Random forest

Creating spatially complete zoning maps using machine learning

Zoning regulates land use and intensity of urban development at the county and municipal level in the United States, promoting economic growth, community health, and environmental preservation. However, limited availability of zoning data at scale …

Quantifying urban flood extent using satellite imagery and machine learning

The risk of floods from tropical storms is increasing due to climate change and human development. Maps of past flood extents can aid in planning and mitigation efforts to decrease flood risk. In 2021, Hurricane Ida slowed over the Mid-Atlantic and …

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

We developed a novel framework to map boro rice at peak season using Sentinel images. These Boro rice maps in Bangladesh showed high classification accuracy (mean of 87.90%). There was no requirement of sample data collection for training the classification model. Multi-Otsu effectively maps rice in low-data areas, outperforming other ML methods and provides stakeholders rice area statistics to support food security management.