The latest decade has witnessed a great development of satellite remote sensing sensors, Earth Observation (EO) Big data with huge quantities of satellite images with high spatial and temporal resolution are available. The successive observation for long periods of time of a large area with short revisit interval from space is resulting in dense satellite image time series (SITS). SITS are a new type of EO product allowing us to analyze and mine not only spatial but spatio-temporal dynamic evolution information content about the scene structure and objects. This need to exploit spatial and temporal information contents from SITS is increasing with a wide range of applications, including urban development, target dynamic monitoring, precise agriculture, forest, and etc. Meanwhile, the open and free data access from many EO missions as in the Copernicus program or ChinaÔÇôBrazil Earth Resources Satellite 4 opens new fantastic perspectives for research and applications of Artificial Intelligence for EO A4EO. With a significant progress of AI, especially the major breakthrough of deep learning has proven to be an extremely powerful tool in many fields including computer vision, speech recognition, natural language processing, etc, and has also been enjoyed into the geoscience and remote sensing community for remote sensing big data analysis. However, contrary to multimedia image analysis which has been boosted by advanced deep neural networks and easily available big multimedia training data, the deep learning techniques and automatic tools for scale dense SITS analysis are limited, and large-scale benchmark dataset is not exist. Moreover, dense SITS analysis raises some specific challenges. Novel network architectures and datasets have to be developed exploiting the temporal information jointly with the spatial and spectral information of the data, but cope with multi-modal, multi resolutions and multi sensors data in a synergistic way. In this project, we intend to develop hybrid explainable AI, deep learning and big data analytics techniques and tools for large-scale dense SITS analysis and address the challenges to advance the state of the art in this area. The overall objective of this project is to provide an effective solution for large-scale dense SITS analysis, being capable of automatic discovery of regularities, relationships, and dynamic evolution, leading to a better and easier understanding of the underlying processes of specific scenes and targets. Specifically, the objectives are:(1) Develop weakly supervised deep learning techniques for object extraction and semantic classification for remote sensing images. (2) Develop deep spatial-temporal network techniques for large dense SITS clustering, classification and prediction. (3) Exploit deep change detection techniques for multi-temporal satellite images. We will compile a large-scale spatial-temporal data sensor for spatial-temporal change detection both for optical remote sensing images and for SAR images and develop novel deep neural network architectures and learning techniques for our specific data tensor and change detection task. (4) Develop spatial-temporal fusion and synergic computation techniques of Multi-modal, Multi-resolutions and Multi-sensor images for SITS mining, classification, and change analysis. The scientists of CEOSpaceTech – the research center within Politehnica University Bucharest – Romania, Tongji University and Shanghai Jiaotong University – China will tightly collaborate to advance these innovative techniques. For the evaluation and validation process we consider 2 use cases, targeting areas of Romania – EU for ecosystem monitoring of an UNESCO protected area and Shanghai – China for urban evolution in support of smart and sustainable urban information services. A list of related funding projects able to co-financing this project is available in the Annexes.