TUP.P16.1: BILSTM−BIGRU: A FUSION DEEP NEURAL NETWORK FOR PREDICTING AIR POLLUTANT CONCENTRATION
Prasanjit Dey, Technological University Dublin, Ireland; Soumyabrata Dev, University College Dublin, Ireland; Bianca Schoen Phelan, Technological University Dublin, Ireland
TUP.P16.2: DETECTING CLOUD PRESENCE IN SATELLITE IMAGES USING THE RGB-BASED CLIP VISION-LANGUAGE MODEL
Mikolaj Czerkawski, Robert Atkinson, Christos Tachtatzis, University of Strathclyde, United Kingdom
TUP.P16.3: MACHINE LEARNING-BASED APPROACH FOR LANDSLIDE SUSCEPTIBILITY MAPPING USING MULTIMODAL DATA
Xianping Ma, Man-On Pun, School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China, China; Ming Liu, MizarVision, Shanghai, China, China; Yang Wang, Xi'an Leitong Technology Co. Ltd, Xi'an, China, China
TUP.P16.5: A REMOTE SENSING IMAGE DEHAZING NETWORK BASED ON DARK CHANNEL ATTENTION MECHANISM
Zongbao Liang, Jindong Xu, Fei Jia, Yijie Wang, Jie Wang, Yantai University, China; ,
TUP.P16.7: DEEPTRCY: LIFE STAGE IDENTIFICATION OF SATELLITE TROPICAL CYCLONE IMAGES
HIDETOMO SAKAINO, Natnapat Gaviphatt, Alivanh Insisiengmay, Louie Zamora, Dwi Fetiria Ningrum, Weathernews Inc., Japan; Shunji Kotsuki, Chiba University, Japan
TUP.P16.8: A NEW CLOUD FEATURE DIFFERENCE LOSS FOR ENHANCING THE DETECTION OF CLOUDS IN REMOTE SENSING IMAGES
Xinyi Xu, Wei He, Yu Xia, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China; Hongyan Zhang, School of Computer, China University of Geosciences, Wuhan, China