ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring

TL;DR

ORBIT introduces a novel global ordering framework for robustly transferring information across space and time, improving surface water monitoring by handling noise and missing data in earth observation datasets.

Contribution

The paper presents ORBIT, a new method leveraging relative ordering constraints to transfer information across scales and time, addressing noise and data gaps in earth observation data.

Findings

Effective in synthetic and real-world datasets

Improves robustness to noise and missing data

Enhances surface water monitoring accuracy

Abstract

Many earth science applications require data at both high spatial and temporal resolution for effective monitoring of various ecosystem resources. Due to practical limitations in sensor design, there is often a trade-off in different resolutions of spatio-temporal datasets and hence a single sensor alone cannot provide the required information. Various data fusion methods have been proposed in the literature that mainly rely on individual timesteps when both datasets are available to learn a mapping between features values at different resolutions using local relationships between pixels. Earth observation data is often plagued with spatially and temporally correlated noise, outliers and missing data due to atmospheric disturbances which pose a challenge in learning the mapping from a local neighborhood at individual timesteps. In this paper, we aim to exploit time-independent global relationships between pixels for robust transfer of information across different scales. Specifically, we propose a new framework, ORBIT (Ordering Based Information Transfer) that uses relative ordering constraint among pixels to transfer information across both time and scales. The effectiveness of the framework is demonstrated for global surface water monitoring using both synthetic and real-world datasets.