Sébastien Giordano

Institut national de l’information géographique et forestière | IGN · LaSTIG/MATIS

We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provides a unique and rich resource for large-scale geospatial analysis. FLAIR contains high-resolution aerial imagery with a ground sample distance of 20 cm and over 20 billion...

The FLAIR #2 dataset hereby presented includes two very distinct types of data, which are exploited for a semantic segmentation task aimed at mapping land cover. The data fusion workflow proposes the exploitation of the fine spatial and textural information of very high spatial resolution (VHR) mono-temporal aerial imagery and the temporal and spec...

The French National Institute of Geographical and Forest Information (IGN) has the mission to document and measure land-cover on French territory and provides referential geographical datasets, including high-resolution aerial images and topographic maps. The monitoring of land-cover plays a crucial role in land management and planning initiatives,...

An orthophotomosaic is as a single image that can be layered on a map. It is produced from a set of aerial images impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds shadows as well as the camera itself, like lens vignetting. These create some unsightly radiometric inhomogeneity in t...

In this paper, we propose an operational solution for the yearly classification of crop parcels at national scale (namely France) for Land Parcel Identification System updating, under the Common Agricultural Policy (CAP) umbrella. Our pipeline is based on the ι2 open-source framework and fed with both time series of Sentinel-1 radar and Sentinel-2...

Dense time series of optical satellite imagery describing vegetation activity provide essential information for the efficient and regular monitoring of vegetation. Nevertheless, the temporal resolution of optical sensors is strongly affected by cloud cover, resulting in significant missing information. The use of complementary acquisitions, such as...

Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. G...

Orthophotomosaic is defined as a single image that can be layered on a map. The term “mosaic” implies that it is produced from a set of images, usually aerial images. Even if these images are taken during cloudless period, they are impaired by radiometric inhomogeneity mostly due to atmospheric phenomena, like hotspot, haze or high altitude clouds...

Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-...

Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and -2 acquisitions, this paper investigates the automation of land parcel identi- fication system (LPIS ) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parc...

Time series of optical and Synthetic Aperture RADAR (SAR) images provide complementary knowledge about the cover and use of the Earth surface since they exhibit information of distinct physical nature. They have proved to be particularly relevant for monitoring large areas with high temporal dynamics and related to significant ecosystem services. G...

Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions. In particular, large-scale control of agricultural parcels is an issue of major political and economic importance. In this regard, hybrid convolutional-recurrent neural a...

Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a powerful tool to monitor and investigate Near-Real-...

More and more countries have digitised or are currently digitising their archives of aerial images. They are a unique and relatively unexplored means to chronicle land-cover information over the past 100 years with very high spatial resolution. Such data provide a relatively dense temporal sampling of the territories. 3D information can even be ret...

In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, a...

In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, a...

Les prises de vues aériennes photogrammétriques anciennes sont un outil unique et assez peu exploité pour le suivi des changements 3D d'occupation du sol au cours du siècle passé. Ces études restent toutefois limitées en termes de surface et de nombre de dates concernées, du fait de l'absence de méthode automatique pour géoréférencer finement les i...

Automatic analysis of Sentinel image time series is recommended for monitoring agricultural land use in Europe. To improve classification capacities, we propose a temporal structured classification combining Sentinel images and former vintages of the Land-Parcel Identification System. Inter-annual crop rotations are learned and combined with the sa...

Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term...

Archival aerial images are a unique and relatively unexplored means to generate detailed land-cover information in 3D over the past 100 years. Many long-term environmental monitoring studies can be based on this type of image series. Such data provide a relatively dense temporal sampling of the territories with very high spatial resolution. Further...

Cette thèse s'inscrit dans le contexte de l'amélioration de la caractérisation de l'occupation du sol à partir d'observations de télédétection de natures très différentes : le radar polarimétrique et les images optiques multispectrales. Le radar polarimétrique permet la détermination de mécanismes de rétrodiffusion provenant de théorèmes de décompo...

Land cover is a layer of information of significant interest for land management issues. In this context, combining remote sensing observations of different types is expected to produce more reliable results on land cover classification. The objective of this work is to explore the use of polarimetric radar images in association with co-registered...

A new approach has been proposed for radar and optical image fusion. Candidate images for fusion are polarimetric radar images and very high resolution stereoscopic optical images. The fused product is expected to produce more reliable large scale land cover classifications. In the first part of this paper, observation-based state-of-the-art method...

A new approach has been proposed for radar and optical image fusion. Candidate images for fusion are polarimetric radar images and very high resolution stereoscopic optical images. The fused product is expected to produce more reliable large scale land cover classifications. In the first part of this paper, observation-based state-of-the-art method...

The Energetic Particle Detector (EPD) on board the Solar Orbiter represents an excellent chance to study Solar Energetic Particles, solar modulation effects and cosmic ray gradients from an unique observation point. The development of silicon detector technology and electronics allows the nuclear and isotopic components of solar and galactic cosmic...

The new class of CMOS panoramic detectors represents an innovative tool for the experimental astronomy of the forthcoming years. While current charge-coupled device (CCD) technology can produce nearly ideal detectors for astronomical use, the scientific quality CMOS detectors made today have characteristics similar to those of CCD devices but a sim...