A computer-implemented method of acquisition of sensor data based on semantic state, the method comprising: receiving, by a computing system comprising one or more computing devices, sensor data based in part on one or more sensor outputs from one or more sensors, wherein the sensor data comprises information associated with one or more states of one or more areas detected by the one or more sensors; generating, by the computing system, based in part on the sensor data, an estimated semantic state of an area of the one or more areas from a target sensor that can detect the one or more states of the one or more areas, wherein the target sensor is configured to detect the state of the one or more areas at a resolution that is higher than a resolution associated with the one or more sensors; determining, by the computing system, based in part on a comparison of the estimated semantic state to one or more semantic states of the area from the one or more sensors, an uncertainty level associated with an accuracy of the estimated semantic state, wherein: the estimated semantic state and the one or more semantic states comprise a set of attributes associated with the one or more states of the area from the target sensor and the one or more sensors not comprising the target sensor respectively; and the uncertainty level comprises a probability that the estimated semantic state is the same as an updated semantic state of the area as would be indicated by an updated version of the sensor data from the target sensor; responsive to the uncertainty level satisfying one or more update criteria, obtaining, by the computing system, the updated version of the sensor data from the target sensor; and determining, by the computing system, a state change rate based in part on an amount of one or more changes in the one or more semantic states of the area over a plurality of time periods, wherein the uncertainty level of the target sensor is based in part on the state change rate. receiving, by a computing system comprising one or more computing devices, sensor data based in part on one or more sensor outputs from one or more sensors, wherein the sensor data comprises information associated with one or more states of one or more areas detected by the one or more sensors; generating, by the computing system, based in part on the sensor data, an estimated semantic state of an area of the one or more areas from a target sensor that can detect the one or more states of the one or more areas, wherein the target sensor is configured to detect the state of the one or more areas at a resolution that is higher than a resolution associated with the one or more sensors; determining, by the computing system, based in part on a comparison of the estimated semantic state to one or more semantic states of the area from the one or more sensors, an uncertainty level associated with an accuracy of the estimated semantic state, wherein: the estimated semantic state and the one or more semantic states comprise a set of attributes associated with the one or more states of the area from the target sensor and the one or more sensors not comprising the target sensor respectively; and the uncertainty level comprises a probability that the estimated semantic state is the same as an updated semantic state of the area as would be indicated by an updated version of the sensor data from the target sensor; the estimated semantic state and the one or more semantic states comprise a set of attributes associated with the one or more states of the area from the target sensor and the one or more sensors not comprising the target sensor respectively; and the uncertainty level comprises a probability that the estimated semantic state is the same as an updated semantic state of the area as would be indicated by an updated version of the sensor data from the target sensor; responsive to the uncertainty level satisfying one or more update criteria, obtaining, by the computing system, the updated version of the sensor data from the target sensor; and determining, by the computing system, a state change rate based in part on an amount of one or more changes in the one or more semantic states of the area over a plurality of time periods, wherein the uncertainty level of the target sensor is based in part on the state change rate.