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Journal articles on the topic 'Artificial Intelligence Machine Learning Algorithms Acoustic Analysis Remote Sensing'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

a, Drakshayini, Mohan S.T, Swathi M., and Kadali Nanjundeshwara. "LEVERAGING MACHINE LEARNING AND REMOTE SENSING FOR WILDLIFE CONSERVATION: A COMPREHENSIVE REVIEW." International Journal of Advanced Research 11, no. 06 (2023): 636–47. http://dx.doi.org/10.21474/ijar01/17110.

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In recent years, the application of machine learning and remote sensing technologies in wildlife conservation has demonstrated tremendous promise. This article provides a comprehensive overview of the advancements in these fields and the impact they have had on various aspects of wildlife conservation. These technologies contribute to more efficient and effective conservation strategies by automating species identification, mapping and monitoring habitats, tracking population dynamics, detecting wildlife crime, and analysing animal vocalisations. This article talks about the development of mac

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Xiao, Perry, and Daqing Chen. "Photothermal Radiometry Data Analysis by Using Machine Learning." Sensors 24, no. 10 (2024): 3015. http://dx.doi.org/10.3390/s24103015.

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Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new data, without being explicitly programmed to do so. In this paper, we first review the latest development of machine learning and its applications in photothermal techniques. Next, we present our latest work on machine learning for data analysis in opto-thermal transient emission radiometry (OTTE

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Argyrou, Argyro, and Athos Agapiou. "A Review of Artificial Intelligence and Remote Sensing for Archaeological Research." Remote Sensing 14, no. 23 (2022): 6000. http://dx.doi.org/10.3390/rs14236000.

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The documentation and protection of archaeological and cultural heritage (ACH) using remote sensing, a non-destructive tool, is increasingly popular for experts around the world, as it allows rapid searching and mapping at multiple scales, rapid analysis of multi-source data sets, and dynamic monitoring of ACH sites and their environments. The exploitation of remote sensing data and their products have seen an increased use in recent years in the fields of archaeological science and cultural heritage. Different spatial and spectral analysis datasets have been applied to distinguish archaeologi

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Tsai, Fuan, Chao-Hung Lin, Walter W. Chen, Jen-Jer Jaw, and Kuo-Hsin Tseng. "Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”." Remote Sensing 12, no. 12 (2020): 1947. http://dx.doi.org/10.3390/rs12121947.

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The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications w

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Liu, Hui, Lifu Shu, Xiaodong Liu, Pengle Cheng, Mingyu Wang, and Ying Huang. "Advancements in Artificial Intelligence Applications for Forest Fire Prediction." Forests 16, no. 4 (2025): 704. https://doi.org/10.3390/f16040704.

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In recent years, the increasingly significant impacts of climate change and human activities on the environment have led to more frequent occurrences of extreme events such as forest fires. The recurrent wildfires pose severe threats to ecological environments and human life safety. Consequently, forest fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing ecological and economic losses, improving forest fire management efficiency, and ensuring personnel safety and property security. To enhance comprehensive understanding of wil

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Furuya, Danielle Elis Garcia, Édson Luis Bolfe, Taya Cristo Parreiras, Jayme Garcia Arnal Barbedo, Thiago Teixeira Santos, and Luciano Gebler. "Combination of Remote Sensing and Artificial Intelligence in Fruit Growing: Progress, Challenges, and Potential Applications." Remote Sensing 16, no. 24 (2024): 4805. https://doi.org/10.3390/rs16244805.

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Fruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool to optimize production, monitor crop health, and predict harvests with greater accuracy. This study was developed in four main stages. In the first stage, a comprehensive review of the existing literature was made from July 2018 (first article found) to June 2024, totaling 117 articles. In t

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Najar, Mahmoud Al, Rafael Almar, Grégoire Thoumyre., Erwin W. J. Bergsma, Jean-Marc Delvit, and Dennis G. Wilson. "GLOBAL SHORELINE FORECASTING USING SATELLITE-DERIVED DATA AND INTERPRETABLE MACHINE LEARNING." Coastal Engineering Proceedings, no. 38 (May 29, 2025): 219. https://doi.org/10.9753/icce.v38.management.219.

