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The original was posted on /r/machinelearning by /u/No-Discipline-2354 on 2025-06-11 14:46:17+00:00.
I am working on a geospatial ML problem. It is a binary classification problem where each data sample (a geometric point location) has about 30 different features that describe the various land topography (slope, elevation, etc).
Upon doing literature surveys I found out that a lot of other research in this domain, take their observed data points and randomly train - test split those points (as in every other ML problem). But this approach assumes independence between each and every data sample in my dataset. With geospatial problems, a niche but big issue comes into the picture is spatial autocorrelation, which states that points closer to each other geometrically are more likely to have similar characteristics than points further apart.
Also a lot of research also mention that the model they have used may only work well in their regions and there is not guarantee as to how well it will adapt to new regions. Hence the motive of my work is to essentially provide a method or prove that a model has good generalization capacity.
Thus other research, simply using ML models, randomly train test splitting, can come across the issue where the train and test data samples might be near by each other, i.e having extremely high spatial correlation. So as per my understanding, this would mean that it is difficult to actually know whether the models are generalising or rather are just memorising cause there is not a lot of variety in the test and training locations.
So the approach I have taken is to divide the train and test split sub-region wise across my entire region. I have divided my region into 5 sub-regions and essentially performing cross validation where I am giving each of the 5 regions as the test region one by one. Then I am averaging the results of each 'fold-region' and using that as a final evaluation metric in order to understand if my model is actually learning anything or not.
My theory is that, showing a model that can generalise across different types of region can act as evidence to show its generalisation capacity and that it is not memorising. After this I pick the best model, and then retrain it on all the datapoints ( the entire region) and now I can show that it has generalised region wise based on my region-wise-fold metrics.
I just want a second opinion of sorts to understand whether any of this actually makes sense. Along with that I want to know if there is something that I should be working on so as to give my work proper evidence for my methods.
If anyone requires further elaboration do let me know :}
