Urban Building Energy Modeling (UBEM) is essential for urban energy-related applications. Its generation mainly requires four data inputs, including geometric data, non-geometric data, weather data, and validation and calibration data. A reliable UBEM depends on the quantity and accuracy of the data inputs. However, the lack of available data and the difficulty in determining stochastic data are two of the main barriers in the development of UBEM. To bridge the research gaps, this paper reviews appropriate acquisition approaches for four data inputs, learning from both building science and other disciplines such as geography, transportation and computer science. In addition, detailed evaluations are also conducted in each part of the study, and the performance of the approaches are discussed, as well as the availability and cost of the implemented data. Systematic discussion, multidisciplinary analysis and comprehensive evaluation are the highlights of this review.