# State-of-the-Art Deep Learning Methods for Objects Detection in Remote Sensing Satellite Images

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/state-of-the-art-deep-learning-methods-for-objects-detection-in-remote-sensing/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Adekanmi Adeyinka Adegun,Jean Vincent Fonou-Dombeu,Serestina Viriri,John Odindi |
| Citations | 42 |
| DOI | 10.3390/s23135849 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/1424-8220/23/13/5849/pdf?version=1687677488 |
| OpenAlex ID | https://openalex.org/W4382135374 |
| PMID | 37447699 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Adekanmi Adeyinka Adegun](https://scholariq.org/researchers/adekanmi-adeyinka-adegun/)

## Paper journal

- [Sensors](https://scholariq.org/journals/sensors/)

## 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/)
- [Automated Road and Building Extraction](https://scholariq.org/topics/automated-road-and-building-extraction/)
- [Advanced Neural Network Applications](https://scholariq.org/topics/advanced-neural-network-applications/)

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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.