Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects

Wang, Junye; Bretz, Michael; Dewan, M. Ali Akber; Delavar, Mojtaba Aghajani

doi:10.1016/j.scitotenv.2022.153559

Cited by 152 publications

(65 citation statements)

References 163 publications

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“…SOM neural networks are trained to reduce the high-dimensional data (e.g., input map with features) to low dimensions. SOMs utilize competitive learning for unsupervised clustering and visualization through error-correction learning [34]. Therefore, SOMs are considered as an effective means of LULC classification concerning dimensionality reduction.…”

Section: Possible Novel Aspects In Lulc Modeling Techniques Utility O...mentioning

confidence: 99%

A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects

Gaur

Singh

2023

Sustainability

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Land use land cover (LULC) modeling is considered as the best tool to comprehend and unravel the dynamics of future urban expansion. The present paper provides a comprehensive review of existing LULC modeling techniques and novel approaches used by the research community. Moreover, the review also compares each technique’s applications, utility, drawbacks, and broader differences. The rationale behind such a comparison is to highlight the strengths/weakness of individual techniques. The review further highlights the utility of the hybridization of different techniques (e.g., machine learning model combined with statistical models) to LULC modeling to complement their strengths. Although significant progress has been made in LULC modeling, the review highlights the need to incorporate the policy framework into LULC modeling for better urban planning and management. The present review will help researchers and policymakers to achieve better land management practices and ultimately assist in achieving Sustainable Development Goal-15 (SDG-15) (i.e., life on land).

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Section: Possible Novel Aspects In Lulc Modeling Techniques Utility O...mentioning

confidence: 99%

A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects

Gaur

Singh

2023

Sustainability

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“…The application of image processing in agriculture results in improved decisionmaking for vegetation, irrigation, fruit sorting, etc. [24,41,42].…”

Section: -1-relevant Studiesmentioning

confidence: 99%

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Francis¹,

Anbananthen

Deisy³

et al. 2023

Emerg Sci J

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Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PDF

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“…Other examples include qualitative and quantitative evaluation of satellite imagery sensor data for regional and urban scale air quality (Avand & Moradi, 2021), support vector machine approach for longitudinal dispersion coefficients in natural streams (Bahari, Ahmad, & Aboobaider, 2014), crisis management , disaster, linear programming for irrigation scheduling (Sun & Zhu, 2019), global climate change and weather forecast (Ise, Oba, & AI, 2019), the status of land cover classification accuracy assessment (J. Wang, Bretz, Dewan, & Delavar, 2022), air pollutants and sources associated with health effects (Verma & Verma, 2021), settlement detection (Assarkhaniki, Sabri, & Rajabifard, 2021)features such as roads/highways and ditch segments extraction (Avand & Moradi, 2021), identify crops' diseases and their yield estimation, building vegetation indices, natural disaster response, and disease outbreak response (Hossain, Zarin, Sahriar, Haque, & Chemistry of the Earth, 2022). In addition, researchers/users are benefitted from the publicly available remote sensing datasets using which they can develop, test and run their ML models for their research (Das, 2020).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Upreti¹

2022

Preprint

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Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

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Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects

Cited by 152 publications

References 163 publications

A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects

A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Machine Learning Application in G.I.S. and Remote Sensing: An Overview

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