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Found 6 papers, 1 papers with code
Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran
Alternatively, high resolution satellite Aerosol Optical Depth (AOD) data can be employed for high resolution mapping of PM2. 5.
Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning
In this research, We explore the effectiveness of conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, in addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data.
Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran
For this purpose, AOD products were fused by machine learning algorithms using different fusion strategies at two levels: the data level and the decision level.
Segment-based fusion of multi-sensor multi-scale satellite soil moisture retrievals
Active, passive, and optic data integration could be a comprehensive solution for exploiting the advantages of different sensors aimed at preparing soil moisture maps.
A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data
The output of the framework was a machine learning model trained to predict PM2. 5 from MAIAC AOD retrievals and meteorological data.
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques.
BIG-bench Machine Learning
Cultural Vocal Bursts Intensity Prediction
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