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Coastal development and climate change are changing the geography of our coasts, while more and more people are moving towards the coasts. Recent advances in artificial intelligence and remote sensing allow for the automatic analysis of observational data at a global scale. Symbolic Regression (SR) is a family of Machine Learning (ML) algorithms for constructing symbolic mathematical expressions which model the relations between inputs and outputs in training data. In this work, we make use of SR and a novel global-scale shoreline forecasting dataset in order to construct globally-applicable a

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8

Li, Wenyue. "The application of artificial intelligence in aerospace engineering." Applied and Computational Engineering 35, no. 1 (2024): 17–25. http://dx.doi.org/10.54254/2755-2721/35/20230353.

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In recent years, there has been considerable interest in applying Artificial Intelligence (AI) in the field of aerospace engineering. However, the existing literature on this topic is not sufficiently comprehensive. This paper is purposed to solve this problem by providing a thorough analysis and overview of the current state of AI in aerospace engineering. The paper is divided into four sections. Firstly, the use of AI in autonomous navigation and flight control is explored, focusing on advanced algorithms and sensor technologies that enable highly autonomous and efficient aircraft navigation

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Shan, Yulong, Ren Zhang, Ismail Gultepe, Yaojia Zhang, Ming Li, and Yangjun Wang. "Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis." Applied Sciences 9, no. 21 (2019): 4487. http://dx.doi.org/10.3390/app9214487.

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The reconstruction and monitoring of visibility over marine environments is critically important because of a lack of observations. To travel safely in marine environments, a high quality of visibility data is needed to evaluate navigation risk. Currently, although visibility is available through numerical weather prediction models as well as ground and spaceborne remote sensing platforms and ship measurements, issues still exist over the remote marine environments and northern latitudes. To improve visibility prediction and reduce navigational risks, gridded visibility data based on artificia

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Lemenkova, Polina. "Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia." Journal of Marine Science and Engineering 12, no. 8 (2024): 1279. http://dx.doi.org/10.3390/jmse12081279.

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This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised

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Lemenkova, Polina. "Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types Around Cheetham Wetlands, Port Phillip Bay, Australia." Journal of Marine Science and Engineering 12, no. 8 (2024): 1279. https://doi.org/10.3390/jmse12081279.

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This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised

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El-Omairi, Mohamed Ali, and Abdelkader El Garouani. "Lithological Mapping using Artificial Intelligence and Remote Sensing data: A Case Study of Bab Boudir region, Morocco." BIO Web of Conferences 115 (2024): 01005. http://dx.doi.org/10.1051/bioconf/202411501005.

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Lithological mapping is a crucial component of geological analysis, providing valuable insights into a region's mineralization potential and aiding mineral prospecting efforts. Manual execution of this task, especially in remote and resource-intensive areas, poses significant challenges. The integration of artificial intelligence (AI) techniques with remotely sensed data offers a swift, cost-effective, and precise approach to lithological mapping. In this study, machine learning algorithms (SVM, RF, and ANN) and deep learning techniques (CNN) were employed to map lithological units in an area,

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Kalichkin, V. K., K. Yu Maksimovich, O. A. Aleshchenko, and V. V. Aleshchenko. "Crop Yield Prediction: Data Structure and Ai-Powered Methods." Agricultural Machinery and Technologies 19, no. 2 (2025): 33–44. https://doi.org/10.22314/2073-7599-2025-19-2-33-44.

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Smart farming, also known as intelligent agriculture, represents a modern stage in the development of agricultural science and practice. Its defining feature lies in the active application of artificial intelligence methods, particularly machine learning and deep learning, to address specific tasks aimed at ensuring sustainable crop production. (Research purpose) The aim of this study is to analyze data structures and compare machine learning and deep learning algorithms used in used in crop yield prediction. (Materials and methods) Using a convergent approach and applying methods of cognitive

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Zhang, Zhiheng. "Environmental Noise Monitoring and Management in the Context of Artificial Intelligence." International Journal of Education and Social Development 2, no. 3 (2025): 90–95. https://doi.org/10.54097/d7vr1f98.

