Trabajo de 1 de la clases semana

Trabajo #1 de la clase de la semana #5

Trabajo de Valorizaciòn S5

Project Framework

In today’s Satellite Data for Agricultural Economists session, we built spatial machine learning models using Tessera geospatial embeddings to map tea plantations in Kenya. Participants learned to download embeddings in Google Colab, prepare training data, train single- and multi-method ensemble models in R with sdm, create binary presence/absence maps, and calculate total tea area, showing how embeddings improve crop segmentation over traditional spectral bands.

This study examines whether different drug types influence fracture risk among patients. Poisson regression is used to model fracture counts and estimate incidence rate ratios.

Przykład użycia.

Today’s session in our Satellite Data for Agricultural Economists course focused on building a spatial machine learning model using the new Google Embeddings dataset to map tea plantations in Kenya. Participants learned how to generate embeddings in Google Earth Engine, prepare training data, train multi-method ensemble models in R with the sdm package, create binary presence/absence maps, and estimate total tea area—highlighting how high-dimensional embeddings can improve crop segmentation compared to traditional spectral bands.