Evidence in support of selective sampling of aerial drone imagery  

One of the tenets1 of the platform2 we propose to analyze drone imagery favors selective sampling as opposed to repeated processing of every aerial drone image frame captured by the drone or UAV swarm. With the ability to decouple on-board computing and sensor capabilities from cloud analytics and yet providing feedback into the control loop for the drone/swarm, this selective sampling must be shown to have theoretical underpinnings. Related work in this field, indeed has demonstrated that. We cite only a few. 

1. In “Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling,” three large-scale experimental case studies demonstrate that cost-effective selective sampling can reduce average modeling costs by up to 85%, while retaining about 92% of model accuracy. 

For two-parameter models: Using less than half the measurements (11/25), they achieved 82% of models within ±5% accuracy; using all measurements gave 93% within ±5%. Sampling saves up to 87% cost. 

For more complex (four-parameter) models: Using just 17 points instead of 625, they achieved ~60% accuracy within ±20% error at less than 3% cost. More samples improved accuracy, but the return diminished as cost increased. 

2. The experiment “Repeated Random Sampling for Minimizing the Time-to-Accuracy” found that a method called RS2 (Repeated Sampling of Random Subsets) reached 66% test accuracy on ImageNet with only 10% of the data per epoch, compared to 69% for full dataset—thus reducing compute cost and training time by more than fourfold, with only a 3% accuracy loss in the reported benchmark. Competing data pruning/design methods suffered notably greater accuracy drops at similar cost reductions. 

3. The paper “Deep Learning with Importance Sampling” shows, with experiments on CIFAR10 and CIFAR100, that focused sampling can lower computational losses by an order of magnitude, while only decreasing accuracy between 5% and 17% relative to uniform sampling. Importance sampling helps maintain high accuracy versus naive selective/removal methods, especially for deep learning 

4. The thesis “Autonomous UAV Swarms: Distributed Microservices, Heterogeneous Swarms, and Zoom Maneuvers” shows that selective sampling reduced energy consumption by 50% and increased the accuracy of field metrics extrapolation even when only 40% of image data was processed, showing that cloud microservices can efficiently handle limited, targeted workloads. This method dramatically lessens computational demand and cloud service costs, as unneeded data (e.g., images similar to prior frames or background clutter) can be filtered out before cloud upload and analysis 

5. The paper “Drone swarm strategy for the detection and tracking of occluded targets” finds that selective analysis of images (rather than contiguous sequential frame ingestion) can be performed in centralized cloud systems by selectively offloading images and telemetry and taking advantage of bandwidth and compute savings. 

6. The paper “Network optimization by regional computing for UAVs' big data"  showed that for a UAV Swarm processing 200-2000 drones’ data, cloud computing costs ranged from $0.52 to $5.36 per task batch with intermediary regional processing included and negligible on-board computing costs but the latter is not scalable. Onboard processing time increased from 7.34 ms up to 73.4 ms as load grew, limiting big data utility. Cloud processing time stayed consistently low (0.05–0.07 ms) but at the expense of higher network delay and cost. 

 

These studies indicate the following cost-vs-accuracy trade-offs. 

Method	Dataset & Parameters	Cost Reduction	Accuracy Retained
Sparse sampling	Synthetic, 2 params	87%	82% (±5%)
Sparse sampling	Synthetic, 4 params	>95%	60% (±20%)
RS2 random sampling	ImageNet, ResNet-18	90%	96% of baseline
Importance sampling	CIFAR10, CIFAR100	83–90%	83–95%

For any scientific experiments including analysis of contiguous aerial drone imagery samples, the typical Accuracy vs. Cost Trends are such that with 

• Moderate sampling (10–40% of data): there is small accuracy drop (1–4%) but large compute savings. 

• Aggressive sampling (<10% of data): Accuracy may drop 10–40%, but cost plummets, useful for rapid prototyping. 

• Sophisticated sampling (importance/randomized methods): Delivers best accuracy/cost tradeoff, especially for high-dimensional models. 

The paper DeepBrain: Experimental Evaluation of Cloud-Based Computation offloading and Edge Computing in the Internet-of-Drones (PMC, 2020) has studied the change in accuracy when offloading deep-learning based detection to the cloud and also evaluates trade-offs between energy consumption, bandwidth uses, latency and throughput. The findings include much higher throughput (frames/sec) in cloud versus onboard computing even when communication delays and bandwidth bottlenecks increased in their experiments with the number of drones streaming video to the cloud and image compression or resolution reduction was introduced. 

And building a knowledge graph or catalog of drone world objects eases detection and tracking of objects across frames. As the paper “Cataloging public objects using aerial and street-level images” has shown, a CNN based model can accurately detect and classify trees and combined with geolocation data builds a dataset that can be used for querying. This approach supports comprehensive analytics by organizing detected objects spatially and semantically. The use of knowledge graph over the catalog of detected objects takes this a step further to enable better semantic understanding and global context that conventional image-only models cannot provide which improves small-object detection and reduces false positives.