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With the acceleration of urbanization, the problem of environmental noise pollution has become increasingly serious, seriously affecting the quality of life and physical and mental health of residents. This paper systematically discusses the progress and challenges of the application of artificial intelligence technology in environmental noise monitoring and management. In the field of monitoring, traditional methods such as sound level meters, remote sensing technology and noise sensors are practical, but there are limitations such as low spatial and temporal resolution, and insufficient data

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Chipatiso, Ezra. "Application of GIS and Artificial Intelligence in Military Operations: Prospects and Challenges." Space Science Journal 1, no. 2 (2024): 01–07. http://dx.doi.org/10.33140/ssj.01.02.06.

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Geographic Information System (GIS) and Remote Sensing have been considered significant in the military due to their spatiality in nature. Recent military developments have seen various military institutions depending on spatial mapping tools, for the purpose of command, control, communication and coordination in military operations. In this study, the qualitativeanalytical method was used to illustrate the applications of GIS in military operations, drawing lessons from land based military developments from selected studies. An online survey was conducted to extract information from a sample

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Al-Khafaji, Mahmoud Saleh, Layth Abdulameer, Muthanna M. A. AL-Shammari, Najah M. L. Al Maimuri, Anmar Dulaimi, and Dhiya Al‑Jumeily. "Revolutionizing Water Quality Monitoring with Artificial Intelligence: A Systematic Review." Journal of Studies in Science and Engineering 5, no. 1 (2025): 358–85. https://doi.org/10.53898/josse2025528.

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Traditional water quality monitoring methods face significant limitations, including delayed data acquisition, high operational costs, and inadequate spatial and temporal resolution, which hinder timely responses to contamination events. This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (M

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Karakhanova, Lobarkhan, Lola Akramova, Hamida Rustamova, and Mukhabbat Khalmuratova. "Smart technologies used in vital ecological data analysis." BIO Web of Conferences 145 (2024): 04036. http://dx.doi.org/10.1051/bioconf/202414504036.

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The advent of smart technologies has revolutionized ecological data analysis, offering innovative solutions to monitor, interpret, and manage ecological systems with unprecedented precision and efficiency. This paper explores the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), remote sensing, and the Internet of Things (IoT) in ecological data analysis. AI and ML algorithms facilitate the processing of vast datasets, enabling predictive modeling and pattern recognition that were previously unattainable. Remote sensing technologies provide real-

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18

LEMENKOVA, Polina. "Machine Learning Methods of Remote Sensing Data Processing for Mapping Salt Pan Crust Dynamics in Sebkha de Ndrhamcha, Mauritania." Artificial Satellites 60, no. 2 (2025): 37–69. https://doi.org/10.2478/arsa-2025-0003.

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ABSTRACT The advances in Machine Learning (ML) and computer technologies enabled to process satellite images using programming. Environmental applications that handle Remote Sensing (RS) data for spatial analysis use such an approach, for example, Python’s library scikit-learn using algorithms on pattern identification, predictions or image classification. This paper presents an ML method of satellite image processing using Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). The aim is to classify multispectral Landsat images using ML for identification of

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19

He, Zeyu. "Social Dynamics and Intelligence of Killer Whales (Orcinus Orca)." Highlights in Science, Engineering and Technology 74 (December 29, 2023): 584–89. http://dx.doi.org/10.54097/4eygvh38.

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The present study undertakes a comprehensive investigation into the behavioral and social complexity of Orcinus orca, commonly referred to as killer whales. Utilizing a multimodal approach that combines acoustic analysis, behavioral observation, and social network mapping, this research addresses four primary dimensions: the species' specialized communication techniques, intricate social structures, cooperative hunting strategies, and evidence for cultural transmission. Our analysis reveals that killer whales employ diverse acoustic and non-acoustic signals, enabling intricate communication ne

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20

Delcea, Camelia, Ionuț Nica, Ștefan Ionescu, Bianca Cibu, and Horațiu Țibrea. "Mapping the Frontier: A Bibliometric Analysis of Artificial Intelligence Applications in Local and Regional Studies." Algorithms 17, no. 9 (2024): 418. http://dx.doi.org/10.3390/a17090418.

