A systematic literature review of remote sensing approaches in urban green space research: Towards achieving sustainable development goals

Urban Climate, Journal Year: 2025, Volume and Issue: 59, P. 102332 - 102332

Published: Feb. 1, 2025

Language: Английский

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Assessing the current landscape of AI and sustainability literature: identifying key trends, addressing gaps and challenges

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: May 6, 2024

Abstract The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, data-driven methods offer potential solutions for optimizing resources, integrating different aspects sustainability, informed decision-making. Sustainability research surrounds various local, regional, challenges, emphasizing need identify emerging areas gaps AI models play crucial role. study performs comprehensive literature survey scientometric semantic analyses, categorizes problems, discusses sustainable use big data. outcomes analyses highlight collaborative inclusive that bridges regional differences, interconnection topics, major themes related It further emphasizes significance developing hybrid approaches combining techniques, expert knowledge multi-level, multi-dimensional Furthermore, recognizes necessity addressing ethical concerns ensuring data in research.

Language: Английский

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Weakly-semi supervised extraction of rooftop photovoltaics from high-resolution images based on segment anything model and class activation map

Applied Energy, Journal Year: 2024, Volume and Issue: 361, P. 122964 - 122964

Published: March 7, 2024

Language: Английский

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Spatial Modelling of Urban Wind Characteristics: Review of Contributions to Sustainable Urban Development

Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 737 - 737

Published: March 8, 2024

Wind, a renewable resource with growing importance in the contemporary world, is considered capable tool for addressing some of problems linked rapid urbanization, unsustainable development, and climate change. As such, understanding modelling approaches to wind characteristics cities becomes crucial. While prior reviews delve into advancements reduced-scale models computational fluid dynamics simulations, there scant literature evaluating large-scale spatial urban environments. This paper aims consolidate by conducting systematic review PRISMA protocol capture contributions sustainable development. The reviewed articles are categorized under two distinctive approaches: (a) studies adopting morphometric approach, encompassing theoretical foundations, input factors, computation methods (b) mapping centering on amalgamation microclimate analysis. findings suggest that methodologies hold considerable promise due their straightforward calculations interpretability. Nonetheless, issues related data precision accuracy challenge validity these models. also probes implications planning policymaking, advocating more

Language: Английский

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Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review

Sustainability, Journal Year: 2024, Volume and Issue: 16(3), P. 976 - 976

Published: Jan. 23, 2024

Urban air pollution is a pressing global issue driven by factors such as swift urbanization, population expansion, and heightened industrial activities. To address this challenge, the integration of Machine Learning (ML) into smart cities presents promising avenue. Our article offers comprehensive insights recent advancements in quality research, employing PRISMA method cornerstone for reviewing process, while simultaneously exploring application frequently employed ML methodologies. Focusing on supervised learning algorithms, study meticulously analyzes data, elucidating their unique benefits challenges. These techniques, including LSTM (Long Short-Term Memory), RF (Random Forest), ANN (Artificial Neural Networks), SVR (Support Vector Regression), are instrumental our quest cleaner, healthier urban environments. By accurately predicting key pollutants particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), these methods offer tangible solutions society. They enable informed decision-making planners policymakers, leading to proactive, sustainable strategies combat pollution. As result, well-being health populations significantly improved. In revised abstract, importance context explicitly emphasized, underlining role improving environments enhancing populations.

Language: Английский

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Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(3), P. 467 - 467

Published: Jan. 25, 2024

Long-term exposure to high concentrations of fine particles can cause irreversible damage people’s health. Therefore, it is extreme significance conduct large-scale continuous spatial particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution PM2.5 ground monitoring stations China uneven with a larger number southeastern China, while the sites also insufficient quality control. Remote sensing technology obtain information quickly macroscopically. possible predict based on multi-source remote data. Our study took as research area, using Pearson correlation coefficient GeoDetector select auxiliary variables. In addition, long short-term memory neural network random forest regression model were established estimation. We finally selected (R2 = 0.93, RMSE 4.59 μg m−3) our by evaluation index. across 2021 was estimated, then influence factors high-value regions explored. It clear that not only related local geographical meteorological conditions, but closely economic social development.

Language: Английский

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Land intensification use scenarios based on urban land suitability assessment of the national park

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 102, P. 105229 - 105229

Published: Jan. 23, 2024

Language: Английский

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Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Coasts, Journal Year: 2024, Volume and Issue: 4(1), P. 127 - 149

Published: Feb. 26, 2024

Mapping coastal regions is important for environmental assessment and monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) present more advantageous solutions pattern-finding tasks such as the automated detection of landscape patches heterogeneous landscapes. This study aimed to discriminate patterns along eastern coasts Mozambique ML modules Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm module ‘r.learn.train’ was used map landscapes shoreline Bight Sofala, remote sensing (RS) data at multiple temporal scales. dataset included Landsat 8-9 OLI/TIRS imagery collected dry period during 2015, 2018, 2023, which enabled evaluation dynamics. supervised classification RS rasters supported by Scikit-Learn package Python embedded GRASS Sofala characterized diverse marine ecosystems dominated swamp wetlands mangrove forests located mixed saline–fresh waters coast Mozambique. paper demonstrates advantages areas. integration Earth Observation data, processed decision tree classifier land cover characteristics recent changes ecosystem Mozambique, East Africa.

Language: Английский

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Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 639, P. 131508 - 131508

Published: June 14, 2024

Language: Английский

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A Novel Urban Heat Vulnerability Analysis: Integrating Machine Learning and Remote Sensing for Enhanced Insights

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3032 - 3032

Published: Aug. 18, 2024

Rapid urbanization and climate change exacerbate the urban heat island effect, increasing vulnerability of residents to extreme heat. Although many studies have assessed vulnerability, there is a significant lack standardized criteria references for selecting indicators, building models, validating those models. Many existing approaches do not adequately meet planning needs due insufficient spatial resolution, temporal coverage, accuracy. To address this gap, paper introduces U-HEAT framework, conceptual model analyzing vulnerability. The primary objective outline theoretical foundations potential applications U-HEAT, emphasizing its nature. This framework integrates machine learning (ML) with remote sensing (RS) identify at both long-term detailed levels. It combines retrospective forward-looking mapping continuous monitoring assessment, providing essential data developing comprehensive strategies. With active capacity, enables refinement evaluation policy impacts. presented in offers sustainable approach, aiming enhance practical analysis tools. highlights importance interdisciplinary research bolstering resilience stresses need ecosystems capable addressing complex challenges posed by increased study provides valuable insights researchers, administrators, planners effectively combat challenges.

Language: Английский

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