively. It will involve utilizing contextual information, both spatial and temporal, integrating satellite imagery at multiple resolutions with publicly available data sources such as land cover map, etc., combining from census, surveys and microcensus data, and explaining the decisions made by these models to the end-users.The aim of the project is to develop sustainable, interpretable, and reliable machine learning models to effectively estimate the population of an area using satellite imagery and survey information. The project will investigate the following research questions: (1) Can contextual neighbourhood information, both short-range and long-range, improve population estimates by understanding the characteristics of the surrounding regions? (2) Can information from census, surveys, and micro-census be combined to track population reliably over time? (3) Can data from different sources and different resolutions be combined to acquire complementary information about an area? (4) Does uncertainty/bias differ in sparsely populated rural areas vary compared to densely populated urban areas? and (5) Can estimated population and associated uncertainty be explained to policymakers effectively?MethodologyThe project will incorporate satellite imagery of different resolutions and survey data using computer vision models, deep learning architecture and explainable model constructs to estimate population of a region.

联合国（联合国）建立的可持续发展目标（SDG）指标的三分之一以上是根据人口总数或特定的人群亚人口的定义[1]。一个地区的最新人口信息对于决策至关重要，包括获得服务，疫苗接种，救灾以及许多其他地区的分配。传统人口数据（例如人口普查）不足以满足此目的，因为普查通常是在十年期间进行的，并且对最新人口的需求最高的国家的频率降低了。近年来，使用替代数据来源（例如卫星图像）进行了估算。已经探索了普查依赖性[2]和与人口普查无关的[3]方法，并获得了一些成功，其中许多方法都利用了高级图像分析方法，例如深卷积神经网络，并具有令人鼓舞的结果。该项目将进一步开发这些方法，以更有效地生成可持续，可解释和可靠的机器学习模型。它将涉及利用空间和时间上的上下文信息，将卫星图像与可公开可用的数据源相结合，例如土地覆盖地图等，结合了人口普查，调查和微观的数据，并将这些模型的决策解释为最终的方法。 信息。该项目将调查以下研究问题：（1）通过了解周围地区的特征来改善人口估计的上下文邻里信息可以改善人口估计吗？ （2）可以合并来自人口普查，调查和微观周期的信息，以随着时间的推移可靠地跟踪人口吗？ （3）可以合并来自不同来源和不同分辨率的数据以获取有关区域的互补信息吗？ （4）与人口稠密的城市地区相比，人口稠密的农村地区的不确定性/偏见是否有所不同？ （5）可以有效地向决策者解释估计的人群和相关的不确定性吗？