Without minimizing or removing these errors, poor segmentation of potholes may occur, subsequently complicating their characterization [22]. Therefore, to minimize uncertainties surrounding thresholding performance, calibration of UAV multispectral imagery to potholes is required prior to the deployment of thresholding classifier. Uncalibrated models tend to either underestimate or overestimate the probability of outcomes [24], subsequently making insignificant decisions regarding pothole size. Performed prior to image classification, calibration adjusts the image radiometric properties to closely match the pothole features [25]. The outcome of the UAV multispectral data calibration must be the degree to which a pixel belongs to a pothole class, instead of a pixel directly becoming a member of a pothole class [26]. Calibration of UAV multispectral data to potholes can be achieved by deploying either isotonic regression (also known as ROC convex hull approach) [27] or Platt-based sigmoid [28]. Although the isotonic regression approach is capable of calibrating probability of classifier scores [29], it is subjected to model overfitting when datasets are smaller, such as when dealing with few potholes. The Platt-based sigmoid approach has demonstrated the ability to overcome the shortfalls associated with the isotonic regression calibration model [30]. This approach was inspired by biological neural networks and has been deployed extensively in training machine learning algorithms, especially as an activation function in artificial neural networks [31]. Of these neural networks, the multilayer perceptron (MLP), trained with backpropagation learning, has become the most popular neural network on which sigmoid calibration function is deployed [32]. This calibration model can fit a logistic regression classifier to UAV multispectral data using the value 0 or 1 to produce a binary image. These binary values can serve as the pothole output points at which the sigmoid function saturates [33]. This approach is important if thresholds are required for demarcation of pothole surface from multispectral imagery [26], especially when the pothole class requires quantification. The sigmoid calibration function can calibrate potholes by relying on two parameters, namely (a) a location parameter (\(m\)), which specifies the sigmoidal midpoint, where the calibrated score is 0.5, and (b) a shape parameter (\(\gamma\)), which specifies the slope of the sigmoid at the midpoint [34]. In this case, the probability outcomes of the calibration model are thresholded to reduce mischaracterization of features [35]. The responsibility of thresholding is to convert a projected probability or score into a certain class. Several studies on pothole characterization based on UAV multispectral imagery focused on improving methods and techniques for detecting potholes [18; 36]. However, the deployment of these approaches without them being subjected to a calibration process poses a question regarding their ability to compensate for information loss due to noise associated with ultra-spatial resolution of UAV sensors. To our best knowledge, there is currently no study that has characterized potholes by integrating entropy thresholding segmentation classifier and sigmoid calibration function. In the quest for optimized characterization of potholes from UAV multispectral imagery, we proposed an approach which combine sigmoid calibration function and entropy thresholding segmentation classifier.