Machine Learning Approaches for Developing Land Cover Mapping

Alzahrani, Ali; Kanan, Awos

doi:10.1155/2022/5190193

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…F-score is one of the machine-learning measures that help assess the RF model's precision. The proportion of all instances that are classified by the model is known as classification accuracy [42]. The minimum level of the overall accuracy should be at least 85 percent [43].…”

Section: Image Preprocessing Interpretation and Analysismentioning

confidence: 99%

An Application of Machine-Learning Model for Analyzing the Impact of Land-Use Change on Surface Water Resources in Gauteng Province, South Africa

Gidey,

Mhangara

2023

Remote Sensing

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The change in land-use diversity is attributed to the anthropogenic factors sustaining life. The surface water bodies and other crucial natural resources in the study area are being depleted at an alarming rate. This study explored the implications of the changing land-use diversity on surface water resources by using a random forest (RF) classifier machine-learning algorithm and remote-sensing models in Gauteng Province, South Africa. Landsat datasets from 1993 to 2022 were used and processed in the Google Earth Engine (GEE) platform, using the RF classifier. The results indicate nine land-use diversity classes having increased and decreased tendencies, with high F-score values ranging from 72.3% to 100%. In GP, the spatial coverage of BL has shrunk by 100.4 km2 every year over the past three decades. Similarly, BuA exhibits an annual decreasing rate of 42.4 km2 due to the effect of dense vegetation coverage within the same land use type. Meanwhile, water bodies, marine quarries, arable lands, grasslands, shrublands, dense forests, and wetlands were expanded annually by 1.3, 2.3, 2.9, 5.6, 11.2, 29.6, and 89.5 km2, respectively. The surface water content level of the study area has been poor throughout the study years. The MNDWI and NDWI values have a stronger Pearson correlation at a radius of 5 km (r = 0.60, p = 0.000, n = 87,260) than at 10 and 15 km. This research is essential to improve current land-use planning and surface water management techniques to reduce the environmental impacts of land-use change.

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Section: Image Preprocessing Interpretation and Analysismentioning

confidence: 99%

An Application of Machine-Learning Model for Analyzing the Impact of Land-Use Change on Surface Water Resources in Gauteng Province, South Africa

Gidey,

Mhangara

2023

Remote Sensing

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“…Four commonly used machine learning algorithms-RF, SVM, CART, and GTB [19,66,[120][121][122][123]-were employed for the classification of the study area. The classification processes were carried out using both Sentinel-2 data alone and the integration of Sentinel-2 and Sentinel-1 data.…”

Section: Classificationmentioning

confidence: 99%

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

Mengesha,

Desta,

Gamba

et al. 2024

Land

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In most developing countries, smallholder farms are the ultimate source of income and produce a significant portion of overall crop production for the major crops. Accurate crop distribution mapping and acreage estimation play a major role in optimizing crop production and resource allocation. In this study, we aim to develop a spatio–temporal, multi-spectral, and multi-polarimetric LULC mapping approach to assess crop distribution mapping and acreage estimation for the Oromia Region in Ethiopia. The study was conducted by integrating data from the optical and radar sensors of sentinel products. Supervised machine learning algorithms such as Support Vector Machine, Random Forest, Classification and Regression Trees, and Gradient Boost were used to classify the study area into five first-class common land use types (built-up, agriculture, vegetation, bare land, and water). Training and validation data were collected from ground and high-resolution images and split in a 70:30 ratio. The accuracy of the classification was evaluated using different metrics such as overall accuracy, kappa coefficient, figure of metric, and F-score. The results indicate that the SVM classifier demonstrates higher accuracy compared to other algorithms, with an overall accuracy for Sentinel-2-only data and the integration of optical with microwave data of 90% and 94% and a kappa value of 0.85 and 0.91, respectively. Accordingly, the integration of Sentinel-1 and Sentinel-2 data resulted in higher overall accuracy compared to the use of Sentinel-2 data alone. The findings demonstrate the remarkable potential of multi-source remotely sensed data in agricultural acreage estimation in small farm holdings. These preliminary findings highlight the potential of using multi-source active and passive remote sensing data for agricultural area mapping and acreage estimation.

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“…Metaheuristic optimization algorithms such as (GA) [10], ant colony optimization (ACO) [11], and chemical reaction optimization (CRO) [12] are used to solve different optimization problems. These algorithms have been extensively used in recent years to implement digital filters in a wide range of applications [13].…”

Section: Related Workmentioning

confidence: 99%

Optimized FIR Filter Using Genetic Algorithms: A Case Study of ECG Signals Filter Optimization

Hamici,

Kanan,

Al-hammuri

2023

BioMedInformatics

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The advancement in technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of finite impulse response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of a comprehensive analysis show that the genetic algorithm-based filter is more effective than conventional filter designs in terms of noise removal efficiency.

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Machine Learning Approaches for Developing Land Cover Mapping

Cited by 7 publications

References 33 publications

An Application of Machine-Learning Model for Analyzing the Impact of Land-Use Change on Surface Water Resources in Gauteng Province, South Africa

An Application of Machine-Learning Model for Analyzing the Impact of Land-Use Change on Surface Water Resources in Gauteng Province, South Africa

Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia

Optimized FIR Filter Using Genetic Algorithms: A Case Study of ECG Signals Filter Optimization

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