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Access to sanitation infrastructure remains a critical challenge in low-income urban areas, with direct consequences for public health and social equity. We propose URBANUS, a web-based decision-support platform that automates the preliminary design of sanitary sewer collection networks using publicly available geospatial data. The system models the urban street network as a graph, enriches vertices with topographic elevation, and applies a gravitational routing heuristic (RSPH) to generate a directed sewer network aligned with terrain constraints. An interactive editor allows specialists to inspect and adjust the graph before processing, keeping human expertise inside the computational workflow. A deep learning module for building detection from satellite imagery, based on fine-tuned models, enables accurate sewage contribution estimates per lot. A use case with a Brazilian municipality reduced the domain expert work from days to seconds. URBANUS aims to support engineers, municipalities, and policymakers in expanding coverage to underserved communities.

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#	Name
1	Maria do Carmo Correia(carmem@selco.com.br)
2	Erick Barcelos(brcls@usp.br)
3	Augusto Pereira(augustocbp@usp.br)
4	Gerson Ferreira(gerson@selco.com.br)
5	Mirela Cazzolato(mtcazzolato@gmail.com)

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