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Interactive visual analytics over distributed systems housing voluminous datasets is hindered by three main factors - disk and network I/O, and data processing overhead. Requests over geospatial data are prone to erratic query load and hotspots due to users’ simultaneous interest over a small sub-domain of the overall data space at a time. Interactive analytics in a distributed setting is further hindered in cases of voluminous datasets with large/high-dimensional data objects, such as multi-spectral satellite imagery. The size of the data objects prohibits efficient caching mechanisms that could significantly reduce response latencies. Additionally, extracting information from these large data objects incurs significant data processing overheads and they often entail resource-intensive computational methods. Here, we present our framework, ARGUS, that extracts low- dimensional representation (embeddings) of high-dimensional satellite images during ingestion and houses them in the cache for use in model-driven analysis relating to wildfire detection. These embeddings are versatile and are used to perform model- based extraction of analytical information for a set of dif- ferent scenarios, to reduce the high computational costs that are involved with typical transformations over high-dimensional datasets. The models for each such analytical process are trained in a distributed manner in a connected, multi-task learning fashion, along with the encoder network that generates the original embeddings.