Advanced deep learning architectures for accurate detection of subsurface tile drainage pipes from remote sensing images-Reference-Cited by-同舟云学术

Advanced deep learning architectures for accurate detection of subsurface tile drainage pipes from remote sensing images

Published:2022-10-26 Issue: Volume: Page:
ISSN:
Container-title:Image and Signal Processing for Remote Sensing XXVIII
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Author:

Breitkopf Tom-Lukas,Hackel Leonard,Ravanbakhsh Mahdyar,Cooke Anne-Karin,Willkommen Sandra,Broda Stefan,Demir Begüm

Publisher

SPIE

Reference20 articles.

1. Predicting national-scale tile drainage discharge in Denmark using machine learning algorithms;Motarjemi;Journal of Hydrology: Regional Studies,2021

2. Mapping of agricultural subsurface drainage systems using unmanned aerial vehicle imagery and ground penetrating radar;Koganti;Sensors,2021

3. Simultaneous Assessment of Sources, Processes, and Factors Influencing Herbicide Losses to Surface Waters in a Small Agricultural Catchment

4. Simulating seasonal variations of tile drainage discharge in an agricultural catchment

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

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1. Systematic review of the detection of subsurface drainage systems in agricultural fields using remote sensing systems;Agricultural Water Management;2024-06

2. Detection of over-ground petroleum and gas pipelines from optical remote sensing images;Image and Signal Processing for Remote Sensing XXIX;2023-10-19

3. Subsurface drainage pipe detection using an ensemble learning approach and aerial images;Agricultural Water Management;2023-09