# Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels

arXiv: https://arxiv.org/abs/2403.02746 · HTML: https://mlsanity.com/paper/2403.02746/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 58 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [CoAtNet: Marrying Convolution and Attention for All Data Sizes](https://mlsanity.com/paper/2106.04803/) (2021)
- [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://mlsanity.com/paper/2103.10697/) (2021)
- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1503.01640/) (2015)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models](https://mlsanity.com/paper/1807.05713/) (2018)
- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping](https://mlsanity.com/paper/2210.10732/) (2022)
- [Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.09653/) (2022)
- [Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis](https://mlsanity.com/paper/2001.01306/) (2020)
- [Weakly Supervised Semantic Segmentation using Out-of-Distribution Data](https://mlsanity.com/paper/2203.03860/) (2022)
- [A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2104.12137/) (2021)
- [LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery](https://mlsanity.com/paper/2005.02264/) (2020)
- [Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models](https://mlsanity.com/paper/1803.06253/) (2018)
- [Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network](https://mlsanity.com/paper/2307.12309/) (2023)

## Built on by (most cited first, 8 of 8)

- [CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models](https://mlsanity.com/paper/2411.18145/) (2024)
- [The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation](https://mlsanity.com/paper/2503.15683/) (2025)
- [Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework](https://mlsanity.com/paper/2404.12721/) (2024)
- [Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data](https://mlsanity.com/paper/2405.05133/) (2024)
- [LightFormer: A lightweight and efficient decoder for remote sensing image segmentation](https://mlsanity.com/paper/2504.10834/) (2025)
- [Efficient Generalization via Multimodal Co-Training under Data Scarcity and Distribution Shift](https://mlsanity.com/paper/2510.07509/) (2025)
- [A Dual-Branch Local-Global Framework for Cross-Resolution Land Cover Mapping](https://mlsanity.com/paper/2512.19990/) (2025)
- [MapSR: Prompt-Driven Land Cover Map Super-Resolution via Vision Foundation Models](https://mlsanity.com/paper/2604.14582/) (2026)

## Abstract (arXiv)

Large-scale high-resolution (HR) land-cover mapping is a vital task to survey the Earth's surface and resolve many challenges facing humanity. However, it is still a non-trivial task hindered by complex ground details, various landforms, and the scarcity of accurate training labels over a wide-span geographic area. In this paper, we propose an efficient, weakly supervised framework (Paraformer) to guide large-scale HR land-cover mapping with easy-access historical land-cover data of low resolution (LR). Specifically, existing land-cover mapping approaches reveal the dominance of CNNs in preserving local ground details but still suffer from insufficient global modeling in various landforms. Therefore, we design a parallel CNN-Transformer feature extractor in Paraformer, consisting of a downsampling-free CNN branch and a Transformer branch, to jointly capture local and global contextual information. Besides, facing the spatial mismatch of training data, a pseudo-label-assisted training (PLAT) module is adopted to reasonably refine LR labels for weakly supervised semantic segmentation of HR images. Experiments on two large-scale datasets demonstrate the superiority of Paraformer over other state-of-the-art methods for automatically updating HR land-cover maps from LR historical labels.