Funder
Connected Conservation Foundation
Airbus
Publisher
Springer Science and Business Media LLC
Reference79 articles.
1. Persson, H. J. (2016). Estimation of Boreal Forest Attributes from very high resolution Pléiades Data. Remote Sens (Basel), 8. https://doi.org/10.3390/rs8090736.
2. Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz, S. (2021). Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24–49.
3. Jung, J., Maeda, M., Chang, A., et al. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology, 70, 15–22.
4. Ghosh, S., Kumar, D., & Kumari, R. (2022). Cloud-based large-scale data retrieval, mapping, and analysis for land monitoring applications with Google earth engine (GEE). Environmental Challenges, 9, 100605.
5. eoPortal (2012). Pleiades-HR (High-Resolution Optical Imaging Constellation of CNES). In: eoPortal. https://www.eoportal.org/satellite-missions/pleiades#eop-quick-facts-section. Accessed 21 Jul 2023.