Geospatial technology has never been more capable. Satellites capture data and images of the entire Earth daily. AI models can detect land cover changes, predict flood damage, and identify objects in aerial imagery with remarkable accuracy. However, one problem persists: the act of getting these systems to talk to each other for an optimized decision-making.
Interoperability is the ability of different software, platforms, and data formats to work together seamlessly and it remains one of the most consequential bottlenecks in the industry. It slows workflows, inflates costs, limits collaboration, and delays the delivery of insights to the people who need them most.
A Fragmented Ecosystem
The geospatial world is built on decades of accumulated tools and proprietary platforms. Esri's ArcGIS dominates enterprise GIS. QGIS leads in open source. Cloud platforms like Google Earth Engine and Microsoft Planetary Computer offer their own services. Drone manufacturers and lidar vendors bundle proprietary formats. Each environment has real strengths, but moving data between them requires constant conversion, projection reconciliation, and quality checks at every handoff.
Organizations often end up maintaining parallel workflows, duplicating data, or limiting their analyses to whatever fits within a single platform's walls.
Standards Exist, So Why Doesn't Interoperability?
The Open Geospatial Consortium has spent decades developing open standards with genuine progress that’s been made, but adoption is uneven. Newer data types like point clouds and real-time sensor feeds have outpaced the open standards, and proprietary formats rush to fill the gaps. There's also a commercial dimension: platform lock-in is, for many vendors, is a feature rather than a bug.
The Human Cost
The consequences aren't just technical. A climate scientist combining NOAA oceanographic data, land use records, census demographics, and field observations may be working across five different formats before any analysis begins. For under-resourced organizations doing climate resilience or environmental justice work, this friction has real stakes: time spent wrangling data is time not spent with communities.
Reasons for Optimism
Cloud-native formats represent meaningful progress. Major data providers including NASA, USGS, and Microsoft now publish STAC-compliant catalogs, making cross-archive discovery increasingly practical. However, STAC is a metadata standard; it doesn't solve incompatible processing environments or vector-raster integration.
What Would Actually Help
A few approaches show real promise: stronger government mandates for open-format data delivery in publicly funded projects; greater institutional investment in translation infrastructure; and better low-code tooling that abstracts format complexity for non-specialists without hiding it entirely.
The Bottom Line
Interoperability is time consuming work, and it doesn't generate headlines the way a new satellite or AI breakthrough does. But it's the connective tissue that determines whether all that capability can actually be put to use by the climate scientist, the emergency manager, or the community organizer trying to understand what rising seas mean for their neighborhood. Until the industry treats interoperability as a foundational requirement rather than a nice-to-have, the gap between what the technology can do and what organizations can actually accomplish will remain wider than it needs to be.
