Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

Goat farming plays an important economic role in numerous developing countries, with Africa being a home to a considerable portion of the global goat population. This study examined the socioeconomic determinants affecting goat herd size among smallholder farmers in Lephalale Local Municipality of the Limpopo Province in South Africa. A simple random sampling technique was used to select 61 participants. The socioeconomic characteristics of smallholder goat farmers in Lephalale Local Municipality were identified and described using descriptive statistics on one hand. On the other hand, a Multiple linear regression model was employed to analyse the socioeconomic determinants affecting smallholder goat farmers’ herd sizes. Findings from the Multiple linear regression model highlighted several key determinants, including the age of the farmer, gender of the farmer, education level, and marital status of farmers, along with determinants like distance to the markets, provision of feed supplements, and access to veterinary services. Understanding these determinants is crucial for policymakers and practitioners to develop targeted strategies aimed at promoting sustainable goat farming practices and improving the livelihoods of smallholder farmers in the region.