AIMC Topic: Satellite Imagery

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ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012-2023 with Landsat series data.

Environmental monitoring and assessment
Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from differe...

A Vision Transformer Model for Convolution-Free Multilabel Classification of Satellite Imagery in Deforestation Monitoring.

IEEE transactions on neural networks and learning systems
Understanding the dynamics of deforestation and land uses of neighboring areas is of vital importance for the design and development of appropriate forest conservation and management policies. In this article, we approach deforestation as a multilabe...

State-of-the-Art Deep Learning Methods for Objects Detection in Remote Sensing Satellite Images.

Sensors (Basel, Switzerland)
Object detection in remotely sensed satellite images is critical to socio-economic, bio-physical, and environmental monitoring, necessary for the prevention of natural disasters such as flooding and fires, socio-economic service delivery, and genera...

Higher depression risks in medium- than in high-density urban form across Denmark.

Science advances
Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensio...

A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images.

Sensors (Basel, Switzerland)
Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth's surface for more than 4 years and has become an important data source for a large ...

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

Computational intelligence and neuroscience
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...

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
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...

Automatic Target Detection from Satellite Imagery Using Machine Learning.

Sensors (Basel, Switzerland)
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 ...

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery.

Computational intelligence and neuroscience
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...

Crop loss identification at field parcel scale using satellite remote sensing and machine learning.

PloS one
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...