arXiv:2607.06529v1 Announce Type: new Abstract: Fine-scale socioeconomic information is often unavailable across rapidly ur-banizing regions of the developing world, like India, limiting the ability to delineate intra-urban variations in affluence and deprivation. This study pro-poses a scalable, grid-based urban delineation framework using building morphology derived from open-source satellite imagery. Urban areas across 59 Indian cities and towns are partitioned into high-resolution spatial grids and characterized using interpretable morphological indicators, which are combined into a transp
The proliferation of open-source satellite imagery and advanced AI techniques makes fine-grained geospatial analysis increasingly feasible, enabling new methods for socioeconomic evaluation in data-scarce regions.
Accurate, fine-scale socioeconomic data is crucial for effective urban planning, resource allocation, and understanding societal disparities, particularly in rapidly developing economies.
Traditional reliance on census data for socioeconomic delineation can now be augmented or replaced by scalable, AI-driven methods using readily available satellite imagery, offering faster and more granular insights.
- · Urban Planners
- · Development NGOs
- · Indian Government
- · Geospatial AI Companies
- · Traditional Survey Methods
- · Organizations relying on outdated demographic data
Improved understanding of intra-urban wealth distribution and deprivation in Indian cities.
More targeted policy interventions and infrastructure development specifically tailored to neighborhood-level needs.
Potential for similar geospatial AI frameworks to be applied globally to address data gaps in other developing regions, informing investment and humanitarian efforts.
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Read at arXiv cs.CL