Multi-temporal modeling for anomaly detection in satellite imagery

The Saint Exupéry Technological Research Institute (IRT) is an accelerator for science, technological research and transfer to the aeronautics and space industries for the development of innovative solutions that are safe, robust, certifiable and sustainable.

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- Take part in innovative research projects, at the service of French technological research and for the benefit of industry established on national and European territory.

- Living your passion for technology, giving yourself the freedom to innovate and developing your pioneering and team spirit!

- Evolve in a collaborative and multicultural environment, working alongside collaborators from academic research or industry: researchers, doctoral students, engineers, technicians, etc.

This postdoctoral project is the result of a collaboration between the Centre National d’Etudes Spatiales (CNES) and the Institute of Technological Research Saint Exupéry (IRT Saint Exupéry). The successful candidate will be employed by CNES. The primary location is at IRT Saint Exupéry site in Toulouse or Sophia Antipolis.

About the Centre National d’Etudes Spatiales (CNES)

CNES is the public institution responsible for proposing and implementing France’s space policy. Through its innovative activities, CNES plays a major role within the European space sector and is a leading player in major international programs.

As an incubator of projects and a laboratory for new ideas, CNES’s mission is to continue inventing the space sector of tomorrow by offering unique career paths. Working at CNES means joining 2,350 employees in a cutting-edge field firmly oriented toward the future and innovation.

Earth Observation (EO) satellites now produce very large archives of high-resolution images, covering the same regions repeatedly over long periods of time. The increasing availability of commercial constellations provides dense image time series with fine spatial and temporal resolution. These datasets offer a unique opportunity to detect, understand, and monitor dynamic phenomena on the Earth’s surface.

However, most existing methods for detecting changes or anomalies in satellite imagery remain limited. Many approaches compare only two images acquired at different times, which fails to leverage the richness of long temporal sequences. Others rely on supervised models trained on labelled data, but collecting reliable ground-truth annotations at scale is extremely expensive and often infeasible. In many applications, particularly those involving rare or unexpected events, no labelled data exist at all.

This research aims to develop unsupervised anomaly detection methods that can learn from historical multi-temporal satellite data without requiring manual labels. The goal is to build models capable of identifying unusual or unexpected patterns over time in a fully data-driven manner.

From an operational perspective, the concept is the following: when a new satellite image is acquired, the model compares it with the historical data available for the same location to automatically detect whether something unusual is occurring. This allows continuous, near-real-time monitoring of large areas enabling the possibility of triggering alerts or reprogramming.

The potential applications of such models are very broad, spanning from environmental monitoring (e.g., detecting pollution events, vegetation stress, or illegal mining) to security and surveillance (e.g., identifying suspicious maritime or land activities).

To make the study more concrete, this postdoctoral research will focus on a specific use case: marine anomaly detection, such as the detection of oil spills or sargassum (seaweed) blooms using high-resolution optical imagery.

This subject tackle following scientific challenges:

1. Multi-Temporal Modelling of Satellite Data:

• Design models that can represent and analyse long sequences of high-resolution images.

• Adapt these models to handle irregular image acquisition, cloud cover, and differences between sensors.

2. Unsupervised and Self-Supervised Learning:

• Develop methods capable of learning useful spatio-temporal representations without annotated data.

• Explore approaches such as contrastive learning, temporal clustering, or generative modelling.

3. Unsupervised Anomaly Detection:

• Create models that can automatically detect abnormal events or changes within long temporal contexts.

• Move beyond simple binary “anomaly/no anomaly” detection to characterize dynamics, extent, and type.

4. Evaluation and Benchmarking:

• Establish robust evaluation metrics for unsupervised anomaly detection.

• Compare the multi-temporal representation learning results on tasks such as detection, segmentation, or classification with baselines that use single images.

• Design dedicated experiments for the marine use case and validate with available data.

We propose the following research program:

• Literature Review. Study existing methods for image time-series analysis, unsupervised/self-supervised learning, and unsupervised anomaly detection in remote sensing.

• Method Design. Propose new frameworks for unsupervised anomaly detection in satellite time series.

• Implementation and Experimentation. Implement and test models on high-resolution datasets. Apply the approach to the marine anomaly detection use case (oil spills, sargassum blooms…).

• Evaluation. Assess the models both quantitatively and qualitatively, and compare with existing baselines.

• Dissemination. Publish results in journals and conferences in remote sensing and machine learning.

PhD holder in Deep Learning and Computer Vision. Experience in Remote Sensing or Satellite Image Analysis is a valuable asset.