Aquaculture Pond Precise Detection and Monitoring for Spacial Planning Using Deep Learning and Remote Sensing

This innovation introduces a comprehensive, automated system for detecting, classifying, and monitoring coastal aquaculture ponds using a hybrid approach that integrates remote sensing, deep learning, and machine learning techniques. The methodology leverages Sentinel-2 multispectral imagery and combines spectral indices, semantic segmentation, and shape-based classification to achieve high-precision aquaculture mapping at regional scales.

At the core of the system is the Normalized Difference Water Index (NDWI), which provides an efficient initial separation of water bodies from surrounding land features. Building on this, a DeepLabv3 semantic segmentation model is employed to accurately delineate aquaculture pond boundaries, capturing complex shapes and varying pond sizes through atrous convolutions and multi-scale contextual analysis. To enhance classification reliability, extracted geometric and morphological features—such as area, perimeter, centroid, hull, and circularity—are processed using a Random Forest classifier to distinguish aquaculture ponds from natural water bodies and to identify filled and unfilled ponds.

Beyond static mapping, the innovation supports spatiotemporal change detection by analyzing multi-decadal satellite data, enabling the quantification of pond expansion, abandonment, and transformation. The system also incorporates Temporal Convolutional Networks (TCNs) to estimate pond age and longevity, offering insights into aquaculture lifecycle patterns and long-term sustainability.

Overall, this innovation provides a scalable, high-accuracy, and data-driven solution for coastal aquaculture monitoring. It significantly reduces manual effort, improves temporal consistency, and delivers actionable spatial intelligence for policymakers, environmental managers, and marine planners working toward sustainable aquaculture development.