The same location can look different in summer and winter, and we should include both in our training set if we want to perform well in both situations.
We knew about this problem long ago and asked our labellers to add situation-specific labels to our dataset. Each image has associated situation labels. For example, we know for each image if it was day, rainy, if there were roadworks, close traffic participants (or far away), and many more things. With the labels and data we can evaluate our machine learning solutions for different situations, and we know for what situations we need to gather more data. This is useful for us if we want to know how well our machine learning algorithm performs in different situations, and which situations we still need to tackle.