AIMC Topic: Satellite Imagery

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An Efficient Deep Learning Mechanism for the Recognition of Olive Trees in Jouf Region.

Computational intelligence and neuroscience Aug 31, 2022
Olive trees grow all over the world in reasonably moderate and dry climates, making them fortunate and medicinal. Pesticides are required to improve crop quality and productivity. Olive trees have had important cultural and economic significance sinc...
Olea Satellite Imagery Deep Learning Remote Sensing Technology
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Prediction of MODIS land surface temperature using new hybrid models based on spatial interpolation techniques and deep learning models.

Environmental science and pollution research international May 6, 2022
Land surface temperature (LST) prediction is of great importance for climate change, ecology, environmental and industrial studies. These studies require accurate LST map predictions considering both spatial and temporal dynamics. In this study, mult...
Neural Networks, Computer Temperature Deep Learning Spatial Analysis Satellite Imagery
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Automatic Target Detection from Satellite Imagery Using Machine Learning.

Sensors (Basel, Switzerland) Feb 2, 2022
Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons ...
Satellite Imagery Software Algorithms Neural Networks, Computer Machine Learning
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A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery.

Computational intelligence and neuroscience Jan 15, 2022
Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of...
Olea Satellite Imagery Deep Learning Image Processing, Computer-Assisted Databases, Factual
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Crop loss identification at field parcel scale using satellite remote sensing and machine learning.

PloS one Dec 16, 2021
Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define...
Machine Learning Seasons Crops, Agricultural Finland Satellite Imagery
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Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image.

PloS one Oct 29, 2021
This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convol...
Satellite Imagery Neural Networks, Computer Pattern Recognition, Automated
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Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks.

Sensors (Basel, Switzerland) Aug 21, 2021
The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not ...
Image Processing, Computer-Assisted Satellite Imagery Algorithms Neural Networks, Computer
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A novel semi-supervised framework for UAV based crop/weed classification.

PloS one May 10, 2021
Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agricul...
Crops, Agricultural Satellite Imagery Weed Control Remote Sensing Technology Aircraft Algorithms Plant Weeds Deep Learning Agriculture
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On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network.

PloS one Sep 28, 2020
This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's To...
Satellite Imagery Nicotiana Triticum Pakistan Deep Learning Data Accuracy Humans Agriculture Vegetables
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A deep learning approach to identify smoke plumes in satellite imagery in near-real time for health risk communication.

Journal of exposure science & environmental epidemiology Jul 27, 2020
BACKGROUND: Wildland fire (wildfire; bushfire) pollution contributes to poor air quality, a risk factor for premature death. The frequency and intensity of wildfires are expected to increase; improved tools for estimating exposure to fire smoke are v...
Satellite Imagery Smoke Humans Fires Australia Communication Air Pollutants Deep Learning
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