Abstract: Rapid extraction and precise identification of freshwater aquaculture ponds can play an important role in the management and decision-making of the aquaculture industry. Satellite remote sensing can be expected to provide the critical extraction of freshwater aquaculture ponds, due to its rapid, timely and effective way of Earth observation. However, it is still elusive to delineate the surface area and the variation in the aquaculture ponds from the satellite remote sensing images. In this study, an improved classification was introduced to delineate the changes in aquaculture ponds from Landsat remote sensing images using the coordinate attention with the U-Net neural network model. Firstly, the dataset was collected from the freshwater aquaculture ponds in the Gaochun district of Nanjing City, Jiangsu Province, China. Landsat remote sensing images were also captured from 1985 to 2021, and supplemented by GF-1, GF-2 satellite data and field survey data. Secondly, a coordinate attention model with U-Net as the backbone was created to fully extract the spatial information of features, where the information remained on the fragmented aquaculture ponds. Finally, the long-term Landsat images were selected to explore the temporal and spatial changes of freshwater aquaculture ponds. Six state-of-the-art models were also utilized to verify the improved model. The experimental results show that the performance of the improved model outperformed the rest, in terms of extraction accuracies. Furthermore, there was a dramatic variation in the surface area and distribution of freshwater aquaculture ponds in the study period. Specifically, the surface areas of aquaculture ponds increased from 0.48 to 36.92 km² in the early stages from 1985 to 2000, with an annual increase of 2.43 km². There was rapid growth in the second stage from 2000 to 2017. Among them, the surface areas of aquaculture ponds increased from 36.92 to 234.47 km², with an annual increase of 11.62 km². A gradual shrinkage was found in the surface areas of freshwater aquaculture ponds from 234.47 to 209.58 km² in the third stage from 2017 to 2021, with an annual decrease of 6.22 km². Moreover, the social and economic factors can be attributed to the main driving factors for the dramatic variation in the surface area of the aquaculture areas. In summary, the high overall accuracies of the improved model were achieved to rapidly extract the aquaculture ponds. Additionally, the food demand and economic value can be the significant driving factors for the rapid changes in the aquaculture ponds, while the policies and human activities can be the key factors for the area changes of the aquaculture ponds. The finding can also provide technical support for the scientific management and decision-making of the aquaculture industry.