Abstract: Systems and methods are provided for generating routes taking into consideration user preferences for crime avoidance, in addition to or alternatively to time, distance, and/or cost. User preferences for crime avoidance during a trip from one location to another can be quantified and used to augment standard routing methods by giving weight to crime avoidance. For example, a weighting triangle can be used, where a user chooses a balanced weighting of three factors, including crime avoidance, before a route from the starting location to the destination is generated. The crime rates considered can be limited to those of potentially significant impact on the user, considering the user's modality, demographics, and preferences. The relevant sub-types of crimes can be further assigned sub-weights reflecting the gravity of their potential impact on the user.

Abstract: Systems and methods for generating ground-level terrain elevation models, preparing vector street data to assist in generating such models, and finding approximate elevation of any point using such terrain models are provided. Lidar data can be analyzed, and Lidar elevation values at roadway/street intersections can be used to determine a model of the ground-level elevation in an area or region. Outliers can be removed. The ground-level elevation at any point in the mapped area can be determined using elevation levels for nearby roadway intersections.

Abstract: Systems and methods are provided for detecting one or more anomalous events in video. Histogram-based noise cleansing, higher-order deep convolutional neural network-based feature extraction, instance segmentation, instance summation, difference calculation, and normalization can be used. Human-in-loop systems and methods can facilitate human decisions for anomaly detection. The decision of an anomaly event can be made by, for example, the instance difference value(s).

Abstract: Systems and methods for generating ground-level terrain elevation models, preparing vector street data to assist in generating such models, and finding approximate elevation of any point using such terrain models are provided. Lidar data can be analyzed, and Lidar elevation values at roadway/street intersections can be used to determine a model of the ground-level elevation in an area or region. Outliers can be removed. The ground-level elevation at any point in the mapped area can be determined using elevation levels for nearby roadway intersections.