  Subsurface tile drainage pipes provide agronomic, economic and environmental
benefits. By lowering the water table of wet soils, they improve the aeration
of plant roots and ultimately increase the productivity of farmland. They do
however also provide an entryway of agrochemicals into subsurface water bodies
and increase nutrition loss in soils. For maintenance and infrastructural
development, accurate maps of tile drainage pipe locations and drained
agricultural land are needed. However, these maps are often outdated or not
present. Different remote sensing (RS) image processing techniques have been
applied over the years with varying degrees of success to overcome these
restrictions. Recent developments in deep learning (DL) techniques improve upon
the conventional techniques with machine learning segmentation models. In this
study, we introduce two DL-based models: i) improved U-Net architecture; and
ii) Visual Transformer-based encoder-decoder in the framework of tile drainage
pipe detection. Experimental results confirm the effectiveness of both models
in terms of detection accuracy when compared to a basic U-Net architecture. Our
code and models are publicly available at
https://git.tu-berlin.de/rsim/drainage-pipes-detection.
