AIMC Topic: Satellite Imagery
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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...
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...
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...
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture.
Sensors (Basel, Switzerland)
Apr 29, 2020
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated descr...
Deep Learning to Unveil Correlations between Urban Landscape and Population Health.
Sensors (Basel, Switzerland)
Apr 8, 2020
The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in ...
Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data.
PloS one
Mar 11, 2020
Nowcasting of precipitation is a difficult spatiotemporal task because of the non-uniform characterization of meteorological structures over time. Recently, convolutional LSTM has been shown to be successful in solving various complex spatiotemporal ...
Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery.
Scientific reports
Nov 27, 2019
Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopi...
Spatiotemporal dynamics of urbanization and cropland in the Nile Delta of Egypt using machine learning and satellite big data: implications for sustainable development.
Environmental monitoring and assessment
Nov 23, 2019
The Nile Delta of Egypt is increasingly facing sustainability threats, due to a combination of nature- and human-induced changes in land cover and land use. In this paper, an analysis of big time series data from remotely sensed satellite images and ...
Using satellite-measured relative humidity for prediction of Metisa plana's population in oil palm plantations: A comparative assessment of regression and artificial neural network models.
PloS one
Oct 18, 2019
Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. T...
Aerial-trained deep learning networks for surveying cetaceans from satellite imagery.
PloS one
Oct 1, 2019
Most cetacean species are wide-ranging and highly mobile, creating significant challenges for researchers by limiting the scope of data that can be collected and leaving large areas un-surveyed. Aerial surveys have proven an effective way to locate a...
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