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This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top

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Costanza, Leonardo, Beatriz Lorente, Francisco Pedrero Salcedo, et al. "Predicting Olive Tree Chlorophyll Fluorescence Using Explainable AI with Sentinel-2 Imagery in Mediterranean Environment." Applied Sciences 15, no. 5 (2025): 2746. https://doi.org/10.3390/app15052746.

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Chlorophyll fluorescence is a useful indicator of a plant’s physiological status, particularly under stress conditions. Remote sensing is an increasingly adopted technology in modern agriculture, allowing the acquisition of crop information (e.g., chlorophyll fluorescence) without direct contact, reducing fieldwork. The objective of this study is to improve the monitoring of olive tree fluorescence (Fv′/Fm′) via remote sensing in a Mediterranean environment, where the frequency of stress factors, such as drought, is increasing. An advanced approach combining explainable artificial intelligence

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Kikaki, Katerina, Ioannis Kakogeorgiou, Paraskevi Mikeli, Dionysios E. Raitsos, and Konstantinos Karantzalos. "MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data." PLOS ONE 17, no. 1 (2022): e0262247. http://dx.doi.org/10.1371/journal.pone.0262247.

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Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Materia

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23

Andronie, Mihai, George Lăzăroiu, Oana Ludmila Karabolevski, et al. "Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things." Electronics 12, no. 1 (2022): 22. http://dx.doi.org/10.3390/electronics12010022.

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The purpose of our systematic review was to inspect the recently published research on Internet of Robotic Things (IoRT) and harmonize the assimilations it articulates on remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms. The research problems were whether robotic manufacturing processes and industrial wireless sensor networks shape IoRT and lead to improved product quality by use of remote big data management tools, whether IoRT devices communicate autonomously regarding event modeling and forecasting by leveraging m

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Chatterjee, Sheshadri, Tomas Kliestik, Zuzana Rowland, and Martin Bugaj. "Immersive collaborative business process and extended reality-driven industrial metaverse technologies for economic value co-creation in 3D digital twin factories." Oeconomia Copernicana 2025, no. 16 (2025): 125–61. https://doi.org/10.24136/oc.3596.

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Research background: Internet of Things devices and sensors, artificial intelligence-based digital asset trading and digital twin-based extended reality technologies, and autonomous robotic and enterprise resource planning systems can be leveraged in 3D semantic scene completion and metaverse-based commercial transactions across Internet of Things-based business environments. Distributed ledger and enterprise business technologies, shop-floor digital twin synthetic data, and 3D simulation and visualization systems configure integrated multi-physics workflows in hyper-realistic immersive indust

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25

Arévalo-Royo, Javier, Francisco-Javier Flor-Montalvo, Juan-Ignacio Latorre-Biel, Rubén Tino-Ramos, Eduardo Martínez-Cámara, and Julio Blanco-Fernández. "AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context." Applied Sciences 15, no. 4 (2025): 2096. https://doi.org/10.3390/app15042096.

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This research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, disease diagnosis, and resource management. Utilizing a comprehensive bibliometric analysis of scientific literature from 2020 to 2024, this research outlines the increasing incorporation of AI in Spain and identifies the prevailing trends and

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Supattra, Puttinaovarat, Saeliw Aekarat, Pruitikanee Siwipa, et al. "River classification and change detection from landsat images by using a river classification toolbox." International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (2021): 948–59. https://doi.org/10.11591/ijai.v10.i4.pp948-959.

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Water bodies especially rivers are vital to existence of all lifeforms on Earth. Therefore, monitoring river areas and water bodies is essential. In the past, the monitoring relied essentially on manpower in surveying individual areas. However, there are limitations associated wih such surveys, e.g., tremendous amount of time and labour involved in expeditions. Presently, there have been accelerated development in remote sensing (RS) and artificial intelligence (AI) technology, particularly for change monitoring and detection in different areas globally. This research presents technical develo

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Lazaroiu, George, Tom Gedeon, Katarina Valaskova, et al. "Cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative artificial intelligence and cyber–physical manufacturing systems in the immersive industrial metaverse." Equilibrium. Quarterly Journal of Economics and Economic Policy 19, no. 3 (2024): 719–48. http://dx.doi.org/10.24136/eq.3131.

