Processing large-scale remote sensing imagery requires excessive time and memory resources. Reducing these bottlenecks allows for real-time object recognition and segmentation.

Inconsistent spectral signatures and temporal variability lead to misidentification of crops and land boundaries. Improving classification accuracy ensures reliable resource management and yield forecasting.

Standard mapping techniques fail to distinguish between complex, overlapping social-ecological land uses in dense urban environments. Improving resolution and category differentiation allows for more precise urban planning and resource allocation.