Coastal aquaculture plays an important role in the provision of seafood, the sustainable development of regional and global economy, and the protection of coastal ecosystems. Inappropriate planning of disordered and intensive coastal aquaculture may cause serious environmental problems and socioeconomic losses. Precise delineation and classification of different kinds of aquaculture areas are vital for coastal management. It is difficult to extract coastal aquaculture areas using the conventional spectrum, shape, or texture information. Here, we proposed an object-based method combining multi-scale segmentation and object-based neighbor features to delineate existing coastal aquaculture areas. We adopted the multi-scale segmentation to generate semantically meaningful image objects for different land cover classes, and then utilized the object-based neighbor features for classification. Our results show that the proposed approach effectively identified different types of coastal aquaculture areas, with 96% overall accuracy. It also performed much better than other conventional methods (e.g., single-scale based classification with conventional features) with higher classification accuracy. Our results also suggest that the multi-scale segmentation and neighbor features can obviously improve the classification performance for the extraction of cage culture areas and raft culture areas, respectively. Our developed approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.Environmental Protection Law, overall marine functional zonation, and nature reserve schemes, have been formulated for the management of coastal areas. However, comprehensive coastal management in China is still a big challenge. Thus, the monitoring and management of coastal aquaculture are imperative to ensure sustainable development of marine aquaculture industry.Remote sensing technology has substantially improved our ability to observe remote or inaccessible areas at a fraction of the cost of traditional surveys [9]. Mapping and monitoring of aquaculture facilities provide decision-makers with important baseline data on production, cultivated area boundaries, and environmental impacts [10,11]. Remote sensing can provide consistent and wide-range monitoring using various sensors to support aquaculture management [12][13][14], which means the mapping of aquaculture facilities can be performed accurately and periodically at selected scales.Previous studies have used visual interpretation, spatial structure enhanced analysis, and object based image analysis (OBIA) to extract coastal aquaculture areas from remotely sensed imagery. Although visual interpretation can achieve the highest accuracy, it takes a lot of time and effort. Thus, it is less used presently. Spatial structure enhancement techniques, such as neighborhood and texture analysis, are the most commonly adopted methods in the classification process [15,16]. OBIA has become a widely used method in the past decades, especially for the classification o...