Version 1
: Received: 26 June 2024 / Approved: 27 June 2024 / Online: 27 June 2024 (12:17:47 CEST)
How to cite:
Tariku, G.; Ghiglieno, I.; Morchio, A. S.; Facciano, L.; Birolleau, C.; Simonetto, A.; Serina, I.; Gilioli, G. Semantic Segmentation of Land Covers Using Deep Learning with a Pre-trained Backbone: A Case Study in in the Franciacorta Wine-growing Area. Preprints2024, 2024061947. https://doi.org/10.20944/preprints202406.1947.v1
Tariku, G.; Ghiglieno, I.; Morchio, A. S.; Facciano, L.; Birolleau, C.; Simonetto, A.; Serina, I.; Gilioli, G. Semantic Segmentation of Land Covers Using Deep Learning with a Pre-trained Backbone: A Case Study in in the Franciacorta Wine-growing Area. Preprints 2024, 2024061947. https://doi.org/10.20944/preprints202406.1947.v1
Tariku, G.; Ghiglieno, I.; Morchio, A. S.; Facciano, L.; Birolleau, C.; Simonetto, A.; Serina, I.; Gilioli, G. Semantic Segmentation of Land Covers Using Deep Learning with a Pre-trained Backbone: A Case Study in in the Franciacorta Wine-growing Area. Preprints2024, 2024061947. https://doi.org/10.20944/preprints202406.1947.v1
APA Style
Tariku, G., Ghiglieno, I., Morchio, A. S., Facciano, L., Birolleau, C., Simonetto, A., Serina, I., & Gilioli, G. (2024). Semantic Segmentation of Land Covers Using Deep Learning with a Pre-trained Backbone: A Case Study in in the Franciacorta Wine-growing Area. Preprints. https://doi.org/10.20944/preprints202406.1947.v1
Chicago/Turabian Style
Tariku, G., Ivan Serina and Gianni Gilioli. 2024 "Semantic Segmentation of Land Covers Using Deep Learning with a Pre-trained Backbone: A Case Study in in the Franciacorta Wine-growing Area" Preprints. https://doi.org/10.20944/preprints202406.1947.v1
Abstract
Land cover mapping, essential for understanding global land use patterns, relies on satellite imagery for monitoring changes, assessing ecosystem health, and supporting conservation ef- forts. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, and a lack of comparative studies for optimal deep learning model selection. Additionally, a scarcity of aerial datasets specifically tailored for agricultural areas exists. This study addresses these gaps by presenting a method for semantic segmentations of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the "Land Cover Aerial Imagery" (LICAID) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conduct a comparative study, employing cutting-edge deep learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately delineating land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpret-ability is essential for advancing the field of remote sensing and supporting sustainable environmental stewardship worldwide.
Keywords
Land cover mapping; Semantic segmentation; Deep learning, Satellite imagery, pre trained backbone
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
,
,
