Earth observation

Monitoring and prediction of objects, hazard risks and streamlining of aerial surveys

Background

Optical images from drones or satellites and data captured by radar sensors from above contain enormous amounts of complex data. They have the potential to reveal valuable information about our planet and its surface that could be used automate terrain mapping or to predict objects and hazard risks such as vessels and potential oil spills at sea.

Main objective

For earth observation the planned innovations aim for improved methods for monitoring and prediction of hazard risks, object detection, and for surveying and mapping ground and sea from air through exploitation of remote sensing images from satellites, aircrafts and drones.

Challenges

Limited and inadequate training data is a general problem in remote sensing. Combination of multi-sensor data (e.g. from optical and radar sensors) and time dependencies is another key challenge. Modelling of contextual information may also enhance the performance, but important contextual issues like integration of physical properties have not yet been addressed.

These are some of the research challenges Visual Intelligence are addressing.

Highlighted publications

Using Machine Learning to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images
June 21, 2022
Developing artificial intelligence methods to help pathologists in analysis of whole slide images for cancer treatment and detection.
Principle of Relevant Information for Graph Sparsification
May 20, 2022
How can we remove the redundant or less-informative edges in a graph without changing its main structural properties?
Detection and classification of fish species from acoustic data
March 7, 2022
Using deep learning to assess fish stocks from acoustic images.