Agricultural Field Boundary Detection through Integration of "Simple Non-Iterative Clustering (SNIC) Super Pixels" and "Canny Edge Detection Method"
Artughrul Gayibov
TL;DR
The paper tackles the challenge of delineating agricultural field boundaries in satellite imagery, where spectral similarity and fragmented plots complicate segmentation. It proposes a cloud-based workflow on Google Earth Engine that fuses SNIC-based superpixels applied to NDVI with Canny edge detection, using Sentinel-2 data to produce accurate boundaries at 10 m resolution. Key contributions include tuned SNIC (seed spacing $15$, compactness $0.5$) and Canny (lower $1.5$, upper $3.0$, sigma $1.0$) parameters, a 3-pixel morphological closing, and validation against OpenStreetMap using IoU and F1 metrics, demonstrated over Azerbaijan regions. The approach offers a scalable, robust method for producing precise field boundary maps to support agricultural monitoring and resource management on cloud platforms, while mitigating outlier effects and enabling large-area analysis.
Abstract
Efficient use of cultivated areas is a necessary factor for sustainable development of agriculture and ensuring food security. Along with the rapid development of satellite technologies in developed countries, new methods are being searched for accurate and operational identification of cultivated areas. In this context, identification of cropland boundaries based on spectral analysis of data obtained from satellite images is considered one of the most optimal and accurate methods in modern agriculture. This article proposes a new approach to determine the suitability and green index of cultivated areas using satellite data obtained through the "Google Earth Engine" (GEE) platform. In this approach, two powerful algorithms, "SNIC (Simple Non-Iterative Clustering) Super Pixels" and "Canny Edge Detection Method", are combined. The SNIC algorithm combines pixels in a satellite image into larger regions (super pixels) with similar characteristics, thereby providing better image analysis. The Canny Edge Detection Method detects sharp changes (edges) in the image to determine the precise boundaries of agricultural fields. This study, carried out using high-resolution multispectral data from the Sentinel-2 satellite and the Google Earth Engine JavaScript API, has shown that the proposed method is effective in accurately and reliably classifying randomly selected agricultural fields. The combined use of these two tools allows for more accurate determination of the boundaries of agricultural fields by minimizing the effects of outliers in satellite images. As a result, more accurate and reliable maps can be created for agricultural monitoring and resource management over large areas based on the obtained data. By expanding the application capabilities of cloud-based platforms and artificial intelligence methods in the agricultural field.