InsightPager was founded in 2018 with a mission to advance the understanding and use of street-level imagery for geospatial intelligence. Our principal investor and lead researcher recognized a critical gap: while satellite imagery research had accelerated rapidly, fundamental research into dynamic, high-frequency street-level geospatial data remained almost entirely unexplored. We established InsightPager to address this need — focusing on the foundational science of extracting, aligning, and reasoning over ground-level visual information at scale.

Today, our research operates at Technology Readiness Levels (TRL) 1–2, concentrating on early-stage frameworks for automating the retrieval of street-view imagery from online videos, cross-view matching with satellite basemaps, pixel-accurate street-level change detection, and domain-adapted super-resolution for ground imagery. By building methods that link raw street-view data to structured, temporally rich maps, we aim to enable continuous, scalable monitoring of urban environments, infrastructure, and environmental change.

While our current focus is fundamental R&D, we are actively designing commercialization pathways. As our technologies mature, we aim to transition from research into deployable systems that deliver trusted, real-time street-level geospatial insights. InsightPager’s long-term vision is to transform the way ground environments are observed and understood — creating a new foundation for urban planning, disaster response, environmental monitoring, and AI training.