High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest
This paper provides baseline methods for land cover change detection, which is useful for participants in the 2021 IEEE GRSS Data Fusion Contest.
This paper presents simple baseline algorithms for detecting high-resolution (1m/pixel) land cover changes using multi-resolution imagery and label data, specifically for the 2021 IEEE GRSS Data Fusion Contest. The authors discuss several baseline models and suggest future research directions for this task.
We present simple algorithms for land cover change detection in the 2021 IEEE GRSS Data Fusion Contest. The task of the contest is to create high-resolution (1m / pixel) land cover change maps of a study area in Maryland, USA, given multi-resolution imagery and label data. We study several baseline models for this task and discuss directions for further research. See https://dfc2021.blob.core.windows.net/competition-data/dfc2021_index.txt for the data and https://github.com/calebrob6/dfc2021-msd-baseline for an implementation of these baselines.