# Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping

arXiv: https://arxiv.org/abs/2305.07586 · HTML: https://mlsanity.com/paper/2305.07586/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation.
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 12 May 2023.

## Placed in

- Knowledge distillation / [Detection and dense prediction distillation](https://mlsanity.com/distill/detection-and-dense-prediction-distillation/)

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications](https://mlsanity.com/paper/2304.05750/) (2023)
- [Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection](https://mlsanity.com/paper/2304.04709/) (2023)
- [SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything](https://mlsanity.com/paper/2304.06022/) (2023)
- [When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation](https://mlsanity.com/paper/2304.08506/) (2023)
- [Learning to "Segment Anything" in Thermal Infrared Images through Knowledge Distillation with a Large Scale Dataset SATIR](https://mlsanity.com/paper/2304.07969/) (2023)

## Built on by (most cited first, 16 of 16)

- [Review of Large Vision Models and Visual Prompt Engineering](https://mlsanity.com/paper/2307.00855/) (2023)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints](https://mlsanity.com/paper/2312.02464/) (2023)
- [RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation](https://mlsanity.com/paper/2307.00997/) (2023)
- [When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System](https://mlsanity.com/paper/2309.06799/) (2023)
- [When SAM Meets Sonar Images](https://mlsanity.com/paper/2306.14109/) (2023)
- [Applications of Knowledge Distillation in Remote Sensing: A Survey](https://mlsanity.com/paper/2409.12111/) (2024)
- [Detecting Wildfires on UAVs with Real-time Segmentation Trained by Larger Teacher Models](https://mlsanity.com/paper/2408.10843/) (2024)
- [Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul](https://mlsanity.com/paper/2307.12674/) (2023)
- [ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing Segmentation With Segment Anything Model](https://mlsanity.com/paper/2406.10855/) (2024)
- [Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation](https://mlsanity.com/paper/2311.15463/) (2023)
- [SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation](https://mlsanity.com/paper/2406.14819/) (2024)
- [ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation](https://mlsanity.com/paper/2401.02326/) (2024)
- [DINOv2 Rocks Geological Image Analysis: Classification, Segmentation, and Interpretability](https://mlsanity.com/paper/2407.18100/) (2024)
- [Synthetic Geology: Structural Geology Meets Deep Learning](https://mlsanity.com/paper/2506.11164/) (2025)
- [Using Global Land Cover Product as Prompt for Cropland Mapping via Visual Foundation Model](https://mlsanity.com/paper/2310.10219/) (2023)

## Abstract (arXiv)

Planetary science research involves analysing vast amounts of remote sensing data, which are often costly and time-consuming to annotate and process. One of the essential tasks in this field is geological mapping, which requires identifying and outlining regions of interest in planetary images, including geological features and landforms. However, manually labelling these images is a complex and challenging task that requires significant domain expertise and effort. To expedite this endeavour, we propose the use of knowledge distillation using the recently introduced cutting-edge Segment Anything (SAM) model. We demonstrate the effectiveness of this prompt-based foundation model for rapid annotation and quick adaptability to a prime use case of mapping planetary skylights. Our work reveals that with a small set of annotations obtained with the right prompts from the model and subsequently training a specialised domain decoder, we can achieve satisfactory semantic segmentation on this task. Key results indicate that the use of knowledge distillation can significantly reduce the effort required by domain experts for manual annotation and improve the efficiency of image segmentation tasks. This approach has the potential to accelerate extra-terrestrial discovery by automatically detecting and segmenting Martian landforms.