Improving Flood Detection Accuracy in Satellite Imagery Using Apache Sedona and Error Analysis

This research introduces a novel approach that combines the use of Apache Sedona, a distributed platform for geospatial data processing, to efficiently analyze and correct errors in flood detection models. The study focuses on systematically identifying and addressing the main sources of inaccuracies in flood damage detection, leading to targeted model optimization and enhanced precision.