Detecting subsurface drainage pipes using a fully convolutional network with optical images

Song, Homin; Woo, Dong Kook; Yan, Qina

doi:10.1016/j.agwat.2021.106791

Cited by 10 publications

(21 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Fig. 1, the soil above the drainage pipes appears brighter after a precipitation event, thus producing distinct features visible at the surface [2,5].…”

Section: Introductionmentioning

confidence: 96%

“…1. The pipes are roughly ten centimeters in diameter and installed at about one meter below surface [5]. Precipitation (at t 0 ) infiltrates rapidly through the soil above the drainage pipes (t 1 and t 2 ) due to preferential flow through especially macropores, leading to relatively low (close to drainage pipes) and relatively high (farther away from the pipes) soil moisture.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1: Pictorial representation of drainage pipes and the impact on soil color on the surface [5].…”

Section: Introductionmentioning

confidence: 99%

“…edge detection, decision tree classification and image differencing [7]) have been applied to remote sensing images of bare soils, obtained after precipitation events [8]. Machine learning methods in particular DL-based methods have recently shown good performance on drainage pipe detection from remote sensing images [5,9,10]. As an example, in [5] a DL-based method is introduced using the deep autoencoder-decoder U-Net architecture [11] to detect drainage pipe from remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methods in particular DL-based methods have recently shown good performance on drainage pipe detection from remote sensing images [5,9,10]. As an example, in [5] a DL-based method is introduced using the deep autoencoder-decoder U-Net architecture [11] to detect drainage pipe from remote sensing images. The U-Net architecture outperforms other remote sensing image-based approaches using edge detection.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images

Breitkopf,

Hackel,

Ravanbakhsh

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…As shown in Fig. 1, the soil above the drainage pipes appears brighter after a precipitation event, thus producing distinct features visible at the surface [2,5].…”

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

“…Figure 1: Pictorial representation of drainage pipes and the impact on soil color on the surface [5].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images

Breitkopf,

Hackel,

Ravanbakhsh

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Machine‐learning model to delineate sub‐surface agricultural drainage from satellite imagery

Redoloza,

Williamson,

Headman

et al. 2023

J of Env Quality

View full text Add to dashboard Cite

Knowing subsurface drainage (tile‐drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile‐drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water‐quality as a function of climate variability or conservation management. We trained a UNet machine‐learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile‐drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile‐drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio‐River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil‐water content and bare fields enabled the best model performance. Each area of interest was heavily tile‐drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%–96% and balanced accuracy of 52%–54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.

show abstract

Contactless estimation of soil moisture using leaky Rayleigh waves and a fully convolutional network

Lee,

Woo,

Choi

2023

Vadose Zone Journal

Self Cite

View full text Add to dashboard Cite

Soil moisture is a key factor that influences various aspects of ecosystem functioning. Measuring soil moisture without installing any objects in the soil is desirable because it allows for accurate characterizations of soil moisture while minimizing impacts on soil structure and ecology. In this study, we explored the potential of leaky Rayleigh waves as a proxy to contactlessly estimate soil moisture. We developed an ultrasonic system containing a transducer, receivers, and acoustic barrier. The specimens of sand, silt, and clay were utilized. Experiments were conducted over 4 months. We used a widely used soil‐embedded moisture sensor to compare and develop relationships between leaky Rayleigh waves and soil moisture. Our results showed that as soil moisture increased, the velocity and amplitude of leaky Rayleigh waves decreased because water molecules attracted to the soils led to their attenuation. However, their magnitudes were not considerable except for very dry soils. To overcome these limited relations to estimate soil moisture from leaky Rayleigh waves, we constructed authentic images based on the observed leaky Rayleigh waves and used them as inputs for a fully convolutional network. We found that the combination of the ultrasonic system and deep learning approach developed in this study were suitable for estimating soil moisture without soil disturbances (RMSE = 0.01 m3 m−3). This study suggests that leaky Rayleigh waves have the potential to serve as a reliable proxy for determining soil moisture without the need for physical contact.

show abstract

Detecting subsurface drainage pipes using a fully convolutional network with optical images

Cited by 10 publications

References 27 publications

Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images

Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images

Machine‐learning model to delineate sub‐surface agricultural drainage from satellite imagery

Contactless estimation of soil moisture using leaky Rayleigh waves and a fully convolutional network

Contact Info

Product

Resources

About