A computer-implemented method for predicting sensor error comprising: receiving first sensor data from at least one sensor (103, 105, 503, 505, 507), the first sensor data collected at a geographic location; extracting a set of input features from the first sensor data, wherein the extracting of the set of input features comprises processing the first sensor data to determine one or more environmental structures (205, 509) at the geographic location; and processing the set of input features using a machine learning model to calculate a predicted sensor error of a target sensor operating at the geographic location, the target sensor being a location sensor configured to process signals from positioning satellites, wherein the set of input features includes one or more attributes of the one or more environmental structures (205, 509) at the geographic location, wherein the at least one sensor (103, 105, 503, 505, 507) is different from the target sensor (103, 105, 503), and wherein the at least one sensor (103, 105, 503, 505, 507) includes a LiDAR sensor, a camera sensor, or a combination of a LiDAR sensor and a camera sensor, and wherein the machine learning model has been trained on ground truth sensor error data to use the set of input features to calculate the predicted sensor error, the ground truth sensor error data comprising a set of training features extracted from second sensor data labeled with ground truth sensor error values, the second sensor data labeled with ground truth sensor error values including location data from a location sensor configured to process signals from positioning satellites, image data from a camera sensor, and three-dimensional mesh data from a LiDAR sensor for a plurality of probe points, a true location of each of the probe points being known, and the ground truth sensor error values being represented by an offset between the true location and a sensed location at each of the probe points. receiving first sensor data from at least one sensor (103, 105, 503, 505, 507), the first sensor data collected at a geographic location; extracting a set of input features from the first sensor data, wherein the extracting of the set of input features comprises processing the first sensor data to determine one or more environmental structures (205, 509) at the geographic location; and processing the set of input features using a machine learning model to calculate a predicted sensor error of a target sensor operating at the geographic location, the target sensor being a location sensor configured to process signals from positioning satellites, wherein the set of input features includes one or more attributes of the one or more environmental structures (205, 509) at the geographic location, wherein the at least one sensor (103, 105, 503, 505, 507) is different from the target sensor (103, 105, 503), and wherein the at least one sensor (103, 105, 503, 505, 507) includes a LiDAR sensor, a camera sensor, or a combination of a LiDAR sensor and a camera sensor, and wherein the machine learning model has been trained on ground truth sensor error data to use the set of input features to calculate the predicted sensor error, the ground truth sensor error data comprising a set of training features extracted from second sensor data labeled with ground truth sensor error values, the second sensor data labeled with ground truth sensor error values including location data from a location sensor configured to process signals from positioning satellites, image data from a camera sensor, and three-dimensional mesh data from a LiDAR sensor for a plurality of probe points, a true location of each of the probe points being known, and the ground truth sensor error values being represented by an offset between the true location and a sensed location at each of the probe points.