Supporting sanitation infrastructure planning in low-income areas with URBANUS - Correia, Maria do Carmo O.; Barcelos, Erick P. A.; Pereira, Augusto C. B.; Ferreira, Gerson Seluque; Cazzolato, Mirela T. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais Estendidos do XLI Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd_estendido.2026.249694, 2026)
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.