AIMC Topic: Satellite Imagery

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Long-term water quality assessment in coastal and inland waters: An ensemble machine-learning approach using satellite data.

Marine pollution bulletin Nov 16, 2024
Accurate estimation of coastal and in-land water quality parameters is important for managing water resources and meeting the demand of sustainable development goals. The water quality monitoring based on discrete water sample analysis is limited to ...
Machine Learning Environmental Monitoring Remote Sensing Technology Chlorophyll A Water Quality Satellite Imagery Seawater
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Downscaling MODIS evapotranspiration into finer resolution using machine learning approach on a small scale, Ribb watershed, Ethiopia.

Environmental monitoring and assessment Nov 8, 2024
By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agr...
Plant Transpiration Seasons Machine Learning Environmental Monitoring Satellite Imagery Agriculture Ethiopia
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Plastic debris detection along coastal waters using Sentinel-2 satellite data and machine learning techniques.

Marine pollution bulletin Oct 10, 2024
Few studies have effectively shown how to use satellites that gather optical data to monitor plastic debris in the marine environment. For the first time, floating macro-plastics distinguishable from seaweed are identified in optical data from the Eu...
Machine Learning Plastics Waste Products Satellite Imagery Brazil Environmental Monitoring
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Towards sustainable coastal management: aerial imagery and deep learning for high-resolution mapping.

PeerJ Sep 23, 2024
The massive arrival of pelagic on the coasts of several countries of the Atlantic Ocean began in 2011 and to date continues to generate social and environmental challenges for the region. Therefore, knowing the distribution and quantity of in the o...
Satellite Imagery Atlantic Ocean Mexico Deep Learning Bathing Beaches Humans Environmental Monitoring Conservation of Natural Resources Algorithms Sargassum
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Comparison of conventional and machine learning regression models for accurate prediction of selected optical active components - A case study: The Gulf of Izmit.

Marine pollution bulletin Sep 14, 2024
This study hypothesizes that advanced machine learning (ML) models can more accurately predict certain critical water quality parameters in marine environments compared to conventional regression techniques. We specifically evaluated the spatio-tempo...
Seawater Chlorophyll Environmental Monitoring Water Quality Chlorophyll A Satellite Imagery Machine Learning Ecosystem
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STransU2Net: Transformer based hybrid model for building segmentation in detailed satellite imagery.

PloS one Sep 12, 2024
As essential components of human society, buildings serve a multitude of functions and significance. Convolutional Neural Network (CNN) has made remarkable progress in the task of building extraction from detailed satellite imagery, owing to the pote...
Humans Neural Networks, Computer Satellite Imagery Algorithms Image Processing, Computer-Assisted
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Mapping of temperate upland habitats using high-resolution satellite imagery and machine learning.

Environmental monitoring and assessment Aug 31, 2024
Upland habitats provide vital ecological services, yet they are highly threatened by natural and anthropogenic stressors. Monitoring these vulnerable habitats is fundamental for conservation and involves determining information about their spatial lo...
Satellite Imagery Machine Learning Conservation of Natural Resources Ecosystem Remote Sensing Technology Ireland Environmental Monitoring
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Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Environmental science & technology Aug 3, 2024
Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for clim...
Satellite Imagery Atmosphere Environmental Monitoring Aerosols Deep Learning
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Utilizing deep learning algorithms for automated oil spill detection in medium resolution optical imagery.

Marine pollution bulletin Jul 30, 2024
This study evaluates the performance of three typical convolutional neural network based deep learning algorithms for oil spill detection using medium-resolution optical satellite imagery from Sentinel-2 MSI, Landsat-8 OLI, and Landsat-9 OLI2. Oil sl...
Neural Networks, Computer Algorithms Deep Learning Environmental Monitoring Petroleum Pollution Remote Sensing Technology Satellite Imagery
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Advancements in urban scene segmentation using deep learning and generative adversarial networks for accurate satellite image analysis.

PloS one Jul 18, 2024
In the urban scene segmentation, the "image-to-image translation issue" refers to the fundamental task of transforming input images into meaningful segmentation maps, which essentially involves translating the visual information present in the input ...
Humans Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Satellite Imagery Deep Learning
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