This paper presents a comprehensive exploration of determining optimal pixel resolutions for object detection in satellite imagery through a class-specific approach. Object detection in satellite imagery, critical for applications such as urban planning, environmental monitoring, and military surveillance, poses unique challenges due to high image resolutions, small object sizes, and computational demands. We propose a reusable pipeline designed to automate the discovery of the “knee point” on resolution-performance curves, achieving a balance between detection accuracy and computational efficiency. The pipeline integrates modules for data preprocessing, model fine-tuning, performance evaluation, and automated report generation. Utilizing the xView1 dataset and the YOLOv8 object detection model, we systematically analyze resolution images across 48 moveable object classes. Our findings show that lower-resolution images can yield competitive performance, significantly reducing resource demands, especially among object classes that perform well at high resolutions. This bridges existing research gaps while emphasizing modularity, efficiency, and usability. On average, across all object classes considered in this pipeline run the designated knee point presented a 57% reduction in pixel data with an 80% retention of the highest detection performance achieved at any resolution. Further, if we narrow our scope down to the top 15 performing classes, we find that the designated knee point presents a 71% reduction in pixel data, enabling a ground coverage area 3.4 times larger than what is achievable at the highest resolution, while retaining 88% of the detection performance and maintaining the same image dimensions and hardware capabilities.