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Deep Learning and Earth Observation to Support the Sustainable Development Goals
Claudio Persello, Jan Dirk Wegner, Ronny Hänsch +4
The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). New developments and a…
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering
Yaoming Cai, Zijia Zhang, Zhihua Cai +3
This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train…
Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
Guichen Zhang, Pedram Ghamisi, Xiao Xiang Zhu
This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs). F…
Multisource and Multitemporal Data Fusion in Remote Sensing
Pedram Ghamisi, Behnood Rasti, Naoto Yokoya +9
The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the eff…