# Optimizing Geospatial Data for ML/CV Applications:
## 1 Introduction  
The rapid growth of Machine Learning (ML) and Computer Vision (CV) applications has led to an increased demand for large-scale, high-quality geospatial datasets [1, 2]. High-resolution datasets are utilized in various domains, including autonomous driving, urban planning, environmental monitoring, or disaster management [3, 4, 5, 6]. In these applications, precise data are crucial for accurate decision-making and effective system performance [7, 8]. However, managing massive image datasets is often computationally intensive and time-consuming, posing significant challenges for both researchers and developers [9, 10, 11]. In recent years, the field of geoinformatics has seen significant advancements. The increasing availability of high-resolution satellite imagery through platforms like Google Earth and Sentinel Hub enables users to obtain geospatial information tailored to their specific needs [12, 13]. Public data repositories from agencies such as NASA and the European Space Agency (ESA) have promoted unprecedented access to detailed satellite images [12]. Additionally, governmental agencies in many countries provide high-resolution orthoimagery data through public portals, allowing for even more localized and detailed analysis [14, 15]. High-resolution images have been employed in various applications ranging from environmental monitoring and advanced urban planning to agricultural management [5, 16, 17]. Valuable publicly available datasets have been utilized in monitoring deforestation, urban expansion, and global climate change impacts [18; 19; 20]. The ability to capture fine details in orthoimagery is critical for accurately assessing and managing both natural and built environments [4; 21; 22]. Despite these advancements, road image datasets often suffer from several limitations that hinder their effective utilization in ML and CV tasks. A significant issue is the inclusion of large amounts of irrelevant data [23; 24]. Areas outside the primary region of interest, such as unrelated surroundings beyond the road boundaries, can result in excessive storage requirements and significant computational resource usage [23], as presented in Figure 1. Redundant imagery further exacerbates these problems. Unoptimized datasets pose challenges when scaling up ML models for real-world applications, as the noise introduced by irrelevant data can degrade model performance and increase training time [25; 26]. Choosing the right data source is very important in geospatial analysis, especially when high precision and reliability are required. Several options are available, each with distinct advantages and limitations. Existing global datasets, such as those provided by OpenStreetMap (OSM), offer extensive coverage of road networks worldwide. OSM is a collaborative project that relies on crowdsourced data from around the globe [28]. While it provides a valuable resource for general mapping purposes, the quality and accuracy of its data can vary significantly [29]. Road attributes in OSM may differ in detail and precision, particularly in regions where local contributions are less extensive or detailed [30]. This variability presents challenges for high-precision applications, which require consistent and accurate road information [31]. Moreover, platforms like Google Earth offer comprehensive road network data and satellite imagery, but their data are often proprietary and not freely available for detailed analysis or integration into custom datasets [32; 33]. These limitations restrict researchers and developers from fully utilizing these resources for localized applications that demand high-resolutions and highly specific data [32]. The integration of high-resolution satellite imagery with existing geographic and cadastral data can significantly enhance the accuracy of spatial analyses and mapping projects [34; 35].