  To cope with the high requirements during the computation of semantic
segmentations of earth observation imagery, current state-of-the-art pipelines
divide the corresponding data into smaller images. Existing methods and
benchmark datasets oftentimes rely on pixel-based tiling schemes or on
geo-tiling schemes employed by web mapping applications. The selection of
subimages (comprising size, location and orientation) is crucial. It affects
the available context information of each pixel, defines the number of tiles
during training, and influences the degree of information degradation while
down- and up-sampling the tile contents to the size required by the
segmentation model. We propose a new segmentation pipeline for earth
observation imagery relying on a tiling scheme that creates geo-tiles based on
the geo-information of the raster data. This approach exhibits several
beneficial properties compared to pixel-based or common web mapping approaches.
The proposed tiling scheme shows flexible customization properties regarding
tile granularity, tile stride and image boundary alignment. This allows us to
perform a tile specific data augmentation during training and a substitution of
pixel predictions with limited context information using data of overlapping
tiles during inference. The generated tiles show a consistent spatial tile
extent w.r.t. heterogeneous sensors, varying recording distances and different
latitudes. We demonstrate how the proposed tiling system allows to improve the
results of current state-of-the-art semantic segmentation models. To foster
future research we make the source code publicly available.
