You gather imagery for three regions of California and chip them without replacement you construct your train and test sets by mixing these chips together and creating two splits. Let's say you're a geospatial MLE interested in land-cover classification. Inter-Region versus Intra-Region SplitsĮven proper chipping can leave room for a softer kind of leakage. For example, if we follow the above data-collection approach and keep track of the extents our train and test sets cover (here, red and blue), we see the emergence of overlap:Ĭhips generated by grid sampling. Then, after you’ve downloaded a large collection of chips, you can make a train/test split by dividing them up.īut there’s a catch: though your saved chips will be unique, the area they cover might not. ![]() When manually chipping large regions, it might seem simplest to sample random windows with replacement: pick a small window within an overall bounding extent, grab the appropriate imagery, and repeat. In fact, using an API like Google’s Earth Engine or SentinelHub, we can do this quite easily (we'll be adding example code here). It's easy to see how this could happen: let's say we’re generating RGB imagery over San Francisco, pulling chips of 10m imagery from the Sentinel-2 satellite. The easiest to avoid we can call chip overlap: when you sample chips from overlapping extents (regions) in your train and test data. When creating train and test splits of chipped data, there are multiple ways to introduce data leakage. One common approach to pare these large acquisitions down into model-friendly sizes is called chipping: sample smaller windows from the regions of interest, either randomly or in a grid.Ĭhips of San Francisco sampled randomly, with replacement. For many geospatial data collections, a single region of interest (for example, San Francisco) might consist of millions of pixels. Unlike natural images, which are captured by photographers, geospatial imagery is typically collected by satellites, which image vast regions of the world at regular intervals. Generating a dataset for geospatial machine learning is significantly different from generating a dataset of natural images. Unfortunately there are many ways to induce data leakage, some of them sneakier than others. Data leakage will result in a model that at appears to generalize well during training, but later disappoints in production on new test data. (We can also create a "validation" set, which is not used for learning model parameters but can be used to search hyperparameters.) The key here is that the model must really never see the data before, or else we have data leakage.ĭata leakage means information about the test data leaks into the training process, opening the model up to memorization and/or overfitting. To measure generalization, we evaluate a trained model on a "test" set that the model has never been trained on. Learning without generalization is just memorizing the training data, which isn't useful for deploying models into production in the real world. When training machine learning models, we aim for generalization: we want our model to be capable of correctly processing data it hasn’t seen before (during training). In this post, we’ll focus on creating distinct train, val, and test splits, or more specifically the ways a well-meaning ML Engineer can accidentally introduce data leakage. At Masterful, we've encountered these challenges while w orking with companies focused on geospatial analytics, and we thought it would be helpful to share how we think through them. When moving from academic benchmarks to data “in the wild”-for example, training a model on a dataset you create yourself- curation transforms from an assumption to a responsibility.Ī variety of common ML mistakes emerge from skipping or bungling one of the components of data curation. Curation is the unsung hero of academic datasets: many common benchmarks in vision are class-balanced, well labeled, and feature distinct train and test sets.
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