Jaycobson/Nasa-Rwanda-Field-Boundary-Segmentation-Challenge

# Nasa-Rwanda-Field-Boundary-Segmentation-Challenge Small farms (<2ha) produce about 35% of the world’s food, and are mostly found in low- and middle-income countries. Mapping these farms allows policy-makers to allocate resources and monitor the impacts of extreme events on food production and food security. Unfortunately, these field-level maps remain mostly unavailable in low and middle income countries, where the food insecurity risk is highest. Combining machine learning with Earth Observation data from satellites like the PlanetScope constellation can help improve agricultural monitoring, cropland mapping, and disaster risk management for these small farms. In this challenge, the goal is to classify crop field boundaries using multispectral observations collected by PlanetScope, available through the NICFI basemaps program. Fields are located in Rwanda’s Eastern Province (Intara y’lburasirazuba) and spread over the districts of Gatsibo and Nyagatare. The NASA Harvest Rwanda field boundary training dataset was generated by TaQadam through a team of annotators, and curated by Radiant Earth Foundation.