par Wyard, Coraline WC;Beaumont, Benjamin 
;Grippa, Taïs ;Georganos, Stefanos ;Hallot, Eric
Référence IEEE International Geoscience and Remote Sensing Symposium (Virtual - Brussels, Belgium)
Publication Non publié, 2021-07-01
;Grippa, Taïs ;Georganos, Stefanos ;Hallot, EricRéférence IEEE International Geoscience and Remote Sensing Symposium (Virtual - Brussels, Belgium)
Publication Non publié, 2021-07-01
Communication à un colloque
| Résumé : | Landfill managers are subject to obligations which include the regular monitoring of the topographical and land cover (LC) evolution of the site. This research aims at developing a cost-effective non-intrusive methodology for mapping landfill LC features. To this end, a state-of-the-art OBIA open-source workflow based on an integration of GRASS GIS and Python programming environment was adapted and applied to 3-cm optical UAV image acquired over the landfill site of Hallembaye (Belgium). The results of this 8-class supervised classification are promising with an overall accuracy of 80.5%. This study shows that existing open-source processing chain can be adapted to UAV imagery. In addition, the added value of feature selection and of textural information provided by very high-resolution optical data is also highlighted. Finally, this study illustrates the potential of machine learning for the monitoring of landfill sites. |
