This document discusses using deep learning techniques like convolutional neural networks and generative adversarial networks to detect field boundaries from remote sensing imagery. Accurate field boundary detection can provide valuable information for agricultural applications. However, remote sensing data presents unique challenges due to differences in spatial resolution, number of spectral bands, and semantic content compared to typical computer vision datasets. The authors propose using data augmentation and a UNet architecture trained on satellite imagery to perform semantic segmentation and detect field boundaries, achieving promising results. They suggest expanding the approach to handle multiple land cover classes and different image resolutions.

1. DEEP LEARNING TECHNIQUES FOR BOUNDARIES DETECTION
Hana Kubickova (Plan4All), Jan Horak, Ondrej Kaas

2. MOTIVATION
Accurate information on field boundaries = valuable input
for agricultural applications:
• Crop monitoring
• Crop management – irrigation managemnt, fertilizer inputs, pesticide
application..
• Yeild prediction
• Harvest time
• Crop future investments
• Food security
• Land use and land cover classification

3. Field boundary detection techniques
▪ Supervised classification: Support Vector Machine
(SVM), Random Forest (RF)
▪ Edge-based methods: Sobel, Canny operator, Prewit
▪ Region-based methods: multi-resolutional segmentation
(MRS), Watershed segmentation (WS)
▪ Hybrid methods: Canny edge detection + Watershed
segmentation (CEWS)

4. Current Emphasis on Artificial Neural Networks
▪ Convolutional neural networks
▪ Recurrent neural networks
▪ Generative adversarial networks

5. Current Emphasis on Artificial Neural Networks
▪ Convolutional neural networks
▪ Recurrent neural networks
▪ Generative adversarial networks

6. Remote Sensing Specifics
▪ A very large amount of training
data needed
▪ Computer vision image archives
cannot be used in Remote
Sensing due to:
▪ Different spatial resolution
▪ A much higher number of
specrtal bands
▪ Different semantic content
Example of ImageNet dataset
https://www.researchgate.net/figure/Examples-in-the-ImageNet-
dataset_fig7_314646236

7. Not enough data? Let’s augument!
● Method of dataset extension by modification of
existing data using transformation, deformation, shift
of color spectrum etc.
● Practical Use: imgaug library
https://medium.com/@mcr222/data-augmentation-benchmark-for-deep-learning-2db712c6eb3e
horizontally
inverted, shifted,
added noise
horizontally inverted,
contrast changed
…

8. Main Challenges with Satellite data
Atmospheric
Corrections
Bands combination
Upscale/Downscale
Bands
Clouds
Geometric
tranformations

9. METHODOLOGY – „Proof of Concept“
Training Image Training field boundaries Pretrained Model
Input Image Pretrained Model Output boundaries

10. OBJECT DETECTION X SEMANTIC SEGMENTATION
X INSTANCE SEGMENTATION

11. UNet Architecture

12. Results
Evaluation of 52 test tiles that were excluded from the
training dataset:
● IoU coefficient: 0.923

13. Further Ideas / Improvements
● Different classes (water, crop, forests, …)
● Different resolution, multi temporal satellite images
● Instance segmentation (Mask R-CNN)
An excellent opportunity to test in Dubrovnik
INSPIRE Hackathon!