Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives
Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …
tools and data fusion strategies has recently opened new perspectives for environmental …
A review of earth artificial intelligence
Z Sun, L Sandoval, R Crystal-Ornelas… - Computers & …, 2022 - Elsevier
In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …
[HTML][HTML] Automation and digitization of agriculture using artificial intelligence and internet of things
The growing population and effect of climate change have put a huge responsibility on the
agriculture sector to increase food-grain production and productivity. In most of the countries …
agriculture sector to increase food-grain production and productivity. In most of the countries …
Temporal convolutional neural network for the classification of satellite image time series
Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite
Image Time Series (SITS) of the world. These image series are a key component of …
Image Time Series (SITS) of the world. These image series are a key component of …
Temporal convolutional networks for the advance prediction of ENSO
Abstract El Niño-Southern Oscillation (ENSO), which is one of the main drivers of Earth's
inter-annual climate variability, often causes a wide range of climate anomalies, and the …
inter-annual climate variability, often causes a wide range of climate anomalies, and the …
Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …
investigating aggregated classes. The increase in data with a very high spatial resolution …
Remote-sensing data and deep-learning techniques in crop mapping and yield prediction: A systematic review
Reliable and timely crop-yield prediction and crop mapping are crucial for food security and
decision making in the food industry and in agro-environmental management. The global …
decision making in the food industry and in agro-environmental management. The global …
A novel CNN-LSTM-based approach to predict urban expansion
Time-series remote sensing data offer a rich source of information that can be used in a wide
range of applications, from monitoring changes in land cover to surveillance of crops …
range of applications, from monitoring changes in land cover to surveillance of crops …
A review of practical ai for remote sensing in earth sciences
Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for
revolutionizing data analysis and applications in many domains of Earth sciences. This …
revolutionizing data analysis and applications in many domains of Earth sciences. This …
Rice crop detection using LSTM, Bi-LSTM, and machine learning models from Sentinel-1 time series
H Crisóstomo de Castro Filho… - Remote Sensing, 2020 - mdpi.com
The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological
cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel …
cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel …