Utilizing UAV and orthophoto data with bathymetric LiDAR in google earth engine for coastal cliff degradation assessment

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 3, 2025

This study introduces a novel methodology for estimating and analysing coastal cliff degradation, using machine learning remote sensing data. Degradation refers to both natural abrasive processes damage reinforcement structures caused by events. We utilized orthophotos LiDAR data in green near-infrared wavelengths identify zones impacted storms extreme weather events that initiated mass movement processes. Our approach included change detection analysis estimate eroded areas. Next, applying Random Forest classifier within Google Earth Engine, we evaluated the importance of features detecting these degraded zones. tested algorithm's performance datasets varying resolutions (10 cm, 20 50 100 cm), UAV dataset acquired two years later validate results. The achieved an overall accuracy approximately 90% across all datasets. findings indicate DEM products are similarly important, while reflectance maps suggest red play significant role identifying degradation. These results it is feasible monitor degradation disasters diverse sensors single training framework.

Language: Английский

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Advanced image segmentation for precision agriculture using CNN-GAT fusion and fuzzy C-means clustering

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109431 - 109431

Published: Sept. 11, 2024

Language: Английский

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Multispectral Semantic Segmentation for Land Cover Classification: An Overview

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 14295 - 14336

Published: Jan. 1, 2024

Land cover classification (LCC) is a process used to categorize the Earth's surface into distinct land types. This vital for environmental conservation, urban planning, agricultural management, and climate change research, providing essential data sustainable decision-making. The use of multispectral imaging (MSI), which captures beyond visible spectrum, has emerged as one most utilized image modalities addressing this task. Additionally, semantic segmentation techniques play role in domain, enabling precise delineation labeling classes within imagery. integration these three concepts given rise an intriguing ever-evolving research field, witnessing continuous advancements aimed at enhancing (MSSS) methods LCC. Given dynamic nature there need thorough examination latest trends understand its evolving landscape. Therefore, paper presents review current aspects field MSSS LCC, following key points: (1) prevalent datasets acquisition methods, (2) preprocessing managing MSI data, (3) typical metrics evaluation criteria assessing performance (4) methodologies employed, (5) spectral bands spectrum commonly utilized. Through analysis, our objective provide valuable insights state contributing ongoing development understanding while also perspectives future directions.

Language: Английский

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Sunflower-YOLO: Detection of sunflower capitula in UAV remote sensing images

European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 160, P. 127332 - 127332

Published: Sept. 1, 2024

Language: Английский

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

Remote Sensing in Earth Systems Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

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Tendencias Actuales en la aplicación del Bigdata y Agricultura Inteligente un Enfoque Bibliométrico

Estudios y Perspectivas Revista Científica y Académica, Journal Year: 2025, Volume and Issue: 5(1), P. 310 - 332

Published: Jan. 29, 2025

La necesidad por alimentar a la población mundial se ha convertido en un desafío nuestra sociedad. producción agrícola requiere de tecnificación que le permita cumplir con esta población. En este sentido Big Data convierte una las herramientas relevantes permiten gestionar y optimizar los recursos naturales e insumos agrícolas convirtiendo actividades el campo agricultura inteligente innova mejora resultados producción. El presente trabajo busca responder pregunta ¿Cuáles son tendencias actuales aplicación bigdata inteligente?. A través análisis bibliométrico buscamos interrogante determinar brecha investigación. Los alcanzados nos muestran 7 brechas investigación: bigdata, blockchain, smart farming, security, artificial intelligence internet of things, estos determinantes áreas investigación crecimiento requieren ser exploradas sus permitirán mejorar producción, alto nivel control su desarrollo sostenible sustentable.

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Integrating deep learning algorithms for forecasting evapotranspiration and assessing crop water stress in agricultural water management

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124363 - 124363

Published: Jan. 31, 2025

Language: Английский

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Spectral Variability Analysis of Lupinus mutabilis Sweet Under Nanofertilizer and Chelate Application Through Spectroscopy and Unmanned Aerial Vehicle (UAV) Multispectral Images

Agronomy, Journal Year: 2025, Volume and Issue: 15(2), P. 469 - 469

Published: Feb. 14, 2025

Lupin is an Andean legume that has gained importance in Ecuador due to the protein content its grain. Nonetheless, recent times production of lupin been affected by inadequate nutritional management. In order avoid such circumstances, current study spectrally analyzed cultivation under application nanofertilizers and Fe Zn chelates, within two controlled trials, using a radiometer spectrum, active crop sensor multispectral mounted on UAV. Vegetation indices were generated subsequently statistically ANOVA Tukey tests. field trial, treatments lacked indication significant improvements, while greenhouse nanofertilizer indicated better results compared control treatments. However, it was also determined at concentration 540 ppm demonstrated efficiency conditions, which could not be achieved field. Furthermore, chelate treatment presented certain degree toxicity for plant.

Language: Английский

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Deforestation and Forest Monitoring With CNN and RNN

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 191 - 208

Published: Jan. 10, 2025

Deforestation poses a significant threat to global biodiversity and climate stability, necessitating effective monitoring management strategies. It is highly necessary for an strategy mitigate deforestation as it possesses potential stability biodiversity. A novel deep learning technique with Convolutional Neural Networks (CNNs) Recurrent (RNNs) proposed identify the forest. CNN deployed deforested areas by extracting spatial features RNN are used capture patterns of forest dynamics processing time series satellite data. This mechanism where temporal analysis done prediction.

Language: Английский

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Enriching the metadata of map images: a deep learning approach with geographic information systems-based data augmentation

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 181 - 203

Published: Jan. 1, 2025

Language: Английский

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