Overview of Remote Sensing Monitoring Applications Based on YOLOv12

This article explores in depth the innovative application of YOLOv12 in the field of remote sensing monitoring. The opening elaborates on the importance of remote sensing monitoring in various industries and the limitations of traditional methods, highlighting the urgent need for efficient intelligent algorithms. Subsequently, the YOLOv12 model was elaborated in detail, including its innovative designs such as region attention module, residual efficient layer aggregation network, and architecture optimization. These designs give the model significant advantages in accuracy and speed compared to traditional object detection models. Through the application case analysis of practical remote sensing scenarios such as land use monitoring, crop growth assessment, and disaster monitoring, YOLOv12 has demonstrated its outstanding performance in accurately identifying different types of land cover, monitoring crop pests and diseases, estimating yield, and quickly assessing the scope and extent of disasters and losses. At the same time, the challenges faced by the model in processing large amounts of high-resolution image data, adapting to complex terrain features, and addressing differences in data from different remote sensing platforms were discussed. The future development direction of combining multi-source data fusion, transfer learning, and active learning technologies was proposed, aiming to provide theoretical and practical basis for promoting the intelligent and accurate development of remote sensing monitoring technology.