Research on intelligent recognition methods for hydraulic engineering based on few-shot learning

Currently, the identification of features in remote sensing images primarily relies on deep learning methods. However, these methods require large volumes of high-quality training samples and tend to perform poorly in tasks such as water conservancy project recognition, where training samples are scarce. To address this issue, introducing machine learning techniques. Based on the analysis and mining of historical project samples, newly collected samples are matched against historical ones to enable the identification and classification of new engineering projects. Using examples such as reservoirs and rivers, this method was applied for feature recognition. The results demonstrate that the approach can effectively identify features such as rivers, lakes, and reservoirs, with an accuracy exceeding 95% for rivers and over 81% for reservoirs. Moreover, it operates with high computational speed and delivers reliable recognition accuracy.