Edge-ANN: Storage-Efficient Edge-Based Remote Sensing Feature Retrieval

Meeting strict latency budgets for high-performance approximate nearest neighbor (ANN) search remains a critical challenge in remote sensing edge devices, including microsatellites and UAVs, because of stringent limitations in both primary (RAM) and secondary (disk) storage. To address this challenge, we propose Edge-ANN, an efficient ANN framework specifically engineered for storage-efficient edge retrieval. Instead of explicitly storing high-dimensional partitioning hyperplanes, Edge-ANN uses pairs of in-data-set points, termed anchors, together with a scalar offset to define partitions implicitly. To ensure that these implicit partitions remain balanced and effective, we introduce a binary anchor optimization algorithm. Rigorous experiments on four multi-source and multi-modal data sets, Million-AID, High-resolution Urban Complex, GlobalUrbanNet, and BigEarthNet, demonstrated that under simulated edge environments with dual storage constraints, Edge-ANN achieves a 30% to 40% reduction in secondary storage compared with an unconstrained Annoy baseline, at the cost of only a 3% to 9% reduction in Recall@10. Furthermore, within constrained memory budgets, Edge-ANN delivers superior retrieval performance to other mainstream ANN methods under the adopted near-real-time latency budget. Collectively, these results establish Edge-ANN as a practical solution for enabling large-scale, high-performance remote sensing feature retrieval on edge devices with exceptionally constrained storage. The codes of Edge-ANN are available at https://github.com/huaijiao666/Edge-ANN