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Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning-assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spa

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Bao, Mingwei, Jiahao Liu, Hong Ren, et al. "Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis." Forests 15, no. 7 (2024): 1197. http://dx.doi.org/10.3390/f15071197.

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Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences.

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Faiza, Bouzahar, Belksier Mohamed Salah, Keblouti Mehdi, et al. "Flood risk mapping using artificial intelligence “application to the east Algerian region”." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e10985. https://doi.org/10.54021/seesv5n2-559.

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The increased needs of the actors in land management mean that static maps no longer meet the requirements of scientists and decision-makers. Access is needed to the data, methods and tools to produce complex maps in response to the different stages of risk evaluation and response. The availability of high spatial resolution remote sensing data makes it possible to detect objects close to human size and, therefore, is of interest for studying anthropogenic activities. The development of new methods and knowledge using detailed spatial data, coupled with the use of Geographic Information System

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Kemper, H., and G. Kemper. "SENSOR FUSION, GIS AND AI TECHNOLOGIES FOR DISASTER MANAGEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1677–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1677-2020.

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Abstract. Modern Disaster Management Systems are based on several columns that combine theory and practice, software, and hardware being under technological advance. In all parts, spatial data is key in order to analyze existing structure, assist in risk assessment and update the information after a disaster incident. This paper focus on technological advances in several fields of spatial analysis putting together the advantages, limitations and technological aspects from well-known or even innovative methods, highlighting the huge potential of nowadays technologies for the field of Disaster R

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Puttinaovarat, Supattra, Aekarat Saeliw, Siwipa Pruitikanee, et al. "River classification and change detection from landsat images by using a river classification toolbox." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (2021): 948. http://dx.doi.org/10.11591/ijai.v10.i4.pp948-959.

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<span>Water bodies especially rivers are vital to existence of all lifeforms on Earth. Therefore, monitoring river areas and water bodies is essential. In the past, the monitoring relied essentially on manpower in surveying individual areas. However, there are limitations associated wih such surveys, e.g., tremendous amount of time and labour involved in expeditions. Presently, there have been accelerated development in remote sensing (RS) and artificial intelligence (AI) technology, particularly for change monitoring and detection in different areas globally. This research presents tech

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S., Magesh, Niveditha V.R., Rajakumar P.S., Radha RamMohan S., and Natrayan L. "Pervasive computing in the context of COVID-19 prediction with AI-based algorithms." International Journal of Pervasive Computing and Communications 16, no. 5 (2020): 477–87. http://dx.doi.org/10.1108/ijpcc-07-2020-0082.

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Purpose The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various par

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Zhao, Xiyong, Yanzhou Li, Yongli Chen, Xi Qiao, and Wanqiang Qian. "Water Chlorophyll a Estimation Using UAV-Based Multispectral Data and Machine Learning." Drones 7, no. 1 (2022): 2. http://dx.doi.org/10.3390/drones7010002.

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Chlorophyll a (chl-a) concentration is an important parameter for evaluating the degree of water eutrophication. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication, and the inversion of water quality from UAV images has attracted more and more attention. In this study, a regression method to estimate chl-a was proposed; it used a small multispectral UAV to collect data and took the vegetation indices as intermediate variables. For this purpose, ten monitoring points were selected in Erhai Lake, China, and two months of monit

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Chicchon, Miguel, Eva Savina Malinverni, Marsia Sanità, Roberto Pierdicca, Francesca Colosi, and Francisco James León Trujillo. "Building Semantic Segmentation Using UNet Convolutional Network on SpaceNet Public Data Sets for Monitoring Surrounding Area of Chan Chan (Peru)." Geomatics and Environmental Engineering 18, no. 3 (2024): 25–43. http://dx.doi.org/10.7494/geom.2024.18.3.25.

