Land use/land cover classification using machine learning models

Abstract

<p>An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.</p>

Predictive maintenance framework for assessing health state of centrifugal pumps

“…Finally, in terms of environmental applications, [59] combined a grid search algorithm and XGBoost model for hyperparameter fine tuning and electricity load prediction respectively, similarly [60] proved that ensemble techniques provide an efficient solution for solar radiation forecasting. Additionally, [61], [62], highlighted the effectiveness of ensemble methods on classification predictions combining XGBoost with ML algorithms for land use and rice leaf disease identification respectively. Nevertheless, collecting real-time sensor measurements and the raw data transformation in a machinecomprehensive format can be challenging.…”

Remote Sensing Study of the Impact of Social-Economic Development on The Scale of Land Use in The Shaki District

“…Image classification is an automated approach to classifying raster data from satellite imagery to aerial imagery and drone imagery (Senta A., and Šerić, L., 2021). This would typically involve evaluating several images and applying statistical rules to determine land cover identity for each pixel in the image (Swetanisha S., et, al, 2022)…”

A Novel Transfer Learning Based Deep Model for Land Classification

“…On recent years, land classification using deep learning techniques has gained significant attention in the research communities due to its potential applications in agriculture, urban planning, environmental monitoring, and more. Further, recent advancements in land classification using machine learning [9][10] [11] and deep learning [12][13] [14] have focused on enhancing classification accuracy, efficiency, and scalability, while also addressing challenges related to data availability, model interpretability, and uncertainty quantification. Researchers are integrating data from various sources, like satellite imagery [15], LiDAR (Light Detection and Ranging) [17], and aerial photographs [18] [19] to improve land classification accuracy and capture finer-scale spatial patterns.…”