ESA WorldCover 10 m: how much of the metro is built-up, water, cropland and green — every share a pixel count.
The finding
The 10-metre ESA WorldCover map classifies every pixel of Jakarta, Indonesia. The dominant class is Built-up at 84.4% of the 44,071 hectares read, with built-up land at 84.4% and open water at 0.9%.
The full breakdown
Every share below is a pixel count, not a planner’s estimate:
- Built-up — 84.4% (37,178 ha)
- Tree cover — 11.5% (5,085 ha)
- Grassland — 2.8% (1,237 ha)
- Permanent water — 0.9% (416 ha)
- Cropland — 0.2% (97 ha)
- Bare / sparse — 0.1% (46 ha)
A land-cover breakdown is the honest baseline for everything else. You cannot argue about encroachment, green cover or flood risk until you agree on what the ground actually is — and here, that agreement is a measurement rather than a negotiation.
How to read a land-cover map
Land cover is the vocabulary every other analysis borrows. Before you can argue about encroachment, green space, heat or flood risk, you have to agree on what the ground actually is — and a per-pixel classification is the least arguable way to settle it, because it is a count rather than an impression.
The shares are the point. A metro that is four-fifths built-up behaves nothing like one that is half cropland, and the difference shows up in temperature, runoff and air. Reading the class breakdown is how you replace a vague sense of a place with a measured profile of it.
Why the land-cover baseline matters
Almost every hard question about a city — is it overheating, is it flooding, is it swallowing its green space — reduces, underneath, to a question about land cover changing over time. Fixing an honest baseline is the unglamorous first step that makes all those later questions answerable rather than rhetorical.
Because the classification is a pixel count, the baseline is not up for negotiation. That is its quiet power: it replaces the argument about what a place is with a measurement of it.
Reading this alongside the other layers
Land cover is the layer every other analysis leans on. Read it with a surface-water history to see which of today’s built-up pixels were water within living memory, and with a flood-exposure screen to find the low ground that has been paved. The classification says what the ground is; the change layers say what it used to be.
How Dyaan measured this
This article was not written from a press release or a secondary summary. It was generated directly from a live read of the underlying satellite archive, performed at publication time by the same engine that powers the Dyaan workspace. There is no human in the loop inventing a number to fit a narrative — the narrative is assembled around numbers the machine measured.
The data source is ESA WorldCover 2021 v200. Dyaan does not download the whole archive; it issues ranged HTTP requests that pull only the bytes covering this bounding box, decodes the relevant raster tiles in memory, and counts the pixels that fall inside the area — 5,186,160 of them for this read, moving roughly 526 KB over the wire. No API key is used, and nothing is cached and then re-presented as fresh: a read either succeeds against the live archive or the article records that it could not.
Because the input is a fixed, published dataset and the area is a fixed bounding box, this analysis is reproducible. Anyone can open the same area in the Dyaan workspace, run the same analysis, and get the same figures — the definition of a measurement rather than an opinion. That reproducibility is the whole point: it is what separates an intelligence product you can audit from a chart you have to trust.
What this analysis is — and what it is not
This is a single-year snapshot at 10 m. It is excellent for shares and patterns, but one 10 m pixel can blend a narrow road, a thin canal or a tree line into whatever surrounds it.
Dyaan’s design rule is to state that footprint plainly rather than paper over it. A number here is only ever as good as the sensor and the method behind it, and both are named. Where the data cannot answer a question, the honest answer is that it cannot — and the tool will say so rather than fill the gap with something that merely looks authoritative.
Reproduce this yourself in about a minute
Every figure above is checkable, and checking it is the fastest way to trust it:
- Open the Dyaan workspace and sign in (the free tier is enough for this).
- Search for Jakarta, Indonesia, or draw a rectangle over the same area on the map.
- Choose the Land cover analysis from the panel and press Run.
- Read the headline numbers, open the table for the full breakdown, and export a CSV or PDF if you want to keep it.
You are not limited to the places Dyaan writes about. The same engine runs over any lake, city, forest or coastline on Earth — so if there is a place you actually care about, that is the one to point it at.
A short glossary
- Class — one of the land-cover categories — built-up, cropland, tree cover, water, and so on.
- Pixel — a 10 m × 10 m square of ground assigned to exactly one class.
- Hectare — 10,000 square metres.
Why this is written by a machine — and why that helps
This article was assembled automatically, and that is a feature rather than an apology. A person writing to a deadline is tempted to round a number toward the story they already have in mind; an engine has no story to protect. It reads the archive, counts what is there, and lays the figures out in the same structure every time, for every place, whether the result is dramatic or dull. The consistency is the point: you can compare this week’s reading with last month’s, or this city with the next, and know the only thing that changed is the ground, not the method or the mood of the author.
It also means the work scales in a way human analysis never could. There are millions of lakes, forests and floodplains on Earth and a handful of people who study any given one. An automated pipeline that reads public data honestly can, in principle, keep an eye on all of them — and surface the ones that are changing fastest to the people who would otherwise never hear about them until it was too late. That is the ambition behind these field notes: not to replace the expert, but to make sure the expert, the official and the ordinary resident all get to see the same measured record, at the same time, for free.
Every safeguard that makes the tool trustworthy is applied here too. The numbers are counted, never estimated; the limits are stated, never hidden; and if a read fails, the failure is recorded rather than papered over with a plausible-looking figure. An automated writer is only as honest as the rules it follows, and these are the rules.
The bottom line
This is an original Dyaan analysis. The words, the framing and the pictures are Dyaan’s own; the only thing borrowed is the raw public dataset it reads — ESA WorldCover 2021 v200 — which is an input, not an article. Nothing here is copied from another publication, and every number can be reproduced in the tool.
If a place matters to you — a lake near your home, a forest you grew up beside, a valley that floods every year — you do not have to wait for Dyaan to write about it. Point the tool at it yourself and read the record for that exact ground. Open the workspace → · How every number is measured →