Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework

Sustainable Cities and Society, Год журнала: 2023, Номер 96, С. 104653 - 104653

Опубликована: Май 15, 2023

Climate change and rapid urbanisation exacerbated multiple urban issues threatening sustainability. Numerous studies integrated machine learning remote sensing to monitor develop mitigation strategies for However, few comparatively analysed joint applications of This paper presents a systematic review formulates framework integrating in studies. The literature analysis reveals: Most occurred Asia, Europe, North America, driven by technical ethical factors, highlighting responsible approaches data-scarce regions; Reviewed prioritised physical spatial aspects over socioeconomic requiring multi-source data comprehensive analysis; Conventional satellite, aerial images, Lidar are prevalent due affordability, quality, accessibility; Although supervised dominates, unsupervised methods algorithm selection paradigms require exploration; Integration offers accurate results thorough image processing analytics, while acquisition decision-making necessitate human supervision. provides an integrative sensing, enriching insights into their potential analytics. study informs planning policymaking promoting efficient management via enhanced integration, bolstering data-driven decision-making.

Язык: Английский

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Spatio-temporal change analysis and prediction of land use and land cover changes using CA-ANN model

Environmental Monitoring and Assessment, Год журнала: 2023, Номер 195(10)

Опубликована: Сен. 19, 2023

Язык: Английский

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Evaluation and mapping of predicted future land use changes using hybrid models in a coastal area

Ecological Informatics, Год журнала: 2023, Номер 78, С. 102324 - 102324

Опубликована: Окт. 2, 2023

Язык: Английский

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Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

Heliyon, Год журнала: 2023, Номер 9(11), С. e21253 - e21253

Опубликована: Окт. 24, 2023

The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, utilization remotely sensed data to assess effectiveness machine learning algorithms (MLAs) LULC classification change detection analysis has been limited. This study monitors analyzes in area from 1991 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost). results showed that RF algorithm produced most accurate maps three-decade period, accompanied by high kappa coefficient (0.93-0.97) compared SVM (0.91-0.95), ANN (0.91-0.96), KNN (0.92-0.96), XGBoost (0.92-0.95) algorithms. Consequently, classifier was implemented categorize all obtainable satellite images. Socioeconomic throughout these transition periods revealed results. Rangeland barren areas decreased 11.33 % (-402.03 km2) 6.68 (-236.8 km2), respectively. transmission increases 13.54 (480.18 3.43 (151.74 0.71 (25.22 occurred agricultural land, forest, built-up areas, outcomes this contribute significantly monitoring developing regions, guiding stakeholders identify vulnerable better use planning sustainable environmental protection.

Язык: Английский

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

Remote Sensing, Год журнала: 2024, Номер 16(16), С. 3032 - 3032

Опубликована: Авг. 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.

Язык: Английский

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Analyzing Urban Expansion and Land Use Dynamics in Bagua Grande and Chachapoyas Using Cloud Computing and Predictive Modeling

Earth Systems and Environment, Год журнала: 2024, Номер unknown

Опубликована: Сен. 26, 2024

Язык: Английский

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Land-Use Land-Cover Dynamics and Future Projections Using GEE, ML, and QGIS-MOLUSCE: A Case Study in Manisa

Sustainability, Год журнала: 2025, Номер 17(4), С. 1363 - 1363

Опубликована: Фев. 7, 2025

Urban expansion reshapes spatial patterns over time, leading to complex challenges such as environmental degradation, resource scarcity, and socio-economic inequality. It is critical anticipate these transformations in order devise proactive urban policies implement sustainable planning practices that minimize negative impacts on ecosystems human livelihoods. This study investigates LULC changes the rapidly urbanizing Manisa metropolitan area of Turkey using Sentinel-2 satellite imagery advanced machine learning algorithms. High-accuracy maps were generated for 2018, 2021, 2024 Random Forest, Support Vector Machine, k-Nearest Neighbors, Classification Regression Trees Among these, Forest algorithm demonstrated superior accuracy consistency distinguishing land-cover classes. Future scenarios 2027 2030 simulated Cellular Automata–Artificial Neural Network model QGIS MOLUSCE plugin. The results indicate significant growth, with built-up areas projected increase by 23.67% between 2030, accompanied declines natural resources bare land water bodies. highlights implications regarding ecological balance demonstrates importance integrating simulation models forecast use changes, enabling management. Overall, effective must be developed manage urbanization conduct a balanced manner.

Язык: Английский

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A cellular automata-based approach for spatio-temporal modeling of the city center as a complex system: The case of Kastamonu, Türkiye

Cities, Год журнала: 2022, Номер 132, С. 104073 - 104073

Опубликована: Ноя. 16, 2022

Язык: Английский

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Simulation of Land Use/Land Cover Dynamics Using Google Earth Data and QGIS: A Case Study on Outer Ring Road, Southern India

Sustainability, Год журнала: 2022, Номер 14(24), С. 16373 - 16373

Опубликована: Дек. 7, 2022

The land use and cover change dynamics is in par with the increasing growth of urban developments associated sprawl. objective study to quantify such changes caused due expansion along outer ring road using Remote Sensing GIS. maps are created for four segments namely Chikkarayapuram, Nazarathpettai, Meppur, Perungalathur years 2009, 2012, 2016, respectively. analyzed among seven classes, agriculture, barren land, residential units, industry, water body, other vegetation, marshland (swamp). Further, terms spatiotemporal aspects (area-based change), environmental (green economical factors. segment corridor 2016 (5.16%, 20.10%, 7.14%, 12.63%), (14.31%, 30.62%, 13.9%, 22.18%), (19.67%, 33.1%, 23.22%, 40.27%), areas have increased from 2009 by 20, 76,530 sq. m. agriculture regions been reduced 12, 62,700 Besides, MOLUSCE plugin open-source GIS (QGIS), simulated year 2022 were based on three (2009, 2016) which then validated ground-truth points obtained Google Earth. scope utilization Earth Engine (GEE) automated feature extraction algorithms predictive analysis.

Язык: Английский

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Predicting land cover changes and carbon stock fluctuations in Fuzhou, China: A deep learning and InVEST approach

Ecological Indicators, Год журнала: 2024, Номер 167, С. 112658 - 112658

Опубликована: Сен. 25, 2024

Язык: Английский

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