# High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest

arXiv: https://arxiv.org/abs/2101.01154 · HTML: https://mlsanity.com/paper/2101.01154/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mining self-similarity: Label super-resolution with epitomic representations](https://mlsanity.com/paper/2004.11498/) (2020)

## Built on by (most cited first, 1 of 1)

- [Pushing the Limits of Vision-Language Models in Remote Sensing without Human Annotations](https://mlsanity.com/paper/2409.07048/) (2024)

## Abstract (arXiv)

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.