# A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, strategies, and challenges

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-survey-of-sample-efficient-deep-learning-for-change-detection-in-remote/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Lei Ding,Danfeng Hong,Maofan Zhao,Hongruixuan Chen,Chenyu Li,Jie Deng,Naoto Yokoya,Lorenzo Bruzzone,Jocelyn Chanussot |
| Citations | 43 |
| DOI | 10.1109/mgrs.2025.3533605 |
| Fields | Earth and Planetary Sciences,Engineering,Environmental Science |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2502.02835 |
| OpenAlex ID | https://openalex.org/W4407212099 |
| Type | article |
| Year | 2025 |

## Paper authors

- [Jie Deng](https://scholariq.org/researchers/jie-deng-2/)

## Paper primary topic

- [Remote-Sensing Image Classification](https://scholariq.org/topics/remote-sensing-image-classification/)

## Paper topics

- [Remote-Sensing Image Classification](https://scholariq.org/topics/remote-sensing-image-classification/)
- [Remote Sensing in Agriculture](https://scholariq.org/topics/remote-sensing-in-agriculture/)
- [Remote Sensing and Land Use](https://scholariq.org/topics/remote-sensing-and-land-use/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.