Accurate and detailed cropland maps are essential for agricultural planning, resource management, and food security, particularly in countries like China, where agricultural productivity is high but resources are limited. Despite the availability of several medium-to-high-resolution satellite-based cropland maps, significant discrepancies in area estimates and spatial distribution persist, limiting their utility. This study proposes a data-driven framework for cropland mapping that leverages 2 m High Resolution (HR) imagery from Mapbox and Google. The framework consists of three main stages: First, national imagery is partitioned into 0.05°×0.05° blocks for efficient parallel computation. An Image Quality Assessment (IQA) using ResNet models is performed on both sources to address the challenge of missing image acquisition metadata. Second, a robust cropland identification model integrates Mask2Former for precise segmentation and XGBoost for error evaluation, facilitating iterative improvements through active learning. Finally, a novel integration strategy combines four feature groups—Geography, IQA, Region Property, and Consistency—using XGBoost to merge the datasets into a unified cropland layer, named Croplayer. The Croplayer dataset achieves an overall mapping accuracy of 88.73 %, with 30 out of 32 provincial units reporting area estimates within ±10 % of official statistics. In contrast, only 1 to 9 provinces from seven other existing datasets meet the same accuracy standard. The results highlight Croplayer's potential for applications such as crop yield estimation and agricultural structure analysis, offering a reliable tool for addressing agricultural and food security challenges.