For storing and querying context from drone video, systems increasingly treat aerial streams as spatiotemporal data, where every frame or clip is anchored in both space and time so that questions like “what entered this corridor between 14:03 and 14:05” or “how did traffic density change along this road over the last ten minutes” can be answered directly from the catalog. Spatiotemporal data itself is commonly defined as information that couples geometry or location with timestamps, often represented as trajectories or time series of observations, and this notion underpins how drone imagery and detections are organized for later analysis. [sciencedirect](https://www.sciencedirect.com/topics/computer-science/spatiotemporal-data)

At the storage layer, one design pattern is a federated spatio‑temporal datastore that shards data along spatial tiles and time ranges and places replicas based on the content’s spatial and temporal properties, so nearby edge servers hold the footage and metadata relevant to their geographic vicinity. AerialDB, for example, targets mobile platforms such as drones and uses lightweight, content‑based addressing and replica placement over space and time, coupled with spatiotemporal feature indexing to scope queries to only those edge nodes whose shards intersect the requested region and interval. Within each edge, it relies on a time‑series engine like InfluxDB to execute rich predicates, which makes continuous queries over moving drones or evolving scenes feasible while avoiding a single centralized bottleneck. [sciencedirect](https://www.sciencedirect.com/science/article/abs/pii/S1574119225000987)

On top of these foundations, geospatial video analytics systems typically introduce a conceptual data model and a domain‑specific language that allow users to express workflows like “build tracks for vehicles in this polygon, filter by speed, then observe congestion patterns,” effectively turning raw video into queryable spatiotemporal events. One such system, Spatialyze, organizes processing around a build‑filter‑observe paradigm and treats videos shot with commodity hardware, with embedded GPS and time metadata, as sources for geospatial video streams whose frames, trajectories, and derived objects are cataloged for later retrieval and analysis. This kind of model makes it natural to join detections with the underlying video, so that a query over space and time can yield both aggregate statistics and the specific clips that support those statistics. [vldb](https://www.vldb.org/pvldb/vol17/p2136-kittivorawong.pdf)

To capture temporal context in a way that survives beyond per‑frame processing, many video understanding approaches structure the internal representation as sequences of graphs or “tubelets,” where nodes correspond to objects and edges encode spatial relations or temporal continuity across frames. In graph‑based retrieval, a long video can be represented as a sequence of graphs where objects, their locations, and their relations are stored so that constrained ranked retrieval can respect both spatial and temporal predicates in the query, returning segments whose object configurations and time extents best match the requested pattern. Similarly, described spatio‑temporal video detection frameworks introduce temporal queries alongside spatial ones, letting each tubelet query attend only to the features of its aligned time slice, which reinforces the notion that the catalog’s primary key is not just object identity but its evolution through time. [arxiv](https://arxiv.org/html/2407.05610v1)

Enterprise video platforms and agentic video analytics systems bring these ideas together by building an index that spans raw footage, extracted embeddings, and symbolic metadata, and then exposing semantic, spatial, and temporal search over the catalog. In such platforms, AI components ingest continuous video feeds, run object detectors and trackers, and incrementally construct indexes of events, embeddings, and timestamps so that queries over months of footage can be answered without rebuilding the entire index from scratch, while retrieval layers use vector databases keyed by multimodal embeddings to surface relevant clips for natural‑language queries, including wide aerial drone shots. These systems may store the original media in cloud object storage, maintain structured spatiotemporal metadata in specialized datastores, and overlay a semantic index that ties everything back to time ranges and geographic footprints, enabling both forensic review and real‑time spatial or temporal insights from aerial drone vision streams. [visionplatform](https://visionplatform.ai/video-analytics-agentic/)