Big Data for Remote Sensing: Challenges and Opportunities

By M. Chi, Antonio J. Plaza, Jon Atli Benediktsson, Zhongyi Sun, Jinsheng Shen, Yangyong Zhu. Proceedings of the IEEE, 2016

523 citations recorded by the source corpus.

Subfields: Media Technology, Pollution, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6d5867465c55b2d8fda24591c3cb40af416de525

How researchers see this paper

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

In Media Technology

1. Lorenzo Bruzzone
2. Jon Atli Benediktsson
3. Pedram Ghamisi
4. Francesca Bovolo
5. Behnood Rasti
6. Antonio J. Plaza
7. Naoto Yokoya
8. Jocelyn Chanussot

In Pollution

1. Francesc X. Prenafeta-Boldu
2. Andreas Kamilaris

In Signal Processing

1. Francesc X. Prenafeta-Boldu
2. Jon Atli Benediktsson
3. Lorenzo Bruzzone
4. Andreas Kamilaris
5. Peter M. Atkinson
6. Pedram Ghamisi
7. Behnood Rasti
8. Francesca Bovolo

Citations in the corpus

• Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review (2020, background): “of the most important steps is to develop a safe, efficient, and advanced cloud computing platform [3], [4].”
• SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery (2023, background): “Remote Sensing Imagery (RSI) interpretation is crucial in understanding our common home, the Earth [17, 70], via quite diverse tasks [5, 14, 49, 84], e.g . crop monitoring, natural disaster management, etc .”
• Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, methodology): “, three facets of big data from different perspectives related to who owns the data, who has innovative big data methods and methodologies, and who needs big data applications [190].”
• The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot (2023, background): “One of the main challenges in applying deep learning techniques to remote sensing image segmentation is the need for large volumes of labeled ground-truth data [6].”
• A review of the use of convolutional neural networks in agriculture (2018, background): “Emerging ICT technologies relevant for understanding agricultural ecosystems include remote sensing (Bastiaanssen et al ., 2000), the Internet of Things (IoT) (Weber and Weber, 2010), cloud computing (Hashem et al ., 2015) and big data analysis (Chi et al ., 2016; Kamilaris et al ., 2017).”