Leandro Leal Parente
Computer scientist with a PhD in Environmental Science working with remote sensing, data science, machine learning, high-performance computing and WebGIS applications.
Sessions
In this workshop, the participants will apply an automated machine learning framework suitable for EO data.
Approximate five billion hectares (38%) of global land area is used for agricultural system, contributing significantly to the loss of biodiversity and having a substantial impact on water resources and greenhouse gas emissions of the World. Aiming to support multi-scale environmental policies and decision making process, several land monitoring systems / products were launched in the last years, including WorldCereal, GLaNCE, Dynamic World, UMD GLAD GLCLUC, GLC_FCS30D and Global Pasture Watch. Even though all these systems / products have different advantages, limitation, constraints and resolutions (thematic, spatial and temporal), in general they have a high potential to be combined to support different land cover and land use applications at global, national and local scale. Here we present a framework able integrate global monitoring systems / products in an automated, flexible and reproducible way, taking advantages of new technologies as cloud-optimized formats and cloud services. We demonstrated it integrating different crop and pastures classes in seamless monitoring system for the tropics, allowing the users to define their own area of interest, harmonization rules and overlap criteria. The implementation is publicly available in scikit-map (https://github.com/openlandmap/scikit-map) and all input layers publicly accessible through SpatioTemporal Asset Catalog (STAC).