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Exploiting Image Processing, Deep Learning, Machine Learning, and Sustainable Artificial Intelligence Applications

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 3578

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Dr. Dan Munteanu

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Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
Interests: artificial intelligence; machine learning; deep learning; computer vision; internet of things
Special Issues, Collections and Topics in MDPI journals

Dr. Simona Moldovanu

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Guest Editor

Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 800008 Galati, Romania
Interests: computer vision; machine learning; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in image processing, deep learning, and machine learning have catalyzed significant transformations across various industries. These technologies have not only enhanced efficiency and innovation, but have also opened new avenues for addressing pressing environmental and societal challenges. As we navigate the complexities of the 21st century, the integration of sustainable artificial intelligence (AI) becomes imperative to promote environmental stewardship, economic viability, and social well-being.

This Special Issue aims to explore the synergy between AI technologies and sustainability practices. By aligning with the journal's focus on sustainability, we invite contributions that demonstrate how image processing, deep learning, and machine learning can be used to develop sustainable solutions in fields such as education, agriculture, forestry, energy management, and the Internet of Things (IoT). The goal is to foster interdisciplinary collaboration and highlight research that not only pushes the boundaries of AI, but also contributes to Sustainable Development Goals (SDGs).

We encourage submissions on topics including, but not limited to, the following:

Image processing for environmental monitoring and conservation: Innovative techniques for analyzing environmental data to support conservation efforts.
Deep learning in sustainable agriculture and forestry: Applications that improve crop yields, forest management, and biodiversity preservation.
Machine learning for energy management: Models that enhance energy efficiency and promote the use of renewable resources.
Sustainable AI in education: AI-driven approaches that improve access, personalization, and outcomes in education systems.
IoT and AI for smart cities: Integration of AI and IoT to develop smart, sustainable urban environments.
Ethical and responsible AI: Frameworks and practices ensuring that AI technologies are developed and deployed responsibly.
Data-driven environmental science: Utilizing big data and AI to address environmental challenges and climate change.

In this Special Issue, we welcome original research articles and comprehensive reviews that contribute to the advancement of sustainable AI applications. By bringing together cutting-edge research and practical case studies, we hope to inspire innovative solutions that address global sustainability challenges.

We look forward to receiving your valuable contributions.

Sincerely,

Dr. Dan Munteanu
Dr. Simona Moldovanu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

image processing
deep learning
machine learning
artificial intelligence
data
environment science
sustainable artificial intelligence
education
healthcare
agriculture
forestry
physical education
energy management
Internet of Things

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Published Papers (4 papers)

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Research

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28 pages, 16466 KB

Open AccessArticle

SAW-YOLOv8l: An Enhanced Sewer Pipe Defect Detection Model for Sustainable Urban Drainage Infrastructure Management

by Linna Hu, Hao Li, Jiahao Guo, Penghao Xue, Weixian Zha, Shihan Sun, Bin Guo and Yanping Kang

Sustainability 2026, 18(8), 3685; https://doi.org/10.3390/su18083685 - 8 Apr 2026

Viewed by 364

Abstract

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines. Full article

(This article belongs to the Special Issue Exploiting Image Processing, Deep Learning, Machine Learning, and Sustainable Artificial Intelligence Applications)

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43 pages, 10782 KB

Open AccessArticle

Nested Learning in Higher Education: Integrating Generative AI, Neuroimaging, and Multimodal Deep Learning for a Sustainable and Innovative Ecosystem

by Rubén Juárez, Antonio Hernández-Fernández, Claudia Barros Camargo and David Molero

Sustainability 2026, 18(2), 656; https://doi.org/10.3390/su18020656 - 8 Jan 2026

Cited by 1 | Viewed by 1029

Abstract

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency;

α / ω \geq 0.82

), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong (

r = 0.41

–

0.63

p < 0.001

). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education. Full article

(This article belongs to the Special Issue Exploiting Image Processing, Deep Learning, Machine Learning, and Sustainable Artificial Intelligence Applications)

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19 pages, 3279 KB

Open AccessArticle

Research on Wetland Fine Classification Based on Remote Sensing Images with Multi-Temporal and Feature Optimization

by Dongping Xu, Wei Wu, Yesheng Ma and Dianxing Feng

Sustainability 2025, 17(24), 10900; https://doi.org/10.3390/su172410900 - 5 Dec 2025

Viewed by 651

Abstract

Wetlands, known as “the kidney of the Earth”, serve as critical ecological carriers for global sustainable development. The fine classification of wetlands is crucial to their utilization and protection. Wetland fine-scale classification based on remote sensing imagery has long been challenged by disturbances such as clouds, fog, and shadows. Simultaneously, the confusion of spectral information among land cover types remains a primary factor affecting classification accuracy. To address these challenges, this paper proposes a fine classification model of wetlands in remote sensing images based on multi-temporal data and feature optimization (CMW-MTFO). The model is divided into three parts: (1) a multi-satellite and multi-temporal remote sensing image fusion module; (2) a feature optimization module; and (3) a feature classification network module. Multi-satellite multi-temporal image fusion compensates for information gaps caused by cloud cover, fog, and shadows, while feature optimization reduces spectral characteristics prone to confusion. Finally, fine classification is completed using the feature classification network based on deep learning. Using coastal wetlands in Liaoning Province, China, as the experimental area, this study compares the CMW-MTFO with several classical wetland classification methods, non-feature-optimized classification, and single-temporal classification. Results show that the proposed model achieves an overall classification accuracy of 98.31% for Liaoning wetlands, with a Kappa coefficient of 0.9795. Compared to the classic random forest method, classification accuracy and Kappa coefficient improved by 11.09% and 0.1286, respectively. Compared to non-feature-based classification, classification accuracy increased by 1.06% and Kappa coefficient by 1.18%. Compared to the best classification performance using single-temporal images, the proposed method achieved a 1.81% increase in classification accuracy and a 2.19% increase in Kappa value, demonstrating the effectiveness of the model approach. Full article

(This article belongs to the Special Issue Exploiting Image Processing, Deep Learning, Machine Learning, and Sustainable Artificial Intelligence Applications)

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Review

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36 pages, 2213 KB

Open AccessReview

Sustainable Estimation of Tree Biomass and Volume Using UAV Imagery: A Comprehensive Review

by Dan Munteanu, Simona Moldovanu, Gabriel Murariu and Lucian Dinca

Sustainability 2026, 18(2), 1095; https://doi.org/10.3390/su18021095 - 21 Jan 2026

Cited by 2 | Viewed by 686

Abstract

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks. Full article

(This article belongs to the Special Issue Exploiting Image Processing, Deep Learning, Machine Learning, and Sustainable Artificial Intelligence Applications)

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