# Handling Image and Label Resolution Mismatch in Remote Sensing

arXiv: https://arxiv.org/abs/2211.15790 · HTML: https://mlsanity.com/paper/2211.15790/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [Geometric GAN](https://mlsanity.com/paper/1705.02894/) (2017)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Deep Hierarchical Semantic Segmentation](https://mlsanity.com/paper/2203.14335/) (2022)
- [Semantic Segmentation of Remote Sensing Images with Sparse Annotations](https://mlsanity.com/paper/2101.03492/) (2021)
- [A Unified Model for Near and Remote Sensing](https://mlsanity.com/paper/1708.03035/) (2017)
- [Revisiting Near/Remote Sensing with Geospatial Attention](https://mlsanity.com/paper/2204.01807/) (2022)
- [Single View Geocentric Pose in the Wild](https://mlsanity.com/paper/2105.08229/) (2021)
- [Dynamic Traffic Modeling From Overhead Imagery](https://mlsanity.com/paper/2012.10530/) (2020)
- [FARSA: Fully Automated Roadway Safety Assessment](https://mlsanity.com/paper/1901.06013/) (2019)
- [Augmenting Depth Estimation with Geospatial Context](https://mlsanity.com/paper/2109.09879/) (2021)
- [A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery](https://mlsanity.com/paper/1810.09528/) (2018)
- [Mining self-similarity: Label super-resolution with epitomic representations](https://mlsanity.com/paper/2004.11498/) (2020)
- [Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery](https://mlsanity.com/paper/2012.04222/) (2020)
- [Towards Indirect Top-Down Road Transport Emissions Estimation](https://mlsanity.com/paper/2103.08829/) (2021)
- [RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery](https://mlsanity.com/paper/2006.08021/) (2020)
- [Single Image Cloud Detection via Multi-Image Fusion](https://mlsanity.com/paper/2007.15144/) (2020)

## Built on by (most cited first, 3 of 3)

- [Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention](https://mlsanity.com/paper/2501.10736/) (2025)
- [Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation](https://mlsanity.com/paper/2305.12691/) (2023)
- [From Pixels to Patches: Pooling Strategies for Earth Embeddings](https://mlsanity.com/paper/2603.02080/) (2026)

## Abstract (arXiv)

Though semantic segmentation has been heavily explored in vision literature, unique challenges remain in the remote sensing domain. One such challenge is how to handle resolution mismatch between overhead imagery and ground-truth label sources, due to differences in ground sample distance. To illustrate this problem, we introduce a new dataset and use it to showcase weaknesses inherent in existing strategies that naively upsample the target label to match the image resolution. Instead, we present a method that is supervised using low-resolution labels (without upsampling), but takes advantage of an exemplar set of high-resolution labels to guide the learning process. Our method incorporates region aggregation, adversarial learning, and self-supervised pretraining to generate fine-grained predictions, without requiring high-resolution annotations. Extensive experiments demonstrate the real-world applicability of our approach.