Parcels represent land units that define property boundaries and play a crucial role in basemap creation and land management. Traditionally, parcel mapping relied on highly accurate surveying methods, which are often costly and time-intensive. Increasingly, high-resolution imagery is being utilized for parcel delineation, and deep learning models can automate and accelerate this process.

While residential parcels often have visible boundaries, legally recognized parcels may not always be marked. Therefore, this model generates plausible approximations of parcel boundaries rather than exact lines. The results can be used to create basemaps that can later be refined through manual editing.