Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends

By Thorsten Hoeser, Claudia Kuenzer. Remote Sensing, 2020

330 citations recorded by the source corpus.

Deep learning (DL) has great influence on large parts of science and increasingly established itself as an adaptive method for new challenges in the field of Earth observation (EO). Nevertheless, the entry barriers for EO researchers are high due to the dense and rapidly developing field mainly driven by advances in computer vision (CV). To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). The survey starts in 2012, when a CNN set new standards in image recognition, and lasts until late 2019. Thereby, we highlight the connections between the most important CNN architectures and cornerstones coming from CV in order to alleviate the evaluation of modern DL models. Furthermore, we briefly outline the evolution of the most popular DL frameworks and provide a summary of datasets in EO. By discussing well performing DL architectures on these datasets as well as reflecting on advances made in CV and their impact on future research in EO, we narrow the gap between the reviewed, theoretical concepts from CV and practical application in EO.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/a9292632c2271e5e53326f47c6e4dd589dd87eca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David Small
2. Richard Bamler
3. Mohsin Ali
4. Lichuan Gu
5. Xiaohui Yuan
6. Yuanyuan Wang
7. Feng Xu
8. Jianfang Shi

In Computer Vision and Pattern Recognition

1. Lichao Mou
2. Xiaoxiang Zhu
3. Richard Bamler
4. Mohsin Ali
5. Lichuan Gu
6. Xiaohui Yuan
7. Yuanyuan Wang
8. Feng Xu

In Media Technology

1. Xiaoxiang Zhu
2. Lichao Mou
3. Richard Bamler
4. Lichuan Gu
5. Xiaohui Yuan
6. Yuanyuan Wang
7. Feng Xu
8. Jianfang Shi

Citations in the corpus

• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, background): “For a more detailed explanations of different CNN models, interested readers are referred to [34].”
• A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5 (2021, background): “Furthermore, it can build and use more abstract concepts [18].”
• Mapping dominant leaf type based on combined Sentinel-1/-2 data – Challenges for mountainous countries (2021, methodology): “Nevertheless, some expertise is still required to set up a deep network architecture and tune the optimization hyperparameters (Hoeser and Kuenzer, 2020).”
• A review of deep learning methods for semantic segmentation of remote sensing imagery (2021, background): “Most recently, there are reviews on semantic image segmentation using deep learning methods (Liu et al., 2018; Guo, Liu, Georgiou, & Lew, 2018; Ajmal et al., 2018; Yu et al., 2018; Garcia-Garcia et al., 2018; Hoeser & Kuenzer, 2020).”