Visual Learning and Reasoning in Earth Observation

Abstract: Over the past years deep learning has brought a real revolution in artificial intelligence for Earth observation (AI4EO), producing stunning results in a variety of different applications. For instance, deep learning-based remote sensing image classification and object detection systems can now be trained to recognize hundreds of different land cover, land use, and object categories, which sometimes are difficult to distinguish even for humans. Albeit these are indeed impressive advancements, there is no doubt that many problems that are really at the core of AI4EO are far from being solved. This is particularly true for those tasks that involve reasoning, such as induction, deduction, and spatial and temporal reasoning. In this seminar, I will present several works on visual learning and reasoning in remote sensing.

Bio: Lichao Mou is currently a Guest Professor at the International AI Future Lab on AI4EO, Technical University of Munich (TUM) and the Head of Visual Learning and Reasoning team at the Department: EO Data Science, Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR). He is interested in algorithms for Earth observation data analysis and visual tasks. His work explores topics in remote sensing, computer vision, and machine/deep learning.

Link to the profile of Lichao Mou