The EOImageNET project, funded by the European Space Agency (ESA), aims to bridge the gap in Earth Observation (EO) by developing a global, multi-scale database of optical satellite imagery and object categories. This innovative dataset is designed to fine-tune deep learning models, making them resolution-invariant and applicable across multiple EO missions. By leveraging advances in computer vision and transfer learning, EOImageNET enables more accurate object detection, opening the door to advanced multi-scale analyses. The dataset and models will be publicly available to accelerate research in EO data processing and analysis.
TUATARA, as a key contributor to EOImageNET, will integrate its expertise in space data analysis and AI-driven solutions to enhance the project’s scalability and precision. By applying TUATARA’s cutting-edge algorithms, including those used in our own Earth Observation (EO) initiatives, we will ensure that the EOImageNET dataset is robust and applicable across diverse EO missions. Our experience in automated monitoring and prediction of natural and human-induced processes from space aligns perfectly with EOImageNET’s goal of revolutionizing object detection in satellite imagery. TUATARA will also focus on optimizing the models for large-scale image datasets, ensuring seamless adaptability to real-world EO applications, such as monitoring of infrastructure and natural objects.
At TUATARA, we are dedicated to leveraging Earth Observation data to deliver impactful solutions across industries. The exponential growth of satellite data in recent years, combined with advancements in AI techniques, has opened new opportunities for detecting, monitoring, and predicting events on Earth. TUATARA’s innovative approach shifts the focus from manual to automated methods, enabling analysis of vast datasets of satellite images collected daily.
For instance, in agriculture, we explore the potential for precision monitoring of soil and crop conditions, which could help detect anomalies at various scales to support farm management. In insurance, we aim to offer data on drought indicators, natural disasters, and crop yield predictions. Our oil & gas solutions may include infrastructure monitoring and oil spill detection capabilities, while our environmental protection initiatives seek to provide insights into plant health, biodiversity, and natural hazard risks. In the defense sector, we work on concepts for monitoring critical infrastructure and areas of interest. Additionally, in the marine industry, we are developing methods for ship detection and ocean condition monitoring, and for governments, we explore providing data solutions that could assist with urban planning and infrastructure status monitoring.
We are also actively involved in an exciting new project focused on satellite and aerial image analysis for agriculture and archaeology, in collaboration with Alioth Space and the Warsaw University of Technology in Poland. Our role in this initiative includes conducting advanced research on image data to develop AI solutions for crop condition assessment, land classification, environmental change detection, and linear object identification. Additionally, we are optimizing analytical models to enhance system performance, contributing to the broader goal of revolutionizing digital agriculture and supporting archaeological discoveries.