CropSight: Towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and PlanetScope satellite imagery

Abstract

Crop type maps are essential in informing agricultural policy decisions by providing crucial data on the specific crops cultivated in given regions. The generation of crop type maps usually involves the collection of ground truth data of various crop species, which can be challenging at large scales. As an alternative to conventional field observations, street view images offer a valuable and extensive resource for gathering large-scale crop type ground truth through imaging the crops cultivated in the roadside agricultural fields. Yet our ability to systematically retrieve crop type labels at large scales from street view images in an operational fashion is still limited. The crop type retrieval is usually at the pixel level with uncertainty seldom considered. In our study, we develop a novel deep learning-based CropSight modeling framework to retrieve the object-based crop type ground truth by synthesizing Google Street View (GSV) and PlanetScope satellite images. CropSight comprises three key components: (1) A large-scale operational cropland field-view imagery collection method is devised to systematically acquire representative geotagged cropland field-view images of various crop types across regions in an operational manner; (2) UncertainFusionNet, a novel Bayesian convolutional neural network, is developed to retrieve high-quality crop type labels from collected field-view images with uncertainty quantified; (3) Segmentation Anything Model (SAM) is fine-tuned and employed to delineate the cropland boundary tailored to each collected field-view image with its coordinate as the point prompt using the PlanetScope satellite imagery. With four agricultural dominated regions in the US as study areas, CropSight consistently shows high accuracy in retrieving crop type labels of multiple dominated crop species (overall accuracy around 97 %) and in delineating corresponding cropland boundaries (F1 score around 92 %). UncertainFusionNet outperforms the benchmark models (i.e., ResNet-50 and Vision Transformer) for crop type image classification, showing an improvement in overall accuracy of 2–8 %. The fine-tuned SAM surpasses the performance of Mask-RCNN and the base SAM in cropland boundary delineation, achieving a 4–12 % increase in F1 score. The further comparison with the benchmark crop type product (i.e., cropland data layer (CDL)) indicates that CropSight is a promising alternative to crop type mapping products for providing high-quality, object-based crop type ground truth of diverse crop species at large scales. CropSight holds considerable promise to extrapolate over space and time for operationalizing large-scale object-based crop type ground truth retrieval in a near-real-time manner.