A method performed by a computer system comprising one or more network nodes for determining at least one parameter associated with an environmental categorization of an area, the environmental categorization representing the area at a specified point in time or a specified time interval, the method being: training (S31, S41) a machine learning model using a set of training data representing a first set of radio frequency characteristics or a performance management counter; receiving (S32, S42) at least one radio frequency characteristic or a performance management counter , wherein the received radio frequency characteristic is from radio frequency characteristics of radio waves of signals propagated from at least one wireless communication device to a radio base station , and wherein information contained within the received performance management counter is derived from radio frequency characteristics of radio waves of signals propagated from at least one wireless communication device to a radio base station ; obtaining (S33, S43) the parameter associated with the environmental categorization , by using the machine learning model after the training on the received radio frequency characteristic or the received performance management counter ; and sending (S34) or initiating the sending of (S44) the parameter associated with the environmental categorization of the area obtained using the machine learning model to any one of: an information storage , a communication device , and a network node of the computer system wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion and/or pedestrian traffic congestion in the area and the traffic congestion parameter comprises at least any one of a vehicle flow rate, a vehicle count, and a vehicle type. training (S31, S41) a machine learning model using a set of training data representing a first set of radio frequency characteristics or a performance management counter; receiving (S32, S42) at least one radio frequency characteristic or a performance management counter , wherein the received radio frequency characteristic is from radio frequency characteristics of radio waves of signals propagated from at least one wireless communication device to a radio base station , and wherein information contained within the received performance management counter is derived from radio frequency characteristics of radio waves of signals propagated from at least one wireless communication device to a radio base station ; obtaining (S33, S43) the parameter associated with the environmental categorization , by using the machine learning model after the training on the received radio frequency characteristic or the received performance management counter ; and sending (S34) or initiating the sending of (S44) the parameter associated with the environmental categorization of the area obtained using the machine learning model to any one of: an information storage , a communication device , and a network node of the computer system wherein the parameter associated with the environmental categorization of the area comprises at least one traffic congestion parameter, wherein the traffic congestion parameter is at least an indication of vehicle traffic congestion and/or pedestrian traffic congestion in the area and the traffic congestion parameter comprises at least any one of a vehicle flow rate, a vehicle count, and a vehicle type. <EOS>