Determining ground level elevation is essential in assessing flood risk. This report presents a com-prehensive framework using state-of-the-art object detection model YOLOv8 trained on a relatively small dataset containing houses, doors and steps. The object detection model is followed up by a post-processing block designed specifically for ground-level elevation assessment. The ground-level elevation is calculated by means of counting the number of steps leading up to the front door of a house. The final system has an precision of 0.908 for the detection of steps and an RMSE value of 1.19 steps per image, based on a test dataset containing 111 images. The report starts with a program of requirements, followed by an overview of the framework. Then a short evaluation of the state-of-the-art YOLOv8 object detection model and a describtion the met-
rics used are outlined. An extensive description of the post-processing block, with design choices substantiated by visualized results follows. The final results are presented, discussed and compared to the program of requirements. The report concludes by highlighting potential areas for further research and improvement, such as exploring larger and more diverse datasets, investigating alter- native object detection models, and refining the post-processing techniques. Overall, this study presents a robust framework for ground level elevation assessment, leverag-ing the capabilities of YOLOv8 and most importantly incorporating tailored post-processing tech-niques. The findings provide valuable insights for flood risk assessment and contribute to the broader field of geospatial analysis.