DEEP LEARNING FOR FARM BOUNDARIES DELINEATION
DOI:
https://doi.org/10.24867/16BE14BaticKeywords:
CNN, Delineation, Remote Sensing, Satellite ImageryAbstract
The most common source of field boundary data is cadaster. Unfortunately, the process of extracting cadastral information has remained highly manual, which inevitably leads to proneness to errors and high costs. One of the possible ways to improve generating and updating cadastral information is automating the delineation of boundaries through satellite imagery and deep learning-based methods. This paper presents a CNN-based architecture for delineating agricultural boundaries, where multispectral satellite images are used as an input of the model. The trained model performs accurate boundary detection and achieves better results than the authors of the original dataset. Even though satisfactory results were achieved, we proposed steps to improve the proposed solution, which could increase robustness.
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