Where Mapflow ran in 2026
👉 Explore the interactive map
Mapflow is a self-serve platform. People sign up, draw a box on the map, pick a model, and get their result — usually without ever talking to us. That's the point. Some talk to Agent as the product drifts with the user intraction patterns. But we are always curious to know where the highest demand is. And as we love maps we visualised the Mapflow processings on the interactive map as an aggregated data, highlighting the demand.
To keep the picture unbiased, we removed largest enterprise customers. What is left is the depesonalised shape of ordinary customer demand in the scope of 2026.
6,521 people ran 18,745 processings over 44,433 km² of ground in 178 countries. That's roughly the land area of Estonia, mapped one box at a time by users who mostly never spoke to us.
Every hotspot matters
Every hotspot is one small patch of the planet where somebody asked Mapflow to map. Bigger hotspot, more jobs.
Every AOI ordered on Mapflow in 2026. Dot size = number of jobs in that cell.
Two things jump out immediately. The first is how urban the pattern is. This isn't a map of countries, it's a map of cities and their edges — Java's north coast, the Ganges plain, the Pearl River Delta, the Nile... The 🏠 Buildings mapping model remains the highest demanded one.
The second is the long thin tail. 178 countries had at least one processing this year. Most of them had a handful. That's a lot of people quietly trying the platform on their own patch of the world.
Group the same data by country and it sharpens up:
Processings per country, 2026. Grey hatching = no processings this year.
You can switch between number of jobs, average area per job and total ground covered, flip to the raw density view, and sort the full 178-country table.
Three countries are almost half the platform
Indonesia, India and China together account for 44% of all Mapflow processings in 2026. Indonesia alone is nearly 16%. What's outstanding about this country that this is not only the top traffic but also the only one where the use of uploaded imagery outperforms imagery basemaps and connected source: 50.4% of all Indonesian AOIs and 42% of Indonesian total area came from uploaded imagery.
Then comes the surprise: Italy and Spain sit at #4 and #5, ahead of Brazil and the US. Two mid-sized European countries, punching far above their population, land area and — frankly — our hopes to gain traction in the biggest market.
But busy is not the same as big
Job counts only tell half the story. The other half is what's the cost of the job, or the average area which is not the same but correlates well.
The median country orders about 2.3 km² per job. Around that median, the platform splits into clearly different behaviours:
- Italy (0.67 km²) and Spain (0.41 km²) — huge job counts, tiny areas. These look like people mapping a village, a solar farm, a construction site, a single neighbourhood. Lots of small, precise, repeated work, probably, related to the use of drone's imagery which reveals at a high resolution data but small areas.
- Morocco (5.9 km²), Russia (3.8 km²), Senegal (3.4 km²) — far fewer jobs, but each one covers a serious chunk of ground. These look like regional mapping projects.
- India (2.7 km²) manages both: the second-highest job count and an above-median area per job. It's the only place in the top five doing high volume at scale.
"Mapflow is popular in Italy for the mapping on drone's imagery" and "Mapflow does a lot of work for the regional mapping projects in Morocco" are both true, and they describe quite different workflows in the same product. That's genuinely useful to know when deciding what to build next — a faster small-AOI path and a cheaper large-AOI path are not the same engineering and business problems.
How this map was made
Nothing here is a survey or an estimate of the market. It's just our own processing log, that we conduct to improve the product. But a few decisions in this analysis worth spelling out.
We used the shape people drew, not the country they registered from. A user's billing address tells you where they live; the box they drew tells you where they work. Those are often the same countries, but not always, and the second one is the interesting one.
Each area was reduced to a single point. Comparing thousands of full polygons against 255 country outlines is slow and, for this question, pointless — an AOI is small enough that one representative point inside it lands in the right country essentially always.
We snapped those points onto a coarse grid before counting. Our reporting connection is a read-only analytics view without any geographic functions available, so instead of asking the database "which country is this in?", we grouped the areas into grid cells — roughly 3 to 50 km across, depending on latitude — and did the country matching afterwards, offline, against the world boundary layer. Coastal cells that fall just offshore get snapped to the nearest coastline within about 100 km, which is why harbours, deltas and small islands still land in the right place.
If your country is somewhere in that long tail and you'd like to see more of it covered, tell us what you're mapping. The distribution above is, more than anything, a record of what people asked for — and it's how we decide what to work on next.