diff --git a/gammapy/estimators/extension.py b/gammapy/estimators/extension.py
new file mode 100644
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+++ b/gammapy/estimators/extension.py
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+# Licensed under a 3-clause BSD style license - see LICENSE.rst
+import astropy.units as u
+from astropy.coordinates import Angle
+from gammapy.datasets import Datasets
+from gammapy.estimators.parameter import ParameterEstimator
+from gammapy.modeling import Fit
+from gammapy.utils.pbar import progress_bar
+
+
+class ExtensionEstimator(ParameterEstimator):
+    """Source extension estimator.
+
+    Estimates source size for a given list of datasets and a given model.
+
+    The size can be estimated in various energy intervals.
+
+    Parameters
+    ----------
+    energy_edges : `~astropy.units.Quantity`
+        Edges of the energy intervals where the extension is estimated.
+    source : str or int
+        For which source in the model to compute the extension.
+    size_min : float
+        Minimum value for the size used for the fit statistic profile evaluation.
+    size_max : float
+        Maximum value for the size used for the fit statistic profile evaluation.
+    size_n_values : int
+        Number of size values used for the fit statistic profile.
+    size_values : `~astropy.coordinates.Angle`
+        Array of size values to be used for the fit statistic profile.
+    n_sigma : int
+        Number of sigma to use for asymmetric error computation. Default is 1.
+    n_sigma_ul : int
+        Number of sigma to use for upper limit computation. Default is 2.
+    selection_optional : list of str
+        Which additional quantities to estimate. Available options are:
+
+            * "all": all the optional steps are executed
+            * "errn-errp": estimate asymmetric errors on size.
+            * "ul": estimate upper limits.
+            * "scan": estimate fit statistic profiles.
+
+        Default is None so the optional steps are not executed.
+    fit : `Fit`
+        Fit instance specifying the backend and fit options.
+    reoptimize : bool
+        Re-optimize other free model parameters. Default is True.
+    """
+
+    tag = "ExtensionEstimator"
+
+    def __init__(
+        self,
+        energy_edges=[1, 10] * u.TeV,
+        source=0,
+        size_min="0.0001 deg",
+        size_max="0.5 deg",
+        size_n_values=11,
+        size_values=None,
+        n_sigma=1,
+        n_sigma_ul=2,
+        selection_optional=None,
+        fit=None,
+        reoptimize=False,
+    ):
+        self.energy_edges = energy_edges
+
+        if fit is None:
+            fit = Fit(confidence_opts={"backend": "scipy"})
+
+        self.size_values = Angle(size_values) if size_values is not None else None
+        self.size_min = Angle(size_min)
+        self.size_max = Angle(size_max)
+        self.size_n_values = size_n_values
+        self.source = source
+        super().__init__(
+            null_value=1e-3,
+            n_sigma=n_sigma,
+            n_sigma_ul=n_sigma_ul,
+            selection_optional=selection_optional,
+            fit=fit,
+            reoptimize=reoptimize,
+        )
+
+    def run(self, datasets):
+        """Run."""
+        datasets = Datasets(datasets)
+        # find extension parameter
+        model = datasets.models[self.source].spatial_model
+
+        if hasattr(model, "sigma"):
+            self.size_parameter = model.sigma
+        elif hasattr(model, "r_0"):
+            self.size_parameter = model.r_0
+        elif hasattr(model, "radius"):
+            self.size_parameter = model.radius
+        else:
+            raise ValueError(f"Cannot find size parameter on model {self.source}")
+
+        rows = []
+
+        for energy_min, energy_max in progress_bar(
+            zip(self.energy_edges[:-1], self.energy_edges[1:]), desc="Energy bins"
+        ):
+            datasets_sliced = datasets.slice_by_energy(
+                energy_min=energy_min, energy_max=energy_max
+            )
+            datasets_sliced = Datasets(
+                [_.to_image(name=_.name) for _ in datasets_sliced]
+            )
+            datasets_sliced.models = datasets.models
+            row = self.estimate_size(datasets_sliced)
+            rows.append(row)
+        return rows
+
+    def estimate_size(self, datasets):
+        """Estimate size for a given energy range.
+
+        Parameters
+        ----------
+        datasets : `~gammapy.datasets.Datasets` or list of `~gammapy.datasets.MapDataset`
+            Map datasets.
+
+        Returns
+        -------
+        result : dict
+            Dict with results for the extension measurement.
+        """
+        datasets = Datasets(datasets)
+
+        if self.size_values:
+            self.size_parameter.scan_values = self.size_values.to_value(
+                self.size_parameter.unit
+            )
+        self.size_parameter.scan_min = self.size_min.to_value(self.size_parameter.unit)
+        self.size_parameter.scan_max = self.size_max.to_value(self.size_parameter.unit)
+        self.size_parameter.scan_n_values = self.size_n_values
+
+        result = super().run(datasets, self.size_parameter)
+        return result