**2. Material**

#### *2.1. Test Sites*

A total of 100 sites were selected to test the reconstruction method (see Figure 1), following a similar approach as in AI4Boundaries [34]. In this parallel research project on mapping crop field boundaries, a random stratified sampling method was designed to extract image chips from various landscapes. The sample was drawn from six European countries for which public parcel data are available: Austria, Spain, France, Netherlands, Slovenia, and Sweden. Each test site covers 256 by 256 pixels, corresponding to 2560 by 2560 m. This is still sufficiently large to contain some contextual information for visual interpretation and can be processed on a computer with relatively low memory constraints (2 GB). The majority of pixels was vegetated land, the land cover in focus for this study. Both agricultural land (57% of all pixels) and forest (27%) were represented. Agricultural land included non-irrigated arable land (27%), pastures (11%), and a mosaic of small cultivated land parcels with different cultivation types (13%). Non-irrigated arable land is defined as cultivated land parcels under rainfed agricultural use for annually harvested non-permanent crops, normally under a crop rotation system, including fallow lands within such crop rotation [35]. Forested pixels were either broad-leaved forest (12%), coniferous forest (13%), or a mixture of both (2%). The remaining non-vegetated pixels included artificial surface and water bodies.

**Figure 1.** Test sample: 100 sites of 2560 by 2560 m distributed over six European countries (Austria, Spain, France, Netherlands, Slovenia, and Sweden).
