abstract: Imagery data has become important for civil infrastructure operation and maintenance because imagery data can capture detailed visual information with high frequencies. Computer vision can be useful for acquiring spatiotemporal details to support the timely maintenance of critical civil infrastructures that serve society. Some examples include: irrigation canals need to maintain the leaking sections to avoid water loss; project engineers need to identify the deviating parts of the workflow to have the project finished on time and within budget; detecting abnormal behaviors of air traffic controllers is necessary to reduce operational errors and avoid air traffic accidents. Identifying the outliers of the civil infrastructure can help engineers focus on targeted areas. However, large amounts of imagery data bring the difficulty of information overloading. Anomaly detection combined with contextual knowledge could help address such information overloading to support the operation and maintenance of civil infrastructures. Some challenges make such identification of anomalies difficult. The first challenge is that diverse large civil infrastructures span among various geospatial environments so that previous algorithms cannot handle anomaly detection of civil infrastructures in different environments. The second challenge is that the crowded and rapidly changing workspaces can cause difficulties for the reliable detection of deviating parts of the workflow. The third challenge is that limited studies examined how to detect abnormal behaviors for diverse people in a real-time and non-intrusive manner. Using video andii relevant data sources (e.g., biometric and communication data) could be promising but still need a baseline of normal behaviors for outlier detection. This dissertation presents an anomaly detection framework that uses contextual knowledge, contextual information, and contextual data for filtering visual information extracted by computer vision techniques (ADCV) to address the challenges described above. The framework categorizes the anomaly detection of civil infrastructures into two categories: with and without a baseline of normal events. The author uses three case studies to illustrate how the developed approaches can address ADCV challenges in different categories of anomaly detection. Detailed data collection and experiments validate the developed ADCV approaches. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2020