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The amount of damage to cultural heritage sites is increasing rapidly every year. This is due to inadequate heritage management and uncontrolled urban growth as well as unpredictable seismic and atmospheric events that manifest themselves in a continuously deteriorating ecosystem. Thus, applications of artificial intelligence (AI) in remote-sensing (RS) techniques (machine-learning and deep-learning algorithms) for monitoring archaeological sites have increased in recent years. This research involves the surrounding area of the archaeological site of Chan Chan in Peru in particular. An approac

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Kalichkin, Vladimir, A. Donchenko, and Kirill Golohvast. "Formation of a digital agriculture management system based on monitoring and long-term field experiments." Agrobiotechnologies and digital farming 4, no. 2 (2025): 58–68. https://doi.org/10.12737/2782-490x-2025-58-68.

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The article is devoted to the development of a digital agriculture management system (DAMS) that combines monitoring of agricultural objects, analysis of long-term field experiment data and modern information technologies. The purpose of the study is to create a theoretical basis for optimizing agricultural production management by integrating artificial intelligence, machine learning, remote sensing, geographic information systems (GIS) and big data. The novelty of the work lies in the use of machine learning algorithms for yield forecasting, scaling of agricultural system archetypes based on

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Reaz Uddin Rayhan, S M Rashidul Islam, and Arfeen Jahan Azad. "Integrating Advanced Monitoring Technologies and Reliability Engineering for Proactive Wildfire Risk Management." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 538–50. https://doi.org/10.32996/jcsts.2025.7.2.57.

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The frequency and intensity of wildfires escalate because of environmental change together with anthropological activities and growing urban-wildland areas. The necessity for proactive wildfire risk management arises because it creates substantial economic losses and environmental damage and harmful impacts on public health thus becoming an essential worldwide concern. This paper investigates the integration of advanced remote sensing technologies with reliability engineering principles to establish a proactive risk management framework for wildfires. The research reviews state-of-the-art moni

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Zhang, Shuhao, Yawei Wang, and Guang Wu. "Earthquake-Induced Landslide Susceptibility Assessment Using a Novel Model Based on Gradient Boosting Machine Learning and Class Balancing Methods." Remote Sensing 14, no. 23 (2022): 5945. http://dx.doi.org/10.3390/rs14235945.

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Predicting the susceptibility of a specific part of a landslide (SSPL) involves predicting the likelihood that the part of the landslide (e.g., the entire landslide, the source area, or the scarp) will form in a given area. When predicting SSPL, the landslide samples are far less than the non-landslide samples. This class imbalance makes it difficult to predict the SSPL. This paper proposes an advanced artificial intelligence (AI) model based on the dice-cross entropy (DCE) loss function and XGBoost (XGBDCE) or Light Gradient Boosting Machine (LGBDCE) to ameliorate the class imbalance in the S

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K S, Somashekar, Moinuddin, Ningaraj Belagalla, et al. "Revolutionizing Agriculture: Innovative Techniques, Applications, and Future Prospects in Precision Farming." Journal of Scientific Research and Reports 30, no. 8 (2024): 405–19. http://dx.doi.org/10.9734/jsrr/2024/v30i82263.

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Precision agriculture (PA) represents a transformative approach to farming, employing advanced technologies to enhance productivity, efficiency, and sustainability. This review article provides an in depth analysis of the latest innovations in PA techniques, their diverse applications, and future directions. Precision agriculture is revolutionizing the agricultural landscape by integrating sophisticated tools such as GPS, remote sensing, Internet of Things (IoT), and big data analytics. These technologies enable farmers to monitor and manage variability in crop production meticulously, optimiz

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Onaiwu, Gregory E., and Nneka Joy Ayidu. "ADVANCEMENTS AND INNOVATIONS IN PM2.5 MONITORING: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES." FUDMA JOURNAL OF SCIENCES 8, no. 3 (2024): 243–55. http://dx.doi.org/10.33003/fjs-2024-0803-2505.

