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Found 6 papers, 3 papers with code
Urban Air Pollution Forecasting: a Machine Learning Approach leveraging Satellite Observations and Meteorological Forecasts
This research contributes to advancing our understanding of urban air quality dynamics and emphasizes the importance of amalgamating satellite, meteorological, and topographical data to develop robust pollution forecasting models.
FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models
This study introduces FMARS (Foundation Model Annotations in Remote Sensing), a methodology leveraging VHR imagery and foundation models for fast and robust annotation.
Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data
Rapid detection and well-timed intervention are essential to mitigate the impacts of wildfires.
Robust Burned Area Delineation through Multitask Learning
In this dataset, we provide annotations for multiple tasks, including burned area delineation and land cover segmentation.
A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe
The increasing frequency of catastrophic natural events, such as wildfires, calls for the development of rapid and automated wildfire detection systems.
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management.
