AIMC Topic: Remote Sensing Technology

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Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data.

Environmental science and pollution research international
Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challe...

Maize yield estimation in Northeast China's black soil region using a deep learning model with attention mechanism and remote sensing.

Scientific reports
Accurate prediction of maize yields is crucial for effective crop management. In this paper, we propose a novel deep learning framework (CNNAtBiGRU) for estimating maize yield, which is applied to typical black soil areas in Northeast China. This fra...

Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation.

Sensors (Basel, Switzerland)
This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were...

Sentinel-2 imagery coupled with machine learning to modelling water turbidity in the Doce River Basin, Brazil.

Environmental monitoring and assessment
Remote sensing and machine learning are techniques that can be used to monitor water quality properties, surpassing the limitations of the conventional techniques. Turbidity is an important water quality property directly influenced by the Fundão dam...

Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.

Sensors (Basel, Switzerland)
Tea ( L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan's annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and qual...

Low-cost sensor-based algal bloom labeling: a comparative study of SVM and logic methods.

Environmental monitoring and assessment
This study explores a low-cost sensor system for real-time algae bloom detection and water management. Harmful algal blooms (HABs) threaten water quality, ecosystems, and public health. Traditional detection methods, like satellite imagery and unmann...

Forest fire susceptibility mapping using multi-criteria decision making and machine learning models in the Western Ghats of India.

Journal of environmental management
Forest fires have significantly increased over the last decade due to shifts in rainfall patterns, warmer summers, and long spells of dry weather events in the coastal regions. Assessment of susceptibility to forest fires has become an important mana...

Deep-learning analysis of greenspace and metabolic syndrome: A street-view and remote-sensing approach.

Environmental research
Evidence linking greenspace exposure to metabolic syndrome (MetS) remains sparse and inconsistent. This exploratory study evaluate the relationship between green visibility index (GVI) and normalized difference vegetation index (NDVI) with MetS preva...

Hyperspectral discrimination of vegetable crops grown under organic and conventional cultivation practices: a machine learning approach.

Scientific reports
A verifiable and regional level method for mapping crops cultivated under organic practices holds significant promise for certifying and ensuring the quality of farm products marketed as organic. The prevailing method for the identification of organi...

Remote sensing of alcohol consumption using machine learning speckle pattern analysis.

Journal of biomedical optics
SIGNIFICANCE: Alcohol consumption monitoring is essential for forensic and healthcare applications. While breath and blood alcohol concentration sensors are currently the most common methods, there is a growing need for faster, non-invasive, and more...