Critical GeoAI批判性 GeoAI and Society与社会

WHAT KIND OF WORLD DOES GEOAI HELP CREATE?GeoAI 正在参与塑造怎样的世界？ What changes when a neighbourhood becomes a training sample, a journey becomes a predicted route, or a satellite image can be generated from scratch? Each invites us to ask how GeoAI represents the world—and how it shapes the ways we come to know and inhabit it. Whose experiences become visible? Which differences are lost? Who gets to decide what matters?当一个街区成为训练样本，一段旅程变成预测路线，一幅卫星影像可以从零生成，会发生什么？这些变化让我们追问：GeoAI 如何呈现世界，又如何影响我们认识世界、生活于其中的方式？谁的经验被看见？哪些差异被抹去？由谁决定什么值得关注？

Organized collaboratively by the Departments of Geography at the University of Washington and The University of Hong Kong, this forum is jointly convened by Professor Bo Zhao and Associate Professor Junxi Qian. We invite participants to think through these questions together over four weeks, following a connected path from critical reflection to research practice. We begin with the debates between GIS and critical geography, then turn to humanistic geography to consider place, lived experience, and care. These perspectives guide our examination of Deepfake Geography, GeoAI Collapse, and posthuman cartography, before we explore how community knowledge and collaboration can change the questions we ask and the ways we use spatial data.本系列工作坊由华盛顿大学地理系与香港大学地理系合作开展，由赵博教授与钱俊希副教授共同召集，邀请大家在为期四周的交流中一起思考这些问题。讨论将沿着一条从批判反思走向研究实践的线索展开。我们先回顾 GIS 与批判地理学之间的争论与对话，再借助人文主义地理学理解地方、生活经验与关怀；随后将这些视角用于检视 Deepfake Geography、GeoAI Collapse 与 posthuman cartography，最后通过社区研究，讨论地方知识与合作如何改变研究问题及空间数据的使用方式。

Undergraduate and master’s students in spatial data science, GIS, computing, and related fields are invited to bring their own questions, projects, and experiences. Through shared readings, research cases, and discussion, participants will revisit concrete choices in their own work: how data are collected, what models represent, how results are interpreted, and who participates in making these decisions.欢迎空间数据科学、GIS、计算机及相关专业的本科生与硕士生带着自己的问题、项目和经验加入。通过共同阅读、研究案例与讨论，参与者将重新审视自己研究中的具体选择：数据如何收集、模型呈现什么、结果如何解释，以及谁能参与这些决定。