Bridging Geographic Bias in Urban Streetscape Inference via Lifelong Learning with Visual-Semantic Pivoting
arXiv:2606.15055v1 Announce Type: cross Abstract: Visual perception of urban streetscapes underpins evidence-based decisions in landscape planning, public health, and place-making. Yet models trained on a few well-photographed metropolises systematically misjudge underrepresented districts, propagating geographic bias into downstream policy. We address this gap with HVSP-LL, a lifelong learning framework that couples a stratified visual-semantic pivoting module with an equity-aware rehearsal mechanism. The pivoting module organises landscape concepts along a three-tier ontology (macro structur
The proliferation of AI models for urban planning necessitates addressing inherent biases to ensure equitable policy outcomes, making solutions like HVSP-LL timely.
This work directly tackles geographic bias in AI, crucial for the fairness and effectiveness of AI applications in sensitive areas like public health and urban development.
The ability to develop more equitable and unbiased AI models for urban streetscape analysis becomes more viable, potentially leading to better-informed policy decisions.
- · Underrepresented districts
- · Urban planners
- · Public health organizations
- · AI ethics researchers
- · AI models without bias mitigation
- · Homogenous urban data providers
- · Decision-makers reliant on biased AI
AI-driven urban policies will become more equitable and responsive to diverse community needs.
Increased trust in AI applications for civic and governance functions, leading to broader adoption.
Reduced socio-economic disparities in urban environments as policy benefits are more evenly distributed.
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Read at arXiv cs.AI