Find our colleagues Vassilis Sito and Stela Girtsou in the session TU2.MM-25: Machine Learning Methods in Hazard Assessment on Tuesday 13th of July (13:00 - 14:10 CEST)!
Stela Girtsou and Vassilis Sito will present the following papers:
TU2.MM-25.7 A MACHINE LEARNING METHODOLOGY FOR NEXT DAY WILDFIRE PREDICTION
(13:30-13:35 CEST)
TU2.MM-25.8 SEMI-SUPERVISED PHENOLOGY ESTIMATION IN COTTON PARCELS WITH SENTINEL-2 TIME-SERIES
(13:35-13:40 CEST)
Abstract
Vassilis Sitokonstantinou
SEMI-SUPERVISED PHENOLOGY ESTIMATION IN COTTON PARCELS WITHSENTINEL-2 TIME-SERIES; TU2.MM – 25.8 Machine Learning Methods in Hazard Assessment; Tue, 13 Jul, 11:35-11:40 (UTC)
This study presents a dynamic phenology estimation methodology for cotton towards early warning and mitigation advice against natural disasters. First, a time-series comparison algorithm that is based on Earth Observation (EO) data is used to assign pseudo-labels to approximately 1,000 parcels. The knowledge is extracted from only limited ground truth information. The pseudo-labels are then used to train Random Forest (RF) regression models for phenology estimation. The pseudo-labeling process is used to augment the annotated dataset and allow for modelling the growth of cotton. The models are applied and evaluated on two different test sites in Greece; for which field campaigns were carried out to collect the labels. The results are satisfactory and show-case the successful generalization of the models to other areas. The dynamic predictions for cotton growth and extreme weather events, from numerical weather prediction (NWP)models, are invaluable information for decision-making relevant to agricultural insurance schemes and farm management.
Index Terms—cotton phenology, agricultural insurance, semi-supervised learning, early warning, natural disasters