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1. Human-centered GeoAI foundation models: where GeoAI meets human dynamics

   https://doi.org/10.1007/s44212-025-00067-x  

   Ye, Xinyue; Du, Jiaxin; Li, Xinyu; Shaw, Shih-Lung; Fu, Yanjie; Dong, Xishuang; Zhang, Zhe; Wu, Ling (February 2025, Urban Informatics)

   Abstract This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts. 

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2. GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

   https://doi.org/10.3390/ijgi11070385  

   Li, Wenwen; Hsu, Chia-Yu (July 2022, ISPRS International Journal of Geo-Information)

   GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography. Although much progress has been made in exploring the integration of AI and Geography, there is yet no clear definition of GeoAI, its scope of research, or a broad discussion of how it enables new ways of problem solving across social and environmental sciences. This paper provides a comprehensive overview of GeoAI research used in large-scale image analysis, and its methodological foundation, most recent progress in geospatial applications, and comparative advantages over traditional methods. We organize this review of GeoAI research according to different kinds of image or structured data, including satellite and drone images, street views, and geo-scientific data, as well as their applications in a variety of image analysis and machine vision tasks. While different applications tend to use diverse types of data and models, we summarized six major strengths of GeoAI research, including (1) enablement of large-scale analytics; (2) automation; (3) high accuracy; (4) sensitivity in detecting subtle changes; (5) tolerance of noise in data; and (6) rapid technological advancement. As GeoAI remains a rapidly evolving field, we also describe current knowledge gaps and discuss future research directions. 

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3. Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection

   https://doi.org/10.3390/rs13112116  

   Hsu, Chia-Yu; Li, Wenwen; Wang, Sizhe (June 2021, Remote Sensing)

   null (Ed.)

   This paper introduces a new GeoAI solution to support automated mapping of global craters on the Mars surface. Traditional crater detection algorithms suffer from the limitation of working only in a semiautomated or multi-stage manner, and most were developed to handle a specific dataset in a small subarea of Mars’ surface, hindering their transferability for global crater detection. As an alternative, we propose a GeoAI solution based on deep learning to tackle this problem effectively. Three innovative features are integrated into our object detection pipeline: (1) a feature pyramid network is leveraged to generate feature maps with rich semantics across multiple object scales; (2) prior geospatial knowledge based on the Hough transform is integrated to enable more accurate localization of potential craters; and (3) a scale-aware classifier is adopted to increase the prediction accuracy of both large and small crater instances. The results show that the proposed strategies bring a significant increase in crater detection performance than the popular Faster R-CNN model. The integration of geospatial domain knowledge into the data-driven analytics moves GeoAI research up to the next level to enable knowledge-driven GeoAI. This research can be applied to a wide variety of object detection and image analysis tasks. 

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4. GeoAI for Public Health

   https://doi.org/10.1201/9781003308423-15  

   Züfle, Andreas; Anderson, Taylor; Kavak, Hamdi; Pfoser, Dieter; Kim, Joon-Seok; Roess, Amira (December 2023, CRC Press)

   Infectious disease spread within the human population can be conceptualized as a complex system composed of individuals who interact and transmit viruses through spatio-temporal processes that manifest across and between scales. The complexity of this system ultimately means that the spread of infectious diseases is difficult to understand, predict, and respond to effectively. Research interest in GeoAI for public health has been fueled by the increased availability of rich data sources such as human mobility data, OpenStreetMap data, contact tracing data, symptomatic online surveys, retail and commerce data, genomics data, and more. This data availability has resulted in a wide variety of data-driven solutions for infectious disease spread prediction which show potential in enhancing our forecasting capabilities. This book chapter (1) motivates the need for AI-based solutions in public health by showing the heterogeneity of human behavior related to health, (2) provides a brief survey of current state-of-the-art solutions using AI for infectious disease spread prediction, (3) describes a use-case of using large-scale human mobility data to inform AI models for the prediction of infectious disease spread in a city, and (4) provides future research directions and ideas. 

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5. Representing science: diversity on the President’s Council of Advisors on Science and Technology

   https://doi.org/10.1093/scipol/scae030  

   Evans, Kenneth_M; Matthews, Kirstin_RW (June 2024, Science and Public Policy)

   Abstract Federal advisory committees related to science and technology are important mechanisms for connecting policy-makers with independent experts and the broader public. The balance of represented expertise and viewpoints shapes a committee’s intended advisory role, consensus building processes, and the quality and impact of its policy recommendations. This paper presents the first historical analysis of the President’s Council of Advisors on Science and Technology (PCAST) and examines its changing balance of social and professional perspectives from 1990 to 2023. We demonstrate that PCAST’s balance has shifted to be more inclusive of different social groups and professional backgrounds over time, particularly under President Biden. We conclude with recommendations for future White House science policy advisory bodies to ensure they are adequately representative of the diversity of perspectives in the US research enterprise and the overall US population. 

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