Crop type classification in images

One or more non-transitory computer-readable storage media comprising a plurality of instructions to cause an apparatus, in response to execution by one or more processors of the apparatus, to:

• obtain a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period;
• predict one or more crop types growing in each of particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets;
• determine a crop type classification for each of the particular locations based on the predicted one or more crop types for the respective particular locations;
• and
• generate a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indications of the crop type classification determined for the respective particular locations;
• wherein to predict the one or more crop types growing in each of the particular locations comprises to apply the image set to one or more machine learning systems, wherein the one or more machine learning systems include a convolutional neural network (CNN);
• and
• wherein the one or more machine learning systems are configured to predict the one or more crop types rowing in each of the particular locations after supervised training on ground truth data.

A method comprising:

• obtaining a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period;
• predicting one or more crop types growing in each of particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets;
• determining a crop type classification for each of the particular locations based on the predicted one or more crop types for the respective particular locations;
• and
• generating a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indications of the crop type classification determined for the respective particular locations;
• wherein predicting the one or more crop types growing in each of the particular locations comprises applying the image set to one or more machine learning systems that include a convolutional neural network (CNN);
• and
• wherein the one or more machine learning systems are configured to predict the one or more crop types growing in each of the particular locations after supervised training on ground truth data.