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This comprehensive review examines the evolving landscape of PM2.5 monitoring, emphasizing its critical role in environmental chemistry, public health and electrical/electronic engineering. Traditional methods, including manual sampling, gravimetric analysis, and the Federal Reference Method (FRM), have long been relied upon for PM2.5 measurement but are hindered by limitations in spatial coverage, temporal resolution, and cost. In response, emerging technologies such as wireless sensor networks, low-cost sensor technologies, remote sensing techniques, and machine learning algorithms offer pro

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Gao, Jerry, Jia Liu, Rui Xu, Samiksha Pandey, Venkata Sai Kusuma Sindhoora Vankayala Siva, and Dian Yu. "Environmental Pollution Analysis and Impact Study—A Case Study for the Salton Sea in California." Atmosphere 13, no. 6 (2022): 914. http://dx.doi.org/10.3390/atmos13060914.

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A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the

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Kissling, W. Daniel. "Using big data to address global environmental challenges." ARPHA Conference Abstracts 8 (May 28, 2025): e151516. https://doi.org/10.3897/aca.8.e151516.

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Global policy frameworks such as the UN Sustainable Development Goals (SDGs) or the Kunming-Montreal Global Biodiversity Framework (KMGBF) as well as numerous EU policies related to species and habitat conservation (e.g. Nature Restoration Law, Birds Directive, Habitats Directive, Water Framework Directive, Marine Strategy Framework Directive), ecosystem services (e.g. Pollinators Initiative, Land Use Land Use Cover and Forestry Regulation, proposed Forest Monitoring Regulation) and the sustainable management of natural resources (e.g. Common Fisheries Policy, Common Agricultural Policy) highl

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Chen, Yuhao, Gongwen Wang, Nini Mou, Leilei Huang, Rong Mei, and Mingyuan Zhang. "Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China." Applied Sciences 15, no. 8 (2025): 4082. https://doi.org/10.3390/app15084082.

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With the rapid development of big data and artificial intelligence technologies, the era of Industry 4.0 has driven large open-pit mines towards digital and intelligent transformation. This is particularly true in mature mining areas such as the Yanshan Iron Mine, where the depletion of shallow proven reserves and the increasing issues of mixed surrounding rocks with shallow ore bodies make it increasingly important to build intelligent mines and implement green and sustainable development strategies. However, previous mineralization predictions for the Yanshan Iron Mine largely relied on trad

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ADEYEYE, Sophia V., and Donald Abidemi ODELEYE. "Leveraging Artificial Intelligence (AI) for Effective Pastoral Bibliotherapy and Hymnotherapy: A New Frontier in Emotional, Psychological, and Spiritual Healing." Pastoral Counsellors: Journal of Nigerian Association of Pastoral Counsellors 4 (February 6, 2025): 423–30. https://doi.org/10.5281/zenodo.14827934.

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This paper examines the potential of Artificial Intelligence (AI) in enhancing the effectiveness of bibliotherapy and hymnotherapy as therapeutic practices. Bibliotherapy, the use of literature for healing, and hymnotherapy, the therapeutic use of hymns and spiritual music, have long been recognized for their ability to support emotional, psychological, and spiritual well-being. AI, with its advanced capabilities in data analysis, personalization, and content delivery, offers new possibilities for making these therapies more accessible, tailored, and impactful. AI can play a pivotal role in bi

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Cenameri, Anja. "Përdorimi i inteligjencës artificiale në hartat mjedisore në Tiranë. Një rrugë drejt planifikimit urban të qëndrueshëm." Optime, no. 1 (November 24, 2024): 265–72. https://doi.org/10.55312/op.vi1.5888.

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Tirana, the capital of Albania, has experienced rapid urban development, resulting in significant environmental challenges. Historical data reveal a drastic reduction in green space per capita, from approximately 10 m² during the communist period to a concerning 0.5 m² in recent years. This de-cline highlights the urgent need for sustainable urban planning strategies that prioritize ecological resilience. This study explores the innovative application of Artificial Intelligence (AI) platforms to compile comprehensive environmental maps of Tirana, aiming to address the city’s sustainability cha

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Franzo, Giovanni, Matteo Legnardi, Giulia Faustini, Claudia Maria Tucciarone, and Mattia Cecchinato. "When Everything Becomes Bigger: Big Data for Big Poultry Production." Animals 13, no. 11 (2023): 1804. http://dx.doi.org/10.3390/ani13111804.

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In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity for the sector, it conceals a multitude of challenges. Pollution and land erosion, competition for limited resources between animal and human nutrition, animal welfare concerns, limitations on the use of growth promoters and antimicrobial agents, and increasing risks and effects of animal infectious diseases and zoonoses are several topics that have received attention from authorities and the publ

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Araujo, Mariela. "Technology Focus: Production Monitoring (March 2023)." Journal of Petroleum Technology 75, no. 03 (2023): 60–61. http://dx.doi.org/10.2118/0323-0060-jpt.

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The oil and gas industry continues to face challenges. We have witnessed volatility in commodity prices (oil supply outstripping demand, leading to overall market slowdown), the COVID-19 pandemic affecting production and supply chains around the world, labor shortages, facilities-security issues, and the need to prepare for the energy transition, just to name a few. Operators, now more than ever, need to make the most of their assets, not only maintaining production but also understanding what is happening around the clock to be able to prevent potential threats or situations that may escalate

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UM, Arshad. "Deep Learning: A Climate Smart Agriculture Tool for Groundnut Farmers." Journal of Energy and Environmental Science 2, no. 1 (2024): 1–13. http://dx.doi.org/10.23880/jeesc-16000111.

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Groundnut is an oil seed crop, which is grown widely in the country, and approximately 80% of groundnut is produced in the rainfed condition. Unlike weather factors even the prices of agriculture commodities are volatile in nature and the groundnut prices also behaves in an unusual pattern. A traditional farmer faces recurrent challenges. Deep learning methods and the accessibility of satellite imagery have, however, created new opportunities for more accurate and effective agricultural yield estimates. Large-scale yield estimation and understanding the impact of the variability of agricultura

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Badhan, Istiaque Ahmed, MD Nurul Hasnain, MD Hafizur Rahman, Irfan Chowdhury, and MD Abu Sayem. "Strategic Deployment of Advance Surveillance Ecosystems: An Analytical Study on Mitigating Unauthorized U.S. Border Entry." Inverge Journal of Social Sciences 3, no. 4 (2024): 82–94. https://doi.org/10.63544/ijss.v3i4.105.

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This research aims at the intricate challenge of securing the U.S. border by investigating the potential of cutting-edge surveillance technologies. We explore a range of innovations, including artificial intelligence, unmanned aerial vehicles (UAVs), sophisticated sensor networks, and sophisticated data integration systems. Through a combination of case studies, technological assessments, and policy analyses, this work aims to understand how these technologies can enhance border security while navigating the complex landscape of ethical and legal considerations. Our research employs a mixed-me

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Sihotang, Tambun, David A. Landgrebe, and Firta Sari Panjaitan. "Expert system approach to improve the accuracy of prediction and solution of various agricultural scenarios." Idea: Future Research 1, no. 1 (2023): 39–47. http://dx.doi.org/10.35335/idea.v1i1.5.

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The proposed research on developing expert systems for accurately predicting and recommending solutions for various agricultural scenarios is novel in several ways: Integration of Multiple Technologies: The research involves the integration of multiple technologies such as knowledge representation, artificial intelligence and machine learning algorithms, data integration and analysis techniques, and evaluation and validation techniques to develop a comprehensive and effective expert system for agriculture. Interdisciplinary Approach: The research is an interdisciplinary approach that brings to

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Drakshayini, S.T Mohan, M. Swathi, and Nanjundeshwara Kadali. "LEVERAGING MACHINE LEARNING AND REMOTE SENSING FOR WILDLIFE CONSERVATION: A COMPREHENSIVE REVIEW." June 21, 2023. https://doi.org/10.5281/zenodo.8124074.

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In recent years, the application of machine learning and remote sensing technologies in wildlife conservation has demonstrated tremendous promise. This article provides a comprehensive overview of the advancements in these fields and the impact they have had on various aspects of wildlife conservation. These technologies contribute to more efficient and effective conservation strategies by automating species identification, mapping and monitoring habitats, tracking population dynamics, detecting wildlife crime, and analysing animal vocalisations. This article talks about the development of mac

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