Advances in sensor fusion and gap-filling for satellite based analysis ready surface reflectance data

This project aims to develop a physics-guided deep learning framework that can be used to further enhance the robustness of surface reflectance forecasting in Planet Fusion. We want to improve and refine existing temporal-driven gap-filling techniques in handling extensive cloud cover and dynamic land changes to avoid delays and reduced reliability of delivered insights to the extent possible. The integration of domain knowledge with advanced AI techniques will allow Planet to deliver more robust, continuous, and predictive Earth observation products. This addresses a core need for the company and its clients in agriculture, climate resilience, and land management, who increasingly demand reliable uninterrupted near real-time surface reflectance solutions.

Faculty Supervisor:

Claudia Wagner-Riddle

Student:

Partner:

Planet Labs Geomatics Corp

Discipline:

Earth science

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program: