The city of Munich has been a pilot city in the ICOS Cities project, which aimed to develop greenhouse gas measurement methodologies and related services for urban areas to support climate action. A part of Munich's city observatory is SCOUT (Street-level Carbon Observatory for Urban Terrain), a network of 100 battery-powered, low-cost, non-dispersive infrared (NDIR) CO₂ sensors. This publication describes the sensor network, the applied data processing strategy, and assesses sensor performance. Data from May 2023 to December 2025 are covered. We propose a novel sensor calibration approach that accounts for the strong, non-stationary temperature dependence of the raw sensor signal, thereby improving the manufacturer’s measurement accuracy by a factor of four to eight. In the first stage, a rolling ordinary least squares regression is used to estimate the time-varying, temperature-induced signal component. This is followed by a multilinear regression that infers the final CO₂ concentration and corrects for humidity and pressure effects. Evaluated across 21 sensors over three to twelve months outside the training time period at a reference site equipped with a calibrated Picarro analyzer, the proposed model achieves a median RMSE of 9.46 [6.1–12.7] ppm and a R² of 0.76 [0.55–0.89] for hourly averaged values. It also shows minimal residual dependence on environmental parameters. Long-term drift is corrected by relating the sensor signal to nearby reference stations during favorable atmospheric conditions with homogeneous CO₂ concentration across the city. We consider three distinct correction scenarios and estimate a city-wide, location-dependent correction uncertainty of σ between 2.3 and 3.1 ppm. Combined with the measurement uncertainty, this yields a total uncertainty of 9.95 [6.4–13.1] ppm. We deployed the sensor systems in pairs and present a strategy to combine the signals from duplicate measurements at the same site. The network can characterize local emission regimes: winter morning CO₂ enhancements show a high correlation with Munich's gridded 2024 emission inventory (r = 0.79), indicating that street-level low-cost networks can serve as proxies for local emissions.