文章基本信息

标题：Land Use Feature Detection from Satellite Imagery using Machine Learning Models
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作者：Sushil Chandra ; Udai Raj ; Rajeev Sonkar 等
期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2022
卷号：4
期号：1
页码：853-860
DOI：10.35629/5252-0401655661
语种：English
出版社：IJAEM JOURNAL
摘要：Land-use feature detection is one of the hot applications of GIS (Geographic Information System). With satellite imagery as the forefront source of updated geographical data, we can use it to observe the land-use feature change and keep up with the latest changes with minimal effort and maximum efficiency. Already, many parties have started deriving and working on different methodologies to achieve this goal. Some of the approaches use the algorithms of Machine Learning and the performance level achieved are quite satisfactory. In this paper, we have explored some of the Machine Learning based approaches (Random Forest, XGBoost, U-Net, Artificial Neural Network) for land-use feature detection. We have used the online platform, google colab and online storage google drive to train our model and perform the prediction.
关键词：Land use features;Satellite Imagery;Machine Learning;Deep Learning;Random Forest; XGBoost;U-Net;Artificial Neural Network(ANN)