基于遥感大数据和先进变压器深度学习模型的高精度人口估计

IF 4.5 Q2 ENVIRONMENTAL SCIENCES
Ziyun Yan , Lei Ma , Xuan Wang , Yongil Kim , Liqiang Zhang
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引用次数: 0

摘要

高精度的人口估计对于感知人们的生活地点和生活方式至关重要,从而支持可持续发展目标。然而,目前还没有一个系统的理论来解释地理空间大数据如何在人口估计研究中发挥作用,并且由于最近人工智能(AI)的繁荣,深度学习模型被热切地应用于社会科学(例如人口估计)等领域。本研究中使用了Shapley加性解释(SHAP)工具来检查机器学习模型和地理空间大数据在人口估计过程中的定量解释能力。结果表明,不同的人工智能人口估计模型不仅在估计精度上存在显著差异,而且在对地理空间数据的依赖上也存在显著差异。研究发现,经典的随机森林模型过于依赖衍生的城市形态特征。先进的变压器深度学习模型可以理解场景,做得更好,可以直接从卫星图像中获得与人口相关的语义。随后,通过整合CNN的本地和Transformer的全球解释能力,保证了高精度的人口估计。本研究首次在人口估计中实现了先进的Transformer模型,并在深度学习框架内提供了可解释性证据。它有望成为人工智能在社会科学领域的典型应用示范。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-precision population estimates by remote sensing big data and advanced transformer deep learning model
High-precision population estimation is crucial for sensing where and how people live, which consequently supports sustainable development goals. Yet, there isn't a systematic theory that explains how geospatial big data works in population estimation studies, and deep learning models are eagerly applied in such fields as social sciences (e.g., population estimates) due to the recent prosperity of artificial intelligence (AI). The Shapley Additive Explanations (SHAP) tool was used in this study to check how well machine learning models and geospatial big data could be interpreted quantitatively for the population estimation process. The results show significant disparities among artificial intelligence models for population estimates, not only in estimate accuracy but also in dependencies on geospatial data. It was found that the classic Random Forest model relies too much on derived urban morphological features. The advanced transformer deep learning model, which can understand scenes, does much better and can directly get population-related semantics from satellite imagery. Subsequently, the high-precision population estimates were promised by integrating CNN's local and Transformer's global interpretation abilities. This study firstly implements the advanced Transformer model in population estimates and provides interpretability evidence within the deep learning framework. It was expected to become a typical application demonstration of AI in the social sciences.
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来源期刊
CiteScore
8.00
自引率
8.50%
发文量
204
审稿时长
65 days
期刊介绍: The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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