PARCS: A Deployment-Oriented AI System for Robust Parcel-Level Cropland Segmentation of Satellite Images

Abstract

Cropland segmentation of satellite images is an essential basis for crop area and yield estimation tasks in the remote sensing and computer vision interdisciplinary community. Instead of common pixel-level segmentation results with salt- and-pepper effects, a parcel-level output conforming to human recognition is required according to the clients' needs during the model deployment. However, leveraging CNN-based models requires fine-grained parcel-level labels, which is an unacceptable annotation burden. To cure these practical pain points, in this paper, we present PARCS, a holistic deployment-oriented AI system for PARcel-level Cropland Segmentation. By consolidating multi-disciplinary knowledge, PARCS has two algorithm branches. The first branch performs pixel-level crop segmentation by learning from limited labeled pixel samples with an active learning strategy to avoid parcel-level annotation costs. The second branch aims at generating the parcel regions without a learning procedure. The final parcel-level segmentation result is achieved by integrating the outputs of these two branches in tandem. The robust effectiveness of PARCS is demonstrated by its outstanding performance on public and in-house datasets (an overall accuracy of 85.3% and an mIoU of 61.7% on the public PASTIS dataset, and an mIoU of 65.16% on the in-house dataset). We also include subjective feedback from clients and discuss the lessons learned from deployment.