GEOMEMBRANE BASINS DETECTION BASED ON SATELLITE HIGH-RESOLUTION IMAGERY USING DEEP LEARNING ALGORITHMS

Abstract. Agriculture is a very important economic sector in Morocco, which requires a set of tools to improve agricultural production. Among these tools, the use of geomembrane basins. The latter is of great importance in smart farming planning, management practices or even in livestock use. In this context, this study evaluates a recognition and classification of the geomembrane basin using remote sensing satellite images; based on the Yolov3 deep learning neural network. This paper first adjusts the network model to make it suitable for detecting small targets on remote sensing images, then uses the k-means algorithm to calculate the grid size of the Yolo network model suitable for geomembrane basins, then uses yolov3 to train the data that makes up the satellite remote sensing imagery. The network model for the detection of the geomembrane basins is obtained by the test phase. Finally, the geomembrane basin detection model adapted to the remote sensing image is obtained in the validation phase. Through the research and analysis of the experimental results, it can be seen that this method effectively detects the geomembrane basins in the remote sensing images and ensures the high detection accuracy of the experimental results, which gave us an accuracy of 75%.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: GEOMEMBRANE BASINS DETECTION BASED ON SATELLITE HIGH-RESOLUTION IMAGERY USING DEEP LEARNING ALGORITHMS ; volume:XLVIII-4/W6-2022 ; year:2023 ; pages:75-79 ; extent:5 ; The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-4/W6-2022 (2023), 75-79 (gesamt 5)

Contributors, Places and Time

• Creator: Benayad, M. ; Houran, N. ; Aamir, Z. ; Maanan, M. ; Rhinane, H.

Further information

• DOI: 10.5194/isprs-archives-XLVIII-4-W6-2022-75-2023
• URN: urn:nbn:de:101:1-2023033007332634869421
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:53 AM CEST