An approach for identifying historic village using deep learning

Abstract

This paper aims to propose an approach to automatically identify historic villages from remote sensing images based on deep learning algorithm and accurately calculate the villages’ geographical coordinates. Experimental datasets of Conghua, a typical region in fast development that retains many historic villages, are designated for training and testing. Comparison experiments of two recognition models, image classification and object detection, are designed to obtain the most suitable identification algorithm. GIS platform is adopted to visualize the distribution of the historic villages. The results show that first, the recognition accuracy of the image classification algorithm is 90.79%. However, visualization of test results shows the identified area is not a village but a surrounding. Second, the recognition accuracy of an object detection algorithm can reach 95.61%, which indicates that the algorithm is accurate and efficient. Third, by using the Historical-Modern tag as a filter, a village with a certain proportion of historic features according to specific requirements may be discriminated. Finally, 1531 historic villages in Conghua area were identified by the preferred algorithm, and their spatial locations were marked. This research will extend the detection of remote sensing image targets of deep learning algorithms from single buildings to group patterns and complex ground objects, so as to promote the integration of heritage conservation and artificial intelligence research. This time-efficiency approach can provide strong support for the discovery and field investigation of historic villages facing fast development and provide a scientific basis for the formulation of conservation policies.Article HighlightsDeep learning is applied to the protection of the cultural heritage of historic villages.Comparative experiments of different algorithms are designed to analyse their applicability in historic village recognition. A recognition rate of up to 95.61% is achieved.The visualization of recognition results is important for understanding the relationship between historic villages and nature, and historic village conservation.