Tutorials/Post - Remote Sensing, GIS, Earth System, Geo-AI/ML

Category: Machine Learning Page 1 of 2


Data Drive Landslide Hazard Assessment (LHASA) Model for the Mekong Region

In this presentation, Dr. Nishan Kumar Biswas, Dr. Pukar Amatya, and Dr. Thomas Stanley from the Goddard Space Flight Center presented the data-driven Landslide Hazard Assessment (LHASA) Model for the Lower Mekong Region. The LHASA uses the SALaD (Semi-Automatic Landslide Detection) landslide mapping system and the XGBoost algorithm to derive the hazard assessment layer.


How many abstracts can an American Geophysical Union (AGU) session get?

I am helping a colleague here at the NASA-SERVIR Science Coordination Office (SCO) in Huntsville, Alabama, to organize a session on Machine Learning (titled Addressing environmental challenges and sustainable development through Earth science applications utilizing Machine Learning). The session got a massive 44 abstracts. I was curious to see how many abstracts a session … CONTINUE READING

Deep Learning Approach for Monitoring Trees outside of Forests

John Brandt, a Data Science Associate at the World Research Institute (WRI), discusses the methodology and implications of his paper “A global method to identify trees outside of closed-canopy forests with medium resolution satellite imagery.”

View the presentation slides here. Read the paper here.… CONTINUE READING

Surface Water Detected by the BCE Algorithm

Deep Learning approach for Sentinel-1 Surface Water Mapping leveraging Google Earth Engine

Satellite remote sensing plays an important role in mapping the location and extent of surface water. A variety of approaches are available for mapping surface water, but deep learning approaches are not commonplace as they are ’data hungry’ and require large amounts of computational resources. However, with the availability of various satellite sensors and rapid … CONTINUE READING

Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine

Air pollution from burning sugarcane is an important environmental issue in Thailand. Knowing the location and extent of sugarcane plantations would help in formulating effective strategies to reduce burning. High-resolution satellite imagery combined with deep-learning technologies can be effective to map sugarcane with high precision. However, land cover mapping using high-resolution data and computationally intensive … CONTINUE READING

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