# Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities

arXiv: https://arxiv.org/abs/2002.08254 · HTML: https://mlsanity.com/paper/2002.08254/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

## Built on by (most cited first, 15 of 15)

- [Semantic Segmentation using Vision Transformers: A survey](https://mlsanity.com/paper/2305.03273/) (2023)
- [SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal](https://mlsanity.com/paper/2201.09613/) (2022)
- [Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing](https://mlsanity.com/paper/2104.01375/) (2021)
- [Segmenting across places: The need for fair transfer learning with satellite imagery](https://mlsanity.com/paper/2204.04358/) (2022)
- [AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensing](https://mlsanity.com/paper/2409.17453/) (2024)
- [RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning](https://mlsanity.com/paper/2110.01919/) (2021)
- [Remote Sensing Image Classification with the SEN12MS Dataset](https://mlsanity.com/paper/2104.00704/) (2021)
- [A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability](https://mlsanity.com/paper/2202.03342/) (2022)
- [Monitoring Urban Forests from Auto-Generated Segmentation Maps](https://mlsanity.com/paper/2206.06948/) (2022)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)
- [Weak Labeling for Cropland Mapping in Africa](https://mlsanity.com/paper/2401.07014/) (2024)
- [Investigating Imbalances Between SAR and Optical Utilization for Multi-Modal Urban Mapping](https://mlsanity.com/paper/2304.05080/) (2023)
- [Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks](https://mlsanity.com/paper/2309.03367/) (2023)
- [Learning Semantic Segmentation with Query Points Supervision on Aerial Images](https://mlsanity.com/paper/2309.05490/) (2023)
- [Comparison of Segmentation Methods in Remote Sensing for Land Use Land Cover](https://mlsanity.com/paper/2507.18099/) (2025)

## Abstract (arXiv)

Fully automatic large-scale land cover mapping belongs to the core challenges addressed by the remote sensing community. Usually, the basis of this task is formed by (supervised) machine learning models. However, in spite of recent growth in the availability of satellite observations, accurate training data remains comparably scarce. On the other hand, numerous global land cover products exist and can be accessed often free-of-charge. Unfortunately, these maps are typically of a much lower resolution than modern day satellite imagery. Besides, they always come with a significant amount of noise, as they cannot be considered ground truth, but are products of previous (semi-)automatic prediction tasks. Therefore, this paper seeks to make a case for the application of weakly supervised learning strategies to get the most out of available data sources and achieve progress in high-resolution large-scale land cover mapping. Challenges and opportunities are discussed based on the SEN12MS dataset, for which also some baseline results are shown. These baselines indicate that there is still a lot of potential for dedicated approaches designed to deal with remote sensing-specific forms of weak supervision.