Examples of landmark detection on SEM images (SE detector) from surfaces of different samples. Cracks, scratches, and dirt on the surface make landmark detection difficult and more error-prone. For each square, the left image shows the final detection, with the yellow dot representing the detected center position of the crossing and the red points the corners of the crossing. The right image is the same with inverted brightness and contrast, with red pixels representing the probability of being a grid edge as detected by the neural network. The probability map from the neural network is the result of the network inference, with the set of images used during training different from the images used as input during the experiment, which is shown here. We observe that the neural network can generalize very well the detection of the grid patterns in the resin surface. Here we exemplify the common cases that can lead to an error in the detection of a landmark. (a) The sample is in a perfect state. (b) A crack present in the upper part might affect the predicted accuracy of the overall map, even if the detection is identified as good (or close to it). (c) Scratches can be the cause of false positives for the grid detection, in this case, scratches parallel to the grid bar. Even if this specific error was later corrected by taking also into account the length of the line stroke, we presume that longer scratches than the ones shown in the exemplary image could cause the same problem again. (d) In other cases, dirt and other material residues, e.g., from silver painting (used around the sample border to derive charges), might mislead the detection algorithm and increase the final error. The detection problems might change on a sample basis. A detailed analysis of the error detection is shown in the supplementary material in notebook 2 (https://github.com/josemiserra/CLEMSite_notebooks). Scale bars: all 100 µm.