Schedule Earth Observation satellites with Deep Reinforcement Learning

International audienceOptical Earth observation satellites acquire images worldwide , covering up to several million square kilometers every day. The complexity of scheduling acquisitions for such systems increases exponentially when considering the interoperabil-ity of several satellite constellations together with the uncertainties from weather forecasts. In order to deliver valid images to customers as fast as possible, it is crucial to acquire cloud-free images. Depending on weather forecasts, up to 50% of images acquired by operational satellites can be trashed due to excessive cloud covers, showing there is room for improvement. We propose an acquisition scheduling approach based on Deep Reinforcement Learning and experiment on a simplified environment. We find that it challenges classical methods relying on human-expert heuristic

Towards operational application of Deep Reinforcement Learning to Earth Observation satellite scheduling

Scheduling an agile optical Earth Observation satellite (AEOS) requires to choose a limited number of images to take among a large set of possibilities. It is a NP-hard problem which is made even more difficult by the presence of uncertainties: an image is useless if it is too cloudy and weather uncertainty cannot be removed, no matter the accuracy of the forecasts. Moreover, among the various types of customer requests of commercial satellites, we focus here on large area acquisitions that cover a country or a continent. Such requests require several months or years to complete even with a constellation of satellites. Considering such long time frames, the completion time highly depends on weather uncertainties and there are currently no trustworthy forecasts. Therefore the selection of the requests is crucial to speed up the completion using a long-term strategy. Reinforcement Learning is an interesting solution to explore when it comes to uncertain environments. We propose to use the well-known Actor Critic (A2C) algorithm combined with Transfer Learning, Domain Knowledge and Domain Randomization (TDDR). We demonstrate how transfer learning is a way to address real-world problem. We find that TDDR method challenge state-of-the-art heuristics for satellite scheduling on various real weather conditions

Temporal-Domain Adaptation for Satellite Image Time-Series Land-Cover Mapping With Adversarial Learning and Spatially Aware Self-Training

Nowadays, satellite image time series (SITS) are commonly employed to derive land-cover maps (LCM) to support decision makers in a variety of land management applications. In the most general workflow, the production of LCM strongly relies on available GT data to train supervised machine learning models. Unfortunately, these data are not always available due to time-consuming and costly field campaigns. In this scenario, the possibility to transfer a model learnt on a particular year (source domain) to a successive period of time (target domain), over the same study area, can save time and money. Such a kind of model transfer is challenging due to different acquisition conditions affecting each time period, thus resulting in possible distribution shifts between source and target domains. In the general field of machine learning, unsupervised domain adaptation (UDA) approaches are well suited to cope with the learning of models under distribution shifts between source and target domains. While widely explored in the general computer vision field, they are still underinvestigated for SITS-based land-cover mapping, especially for the temporal transfer scenario. With the aim to cope with this scenario in the context of SITS-based land-cover mapping, here we propose spatially aligned domain-adversarial neural network, a framework that combines both adversarial learning and self-training to transfer a classification model from a time period (year) to a successive one on a specific study area. Experimental assessment on a study area located in Burkina Faso characterized by challenging operational constraints demonstrates the significance of our proposal. The obtained results have shown that our proposal outperforms all the UDA competing methods by 7 to 12 points of F1-score across three different transfer tasks

Adaptation de domaine pour la cartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires optiques

International audienceSource de données à fortes valeurs intrinsèques, les séries temporelles d'images satellitaires sont devenues indispensables dans l'élaboration de produits tels que les cartographies d'occupation des sols. Leur production automatique par apprentissage machine est possible sous réserve de disposer des données de référence nécessaires. Le facteur limitant concerne bien souvent la quantité et la qualité de ces données. Étudiée depuis une dizaine d'années, l'adaptation de domaine non supervisé est un champ de recherche prometteur mais peu décliné et efficace dans le cas de cette problématique. Aussi, nous proposons de rechercher une nouvelle méthode d'adaptation de domaine adaptée aux données d'observation de la Terre et tirant profit de la dimension temps des série temporelles d'images satellitaires