# Optimizing Geospatial Data for ML/CV Applications:
## 5 Discussion  
The results of the comparative study clearly demonstrate the efficiency of the proposed optimization method of orthoimagery images across different resolutions and road types. By applying a combination of filing, masking, and trimming processes, significant reductions in file size were achieved. The utilization of the parcel geometry preserved the essential information required for geospatial analysis and ML/CV applications. The optimization process led to substantial reductions in average file sizes for both highway and road at all tested resolutions. For highways, total reduction percentages ranged from 56% at 25 cm resolution to 79% at 10 cm resolution. For roads, reductions exceeded 90% across all resolutions. The greatest individual file size reduction was 207.75 MB, where an original image of 218.16 MB was reduced by 95% to 10.41 MB. Although the proposed optimization method successfully reduced file sizes, several limitations need to be acknowledged. Firstly, the quality of input data from public sources, such as Geoportal and LP-Portal, can vary greatly in terms of resolution and accuracy. This variability can affect the consistency of results, especially in regions where access to high-quality data is limited. Secondly, the method's dependence on public APIs introduces a potential limitation, where changes in API structures, data availability, or access policies could compromise the methodology's robustness. Future work could address this by integrating alternative data sources or developing methods to standardize data quality. The differences in reduction percentages between highways and roads can be attributed to their spatial characteristics. Highways are generally more linear, resulting in images where the area of interest occupies a larger proportion of the total image area. Roads in urban settings often have more complex geometries with surrounding areas that are unnecessary from the perspective of the dataset. This makes optimization more effective for roads due to the higher proportions of irrelevant data. Figure 13: File size optimization—original file (top, 769 KB); preprocessed file with irrelevant areas removed (bottom, 261 KB). The reduction in file sizes improves both storage and computational efficiency. By minimizing storage requirements, the optimized dataset reduces the memory and processing power needed for data handling, leading to more effective geospatial data management and distribution. The optimized images maintain the spatial information critical for high-precision analysis, ensuring suitability for applications like feature extraction, object detection, and classification. However, the optimization's effectiveness depends on the accuracy of the input geometries. The contextual data outside the defined regions may sometimes be important. Despite this, the optimized datasets hold substantial promise for improving ML and CV applications by accelerating model training, reducing computational costs, and enhancing data transfer efficiency. Future work will focus on evaluating the impact of optimized datasets on model performance, particularly in improving efficiency and accuracy in Machine Learning and Computer Vision applications. A significant part of this exploration involves comparing models trained on optimized datasets versus those using unoptimized datasets. Key metrics such as training time, model accuracy, and resource utilization will be assessed to demonstrate the benefits of the optimization process. Additionally, quantifying the influence of data quality variations from public sources on the results remains a critical area for future investigation. Variations in resolution and accuracy can significantly affect segmentation precision and optimization outcomes, emphasizing the importance of robust methodologies to address these inconsistencies. Furthermore, extending this method to other geospatial features beyond road networks, such as rivers, vegetation, or urban infrastructure, presents both opportunities and challenges.