FLAIR #1: semantic segmentation and domain adaptation dataset

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, which can have significant socio-economic and environmental impact. Together with remote sensing technologies, artificial intelligence (IA) promises to become a powerful tool in determining land-cover and its evolution. IGN is currently exploring the potential of IA in the production of high-resolution land cover maps. Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images. However, territories as large as France imply heterogeneous contexts: variations in landscapes and image acquisition make it challenging to provide uniform, reliable and accurate results across all of France. The FLAIR-one dataset presented is part of the dataset currently used at IGN to establish the French national reference land cover map "Occupation du sol \`a grande \'echelle" (OCS- GE).Comment: Data access updat

Preface: the 2020 edition of the XXIVth ISPRS congress

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Suivi de la dynamique des prairies permanentes par analyse des séries temporelles multi-modales

Les vastes surfaces de prairies ainsi que la reconnaissance croissante des services écosystémiques qu'elles fournissent ont révélé des besoins urgents pour leur conservation et leur gestion durable. En particulier, leur surexploitation entraîne une diminution de leur capacité à fournir des services écosystémiques. En dépit de la nécessité d’obtenir des données décrivant l’exploitation des prairies, l’observation de la fréquence et de la nature de leur exploitation demeure restreinte.La télédétection par satellite est un outil approprié pour un suivi efficace des prairies. Les séries temporelles d'images satellites permettent des observations synoptiques et régulières. Combinées, les caractéristiques fournies par les images complémentaires optiques et radars des satellites Sentinel offrent de nouvelles opportunités. Les recherches menées dans le cadre de ce doctorat visent à étudier les capacités et la synergie des séries temporelles Sentinel pour le suivi des prairies. Plus spécifiquement, elles visent le développement de méthodes de détection des pratiques agricoles. La gestion de chaque prairie est faite avec des intensités et des calendriers distincts. Par conséquent, des acquisitions fréquentes et régulières sont d’autant plus indispensables que les prairies peuvent repousser rapidement après leur exploitation.L’abondance des données de Sentinel-1 et Sentinel-2 et leur exploitation conjointe soulèvent de nouvelles problématiques. La haute dimension et la nature physique hétérogène des données, conjuguant divers domaines spatiaux, spectraux et temporels, font partie des aspects à explorer. Récemment, les progrès en matière de ressources informatiques et d'algorithmes d'apprentissage automatique mettent au premier plan les stratégies d'apprentissage profond, qui permettent de relever les défis exposés, tels que le traitement à grande échelle et l'extraction d’informations complexes. L’objectif principal de la thèse est donc de développer des méthodes permettant le suivi en continu des prairies et la détection de leur exploitation. À cette fin, la thèse : (i) utilise les progrès permis par l'apprentissage profond, pour développer une méthodologie multi-source exploitant la synergie des données Sentinel-1 et Sentinel-2. La méthodologie développée vise spécifiquement à régresser les séries temporelles radars multivariées vers le NDVI optique et propose l'incorporation de connaissances contextuelles pour réduire l'impact de facteurs exogènes ; (ii) explore différentes méthodes permettant de détecter l’exploitation hétérogène des prairies.L'approche proposée, nommée Sentinels Regression for Vegetation Monitoring (SenRVM), fournit des séries temporelles de NDVI complètes avec une répétitivité de six jours. Les résultats, comparés aux NDVI obtenus par Sentinel-2, indiquent de faibles erreurs et une bonne stabilité sur diverses surfaces de végétation et différents contextes géographiques. Une étude d'ablation des données satellitaires et auxiliaires ainsi qu’une comparaison avec des méthodes communément adoptées pour interpoler les données manquantes soulignent la pertinence des contributions méthodologiques. Pour détecter avec précision les pratiques agricoles, une segmentation des parcelles de prairie à l'échelle du superpixel, justifiée par leur gestion rotative, permet d'exploiter les séries temporelles denses sur des zones homogènes. Différentes méthodologies de détection de changements sont comparées à l'aide de jeux de données de validation construits. Les résultats atteignent des performances élevées dans l’identification des différentes tendances liés à la gestion des prairies. L'intégration de données accessibles gratuitement, dont la continuité est assurée, et l'exploitation de méthodes d'apprentissage profond favorisant les applications à grande échelle et polyvalentes, ont permis d'introduire des méthodes qui proposent des bases pour la collecte d'informations pertinentes liées à l'écosystème des prairiesThe vast grassland surfaces as well as the growing recognition of the ecosystem services they provide have revealed urgent needs for their conservation and sustainable management. In particular, over-exploitation causes a significant decrease in their capacity to provide multiple ecosystem services. Despite the acknowledged importance of management practices, there are currently no large-scale efforts reporting on their frequency and nature.Satellite remote sensing appears to be a suitable tool for efficient grassland monitoring. Satellite time series specifically allow synoptic and regular observations. Combined, the characteristics provided by complementary optical and Synthetic Aperture Radar (SAR) images from the Sentinels bring new opportunities to monitor grassland vegetation conditions.The research conducted in this PhD thesis intends to investigate the capabilities and the synergy of Sentinel time series for grassland monitoring. