Convert Raster to Vector in QGIS: 5 Methods
To convert a raster to vector polygons in QGIS, open Raster > Conversion > Polygonize (Raster to Vector), pick your band, name the output field, and run it. You get one polygon per connected group of pixels sharing a value, with that value in the attribute table. It is the right answer for a classified raster and the wrong one for a photograph. Four other methods cover the rasters polygonize cannot read.
, open Method 5 is a plugin we build, so weigh that section accordingly. It gets the same five lines as the other four.
Open the raster's properties and read two things: the band type and the number of distinct values. They pick the method for you.
| Your input | Method | What it costs |
|---|---|---|
| Classified raster (land cover, a slope reclass, a mask) | Method 1, Raster > Conversion > Polygonize | Free, in core QGIS |
| Continuous raster (NDVI, slope, a reflectance band) | Method 2, Reclassify by table, then polygonize | Free, in core QGIS |
| DEM or any elevation surface | Method 3, Raster > Extraction > Contour, or Contour Polygons | Free, in core QGIS |
| Scanned paper map or a plan | Method 4, GRASS r.thin plus r.to.vect | Free, and slow. Paid AI tracing exists |
| Orthophoto, drone or satellite imagery | Method 5, a segmentation model | Free plugins need an NVIDIA card. Cloud plugins have free tiers, then a subscription |
Every menu path, algorithm id and default below was checked against the QGIS 3.44 documentation and the GDAL stable docs on 23 August 2026. Menu paths assume the default QGIS layout, which a plugin can change.
Method 1: Polygonize
Work in a projected CRS. Two steps below take a distance or an area in layer units, and in degrees those numbers mean nothing.
The algorithm is Polygonize (raster to vector), id gdal:polygonize, wrapping the GDAL utility gdal_polygonize.
Open the tool
Raster > Conversion > Polygonize (Raster to Vector), or the Processing Toolbox under GDAL, Raster conversion.
Pick the band
Band 1 unless your classes live elsewhere. One band only, so reduce an RGB raster to a single class band.
Name the field
DN is the default. Rename it, landcover_code rather than DN.
Decide on 8-connectedness
Off by default, so pixels must share a full border to join. Turn it on and diagonally touching pixels join too. Left off, a thin diagonal feature splits into a chain of polygons.
Save to a real file
The default is a temporary layer. A large raster gives tens of thousands of features, so write straight to a GeoPackage.
![The QGIS Polygonize dialog: Land cover 10 m [EPSG:32631], Band 1 (Palette), field landcover_code, 8-connectedness unticked, output to a GeoPackage.](/_next/image?url=%2Fblog%2Fraster-to-vector-qgis%2F02_polygonize_dialog.jpg&w=3840&q=75)
Two things save you time. First, set a NoData value on the class you do not want: GDAL then reads the band's validity mask and skips those pixels instead of wrapping one enormous background polygon around everything. That is the most common complaint about polygonize output, and it is a raster property, not a tool setting.
Second, watch the band type. gdal:polygonize always writes an integer field, so it casts a float band on the way in, and that fails in two ways depending on how the raster stores its values. Neither warns you. A Float32 band goes through Method 2 first.
The staircase, and the four steps that fix it
Polygonize traces pixel boundaries, so a diagonal field edge comes back as right-angled steps, one per pixel. A 10 m Sentinel classification gives 10 m stairs.
That is correct output, and no setting inside polygonize changes it. The fix is four vector geometry algorithms, in this order.
Dissolve by the class field
native:dissolve, on the class field you just created. Run it first, while the geometry is exact: merging simplified edges leaves slivers along every shared boundary. Tick Keep disjoint features separate for one row per patch instead of one multipart feature per class.
Filter by area
native:extractbyexpression, in Vector selection, with $area > 500 in layer CRS units. On a 10 m classification, 500 m2 is five pixels, about where speckle stops. The figure below used 2,000 m2, because at 500 the surviving slivers crossed each other once simplified. Read the histogram of your own areas. For solid patches, run native:deleteholes after this step, with 0.0 to remove every hole.
Simplify
native:simplifygeometries. Tolerance defaults to 1.0 in layer units. Set it to roughly one pixel and the stairs collapse into straight runs, the boundary moving less than a reader can see. The method dropdown offers Douglas-Peucker (distance based), Visvalingam (area based) and snap to grid. Visvalingam holds small features better at a high tolerance.
Smooth, but only on curves
native:smoothgeometry, on genuinely curved things: streams, coastlines, vegetation patches. Offset defaults to 0.25, iterations control how round it gets. Set Maximum node angle to smooth to 90 and sharper corners stay sharp, which saves the corners of buildings and parcels.
Use it when: the raster holds classes as integers.
Skip it when: the band is a float, a photograph or elevation.
Method 2: Reclassify, then polygonize
Polygonize on a continuous raster fails in one of two ways, depending on how the values are stored.
Stored as scaled whole numbers, which is how NDVI products ship, you get a polygon for nearly every pixel. I measured 39,801 polygons out of 40,000 pixels on a Sentinel-2 scene.
Stored as raw floats between -1 and 1, the cast to integer flattens every value to -1, 0 or 1 before the trace starts. The same scene came back as 238 polygons in two classes. That looks clean, but the values are gone.
Slope, elevation, temperature and a raw reflectance band all do the same. The tool traces boundaries between integer values, and a continuous raster has none worth tracing. So make the classes yourself, with Reclassify by table, id native:reclassifybytable.
Open Reclassify by table
Processing Toolbox, Raster analysis. The continuous raster is the input.
Type the class table
Three columns: minimum, maximum, new value. It accepts -inf, inf and nan for NoData. A vegetation mask is three rows.
| Minimum | Maximum | Value |
|---|---|---|
| -inf | 0.2 | 1 |
| 0.2 | 0.5 | 2 |
| 0.5 | inf | 3 |
Set the output data type
In the advanced parameters, Output data type defaults to Float32. Set it to Byte or Int16 and polygonize behaves.
Check the range boundaries
Range boundaries defaults to min < value <= max, which decides where a pixel at exactly 0.2 lands. Pick what your class definition means.
Polygonize the result
Run Method 1 on the reclassified raster, then the four cleanup steps.
Use it when: you can say in numbers where one class stops and the next starts.
Skip it when: the thing you want is an object rather than a value range.
Method 3: Contours from a DEM
For elevation, do not polygonize. Use Contour at Raster > Extraction > Contour, id gdal:contour.
Open Contour
Raster > Extraction > Contour, with the DEM as input.
Set the interval
Defaults to 10.0 in raster units, so a metric DEM gets a line every 10 m.
Name the attribute
Elevation goes into ELEV by default. Keep it, and label from it.
Decide on Produce 3D vector
Tick it for the height on every vertex. Leave it off for a printed map, since a lot of software drops the Z.
Switch to contour polygons for filled bands
The sibling algorithm gdal:contour_polygon gives filled bands between two elevations, with ELEV_MIN and ELEV_MAX on each polygon. That is the one for a hypsometric map or an inundation extent, and it saves polygonizing a reclassified DEM by hand.
Contours from a lidar DEM come out noisy, because the surface is noisy. Smooth the raster before the contour step, never the lines afterwards: smoothed lines cross each other.
Use it when: the thing you want is a level: elevation, depth, rainfall, concentration.
Skip it when: the raster already holds classes.
Method 4: Scanned paper maps
If you came from ArcGIS looking for the QGIS equivalent of ArcScan, there is not one. Interactive raster tracing, where you click a line on a scan and the tool follows it, is a real gap in QGIS, and I would rather say so than have you hunt the menus.
The free path runs through GRASS, which ships with most QGIS installers.
Threshold the scan
Turn the scanned sheet into a binary raster: line work in one value, paper in the other.
Thin the lines
r.thin reduces each stroke to a single pixel wide.
Vectorize
r.to.vect with feature type line. The GRASS documentation warns that this creates excessive nodes at junctions and dangling spurs, so budget for cleanup.
Two other routes exist. Potrace and AutoTrace run outside QGIS: bitmap in, vector out, then you georeference the result. They are good on high-contrast scans and blind to geography.
Inside QGIS, Bunting Labs AI Vectorizer (QGIS repository listing) autocompletes a line as you digitize, aimed at geologic maps, as-builts and plans. It sends raster chunks to their servers, there is no free tier beyond a trial, and published prices start at 29 dollars a month, read 23 August 2026.
For a handful of features, hand-digitizing with snapping still wins. I would not build a tracing workflow for fewer than about fifty features.
Use it when: the scan carries clean line work and hundreds of features.
Skip it when: the sheet is faint, hatched or annotated over the lines.
Method 5: A segmentation model for photographs
A segmentation model has learned what the objects look like, so it finds them in a photograph where no pixel value separates them. We build one of these, AI Segmentation. This section is about our own tool.
Point polygonize at the same orthophoto and you get noise. A red roof and a red car have similar pixel values, and so do a grey roof and a grey road. The classes exist in your head, not in the file.
Install from the plugin manager
No GPU, no Python environment, no model weights to fetch.
Create an account
It needs one. Free allowance, then 39 euros a month excluding VAT.
Pick a mode
Both cloud modes send imagery to a server. The small local model stays on your machine.
Run it over your extent
Back comes a polygon layer in the project CRS, with label, class, score, area_m2 and perimeter_m on every feature.
Filter the output
Raw model output needs filtering the way polygonize output needs cleaning. On one run over central Paris I kept 82 polygons out of 635, and those 82 held 84 percent of the built area.
Five other plugins do this job, ours in the last row. Costs and free tiers read 23 August 2026.
| Tool | What you install | GPU | Account | Imagery leaves your machine | What comes out | Cost |
|---|---|---|---|---|---|---|
| Deepness (source) | Plugin manager, then its Python packages and an ONNX model | Optional, CPU works | No | No | Polygon layer, bounding boxes, or a raster | Free |
| Geo-SAM | Plugin manager, then dependencies and model weights from its settings dialog | Optional, clicking runs on CPU | No | No | Polygon layer, pixel-level or simplified | Free |
| samgeo | A Python package in your environment, no QGIS panel | Recommended | No | No | GeoJSON, Shapefile or GeoPackage you load yourself | Free |
| Mapflow (site) | Plugin manager | No | Yes | Yes, your GeoTIFF upload or the tile service you point at | Polygon layer | Credits, paid |
| Bunting Labs AI Vectorizer | Plugin manager | No | Yes | Yes, in raster chunks | Vertices into the layer you are digitizing | Trial, then 29 dollars a month |
| AI Segmentation (ours) | Plugin manager | No | Yes | Yes in both cloud modes, no for the small local model | Polygon layer in the project CRS, with label, class, score, area_m2 and perimeter_m | Free allowance, then 39 euros a month excluding VAT |
One limit each.
- Deepness runs the model you hand it, so you find or train one first.
- Geo-SAM takes clicks and boxes, one object at a time.
- samgeo has no QGIS interface. You write Python.
- Mapflow ships a fixed catalogue: buildings, forest, roads and construction sites, so it cannot find an object nobody trained it for.
- Bunting Labs AI Vectorizer follows line work as you digitize. It does not find every object in a zone.
- AI Segmentation, ours, sends imagery to a server in both cloud modes. Only the small local model stays local.
Use it when: the input is a photograph and there are more objects than you would draw by hand.
Skip it when: the imagery cannot leave your machine, which points at Deepness or Geo-SAM.
Try it free in QGIS, no card needed
What to remember
| What the properties say | Method |
|---|---|
| Integer band, under a hundred distinct values | Method 1, polygonize, then dissolve, filter, simplify |
| Float band, thousands of distinct values | Method 2, reclassify by table, then Method 1 |
| Elevation in metres, or any smooth surface | Method 3, contour or contour polygons |
| A scanned sheet with no class values at all | Method 4, GRASS, or digitize by hand |
| Three bands of a photograph | Method 5, a segmentation model |
The distinct-value count settles it. Under a hundred, somebody has already classified the raster, and polygonize traces those classes cleanly.
In the thousands, nobody has classified anything, so you draw the thresholds yourself in Method 2 or hand the job to a model in Method 5.
The staircase is correct output, and no setting inside polygonize removes it. Dissolve, an area filter, simplify and smooth do, in that order.
Every cleanup step moves vertices, so compute any area that feeds a report from the raster rather than from the polygons.
Polygonize cannot read a photograph at any setting, because the classes are in your head and not in the file.
Questions people ask
Why does polygonize give me one giant polygon around everything?
The background class has no NoData value set, so GDAL traces it as real data. Set NoData on that class in the raster's properties, then run Method 1 again.
Why did polygonize create a polygon for almost every pixel?
The raster is continuous, not classified, so almost no two neighbouring pixels hold the same value. Reclassify it first: Method 2.
How do I get rid of the staircase edges?
Run the four cleanup algorithms in Method 1: dissolve, area filter, simplify, smooth. Compute areas from the raster if they feed a report.
Does QGIS have an ArcScan equivalent?
No. Core QGIS has no interactive raster tracing. Method 4 has the free path, GRASS r.thin then r.to.vect, and the paid one, the Bunting Labs plugin from 29 dollars a month.
Can I polygonize an aerial photo or a satellite image?
No, and the output will look like static, since a red roof and a red car share pixel values. Use a segmentation model: Method 5.
Is raster to vector free in QGIS?
Yes for Methods 1 to 3: polygonize, reclassify, contour and the cleanup algorithms are all core QGIS. Only the photograph case and paid scan tracing cost money.
The building footprints guide walks the filtering step end to end, and the SAM in QGIS post covers which objects these models find well. The QGIS AI hub compares the plugins side by side. If all you need is buildings, our free building footprints tool pulls them from open data in the browser, with no plugin.
