Editorial for Special Issue: “How the Combination of Satellite Remote Sensing with Artificial Intelligence Can Solve Coastal Issues”
Author:
Affiliation:
1. Institute of Research for Development, LEGOS, 31055 Toulouse, France
2. ISAE-SUPAERO, 10 Avenue Edouard Belin, 31055 Toulouse, France
3. EOlab, Centre National d’Etudes Spatiales (CNES), 18 Av. Edouard Belin, 31400 Toulouse, France
Abstract
Publisher
MDPI AG
Subject
General Earth and Planetary Sciences
Link
https://www.mdpi.com/2072-4292/15/11/2897/pdf
Reference10 articles.
1. Zhong, J., Sun, J., Lai, Z., and Song, Y. (2022). Nearshore Bathymetry from ICESat-2 LiDAR and Sentinel-2 Imagery Datasets Using Deep Learning Approach. Remote Sens., 14.
2. Almar, R., Bergsma, E.W., Brodie, K.L., Bak, A.S., Artigues, S., Lemai-Chenevier, S., Cesbron, G., and Delvit, J.M. (2022). Coastal topo-bathymetry from a single-pass satellite video: Insights in space-videos for coastal monitoring at Duck Beach (NC, USA). Remote Sens., 14.
3. Najar, M.A., Benshila, R., Bennioui, Y.E., Thoumyre, G., Almar, R., Bergsma, E.W., Delvit, J.M., and Wilson, D.G. (2022). Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches. Remote Sens., 14.
4. Almar, R., Bergsma, E.W., Thoumyre, G., Baba, M.W., Cesbron, G., Daly, C., Garlan, T., and Lifermann, A. (2021). Global satellite-based coastal bathymetry from waves. Remote Sens., 13.
5. Lundine, M.A., and Trembanis, A.C. (2021). Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models. Remote Sens., 13.
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