Everyone’s talking about a tech shift due to the AI revolution. Lately, geography is right at the centre of it. Google Earth’s newest AI tool can read archives of satellite images, spot patterns, and even make forecasts. This is only the start. While it shows up first in satellite images, it will soon touch many parts of daily life.
Why? Because location is powerful. Once a location goes into fast, automated analysis, it can reveal a lot, like it or not. People assume satellite data will be the only source of GEOAI. In truth, there’s more. Every time we follow a map, share our whereabouts, or order food through an app, we create trails of location data. When a system puts those trails together, they can help government, business, and public services make quicker, sharper decisions.
The good part is, automation can clear dull, repetitive work and save time. They can suggest likely spatial patterns and show how places change over time. They can smooth traffic, monitor pollution, water bodies and forests, and support better forecasts for rain and temperature. They can even help track and predict the spread of diseases. Used well, that is a strong win for human society.
But there’s a catch. The same strength, knowing where everything is, can become a privacy headache. Your phone checks in; your car’s navigation system reports back. Soon a system can infer where you live, work, eat, protest, and pray. That is not just “sharing a location” – to me, that is sharing your life.
Then there is bias. We know automated methods learn from old training datasets, and if the past is not fair. Unfair outcomes will be repeated by them if they are given an unfair history. Add the “black box” problem: a model gives an answer, but not a reason. If a system guides who receives disaster relief first, or which neighbourhood gets new roads, “because the model said so” will not do.
This shift will also reshape jobs. For years, geospatial companies and communities have created, cleaned, stored, and managed basic geospatial data. A lot of that pipeline can be automated. The biggest squeeze will fall on roles built around basic, repetitive skills. Many educational institutions in India teaching geography and geospatial still train mainly for those tasks. If machines take them over, where do those graduates go? The gap between industry and academia could widen as tools speed ahead and campuses race to catch up.
So, what should we do? First, set rules. Build privacy by design: collect less, protect more, and make opt-outs simple. Keep stakeholders in the loop for high-stakes choices. Document sources, steps, and behaviour so tough questions get straight answers. Create feedback channels for communities affected by geospatial decisions.
Second, update the curriculum quickly. Teach privacy, fairness testing, and open explanations. Go beyond button-click work to core skills: spatial thinking, statistics, coding, geo-computation, and importantly, the ethical use of GEOAI. Train students to ask: Who is missing from this data? Who might be harmed? Can we explain this result in plain words?
Is policy intervention required? Absolutely, establish uniform standards for testing fairness of geospatial data, conduct impact checks, and ensure accountability.
As the matter is a bit serious, nearly 80% of everyday activities have a location link. The stakes are everywhere: health, transport, housing, farming, climate, policing, disaster response. The promise is huge; the risks are real. If we invest in capacity building, set firm guardrails, and treat location as the sensitive asset it is, we can take the best of this wave - without losing ourselves in it!