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 However, these maps are often outdated or not present.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.
翻译:地表下面的排水管道提供农艺、经济和环境效益,通过降低湿土的水位,改善植物根部的通风,最终提高农田的生产力,但也为地下水体提供了农用化学进入通道,增加了土壤的营养损失。为了维护和基础设施发展,需要绘制地表下排水管道位置和排水农业用地的准确地图。然而,这些地图往往已经过时或不存在。多年来应用了不同的遥感图像处理技术,在克服这些限制方面取得了不同程度的成功。最近,在深入学习技术(DL)方面,利用机械学习分解模型改进了传统技术。在本研究中,我们采用了两个基于DL的模型:i)改进了U-Net结构;和ii)视觉变异器的电解码器在排水管检测框架中的解码器。实验结果证实,与基本的U-Net结构相比,这两种模型在探测准确性方面的有效性。我们的代码和模型在https://gitut.berlinde/rsim/drainstrain-traction.