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这是谁的城市? 用公民指导的AI绘制感知到的城市宜居性
Florencio Campomanes V1, Angela Abascal1,2, Lorraine Trento Oliveira1
1University of Twente, Enschede, Netherlands.
概括
受过贫困城市地区 (DUA) 居民偏好培训的人工智能选民比受过城市规划师培训的人更准确地绘制城市居住条件. 这种方法通过结合当地见解来提高包容性,减少90%的数据需求.
科学领域:
- 城市规划和遥感技术
- 人工智能和机器学习
- 地理空间分析和社会公平
背景情况:
- 城市居住能力指标往往忽略了贫困城市地区 (DUA) 居民的经验,这可能会加剧不平等.
- 传统的指标可能无法捕捉到不同城市人口的细微偏好.
- 整合参与式方法对于公平的城市发展至关重要.
研究的目的:
- 开发和评估轻量级的深度学习模型 ("AI-voters") 用于绘制城市可居住性的地图.
- 为了比较在DUA居民和城市规划者身上训练的AI选民偏好.
- 评估人工智能在可扩展,包容性城市规划方面的潜力.
主要方法:
- 开发了使用开源卫星图像的轻量级深度学习模型 ("AI-voters").
- 从加纳大阿克拉大都会地区的DUA居民和城市规划人员中训练了关于宜居性偏好的模型.
- 采用了两步城市形式采样策略,以减少90%的数据需求,以实现可扩展的参与式绘图.
主要成果:
- 与那些受过城市规划师培训的人相比,受过DUA居民偏好培训的AI选民更好地反映了当地对城市宜居性的见解.
- 城市规划者之间存在分歧,并始终分配更高的宜居性得分,忽视了DUA居民的偏好.
- 人工智能选民准确地反映了基于城市物理特征 (如绿地和建筑密度) 的人类偏好.
结论:
- 将社区观点整合到人工智能模型中对于公平地绘制城市居住能力至关重要.
- 根据DUA居民的偏好受过训练的AI选民可以作为本地洞察力的可扩展代理.
- 这种方法可以揭露隐藏的空间不平等,并促进更具包容性的城市发展.
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