@inproceedings{10641828,

title = {Automated Detection and Categorization of River Islets Using Sentinel 2 Images},

author = {Thodoris Betsas and Ioannis Rallis and Anastasios Doulamis and Nikolaos Doulamis and Andreas Georgopoulos},

doi = {10.1109/IGARSS53475.2024.10641828},

issn = {2153-7003},

year  = {2024},

date = {2024-07-01},

urldate = {2024-07-01},

booktitle = {IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium},

pages = {4344-4348},

abstract = {Climate change and extreme weather events diversly affect riverine areas. Since decades river monitoring techniques in combination with satellite images, are used to draw meaningful conclusions about rivers e.g., river banklines, islets, flooded areas etc. However, the majority of the existing methods rely on manual techniques especially for the analysis of river islets. In this paper we propose an automatic method to find the islets of a river and analyze them. Firstly, the proposed method uses deep learning and GIS algorithms, applied on satellite images, to find the islets. Then, the detected islets are stored as GIS layers in multiple geometries i.e., polygons, lines and points. Finally, the created layers are used to classify the islets into the "missing", "existing" and "new" ones resulting in an informative output for the users. In general, the proposed approach gives promising results and especially for the vectorization part which achieves an mIoU 94.9%.},

keywords = {deep learning, Geometry, Geoscience and remote sensing, islets categorization, islets detection, Land surface, Manuals, remote sensing, river monitoring, Rivers, Satellite images, segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}