Aerial drone vision systems only become truly intelligent once they can remember what they have seen—across frames, across flight paths, and across missions. That memory almost always takes the form of some kind of catalog or spatio‑temporal storage layer, and although research papers rarely call it a “catalog” explicitly, the underlying idea appears repeatedly in the literature: a structured repository that preserves spatial features, temporal dependencies, and scene‑level relationships so that analytics queries can operate not just on a single frame, but on evolving context.

One of the clearest examples of this comes from TCTrack, which demonstrates how temporal context can be stored and reused to improve aerial tracking. Instead of treating each frame independently, TCTrack maintains a temporal memory through temporally adaptive convolution and an adaptive temporal transformer, both of which explicitly encode information from previous frames and feed it back into the current prediction arXiv.org. Although the paper frames this as a tracking architecture, the underlying mechanism is effectively a temporal feature store: a rolling catalog of past spatial features and similarity maps that allows the system to answer queries like “where has this object moved over the last N frames?” or “how does the current appearance differ from earlier observations?”

A similar pattern appears in spatio‑temporal correlation networks for UAV video detection. Zhou and colleagues propose an STC network that mines temporal context through cross‑view information exchange, selectively aggregating features from other frames to enrich the representation of the current one Springer. Their approach avoids naïve frame stacking and instead builds a lightweight temporal store that captures motion cues and cross‑frame consistency. In practice, this functions like a temporal catalog: a structured buffer of features that can be queried by the detector to refine predictions, enabling analytics that depend on motion patterns, persistence, or temporal anomalies.

At a higher level of abstraction, THYME introduces a full scene‑graph‑based representation for aerial video, explicitly modeling multi‑scale spatial context and long‑range temporal dependencies through hierarchical aggregation and cyclic refinement arXiv.org. The resulting structure—a Temporal Hierarchical Cyclic Scene Graph—is effectively a rich spatio‑temporal database. Every object, interaction, and spatial relation is stored as a node or edge, and temporal refinement ensures that the graph remains coherent across frames. This kind of representation is precisely what a drone analytics framework needs when answering queries such as “how did vehicle density evolve across this parking lot over the last five minutes?” or “which objects interacted with this construction zone during the flight?” The scene graph becomes the catalog, and the temporal refinement loop becomes the indexing mechanism.

Even in architectures focused on drone‑to‑drone detection, such as TransVisDrone, the same principle appears. The model uses CSPDarkNet‑53 to extract spatial features and VideoSwin to learn spatio‑temporal dependencies, effectively maintaining a latent temporal store that captures motion and appearance changes across frames arXiv.org arXiv.org. Although the paper emphasizes detection performance, the underlying mechanism is again a temporal feature catalog that supports queries requiring continuity—detecting fast‑moving drones, resolving occlusions, or distinguishing between transient noise and persistent objects.

Across these works, the pattern is unmistakable: effective drone video sensing requires a structured memory that preserves spatial and temporal context. Whether implemented as temporal convolutional buffers, cross‑frame correlation stores, hierarchical scene graphs, or transformer‑based temporal embeddings, these mechanisms serve the same purpose as a catalog in a database system. They allow analytics frameworks to treat drone video not as isolated frames but as a coherent spatio‑temporal dataset—one that can be queried for trends, trajectories, interactions, and long‑range dependencies. In a cloud‑hosted analytics pipeline, this catalog becomes the backbone of higher‑level reasoning, enabling everything from anomaly detection to mission‑level summarization to agentic retrieval over time‑indexed visual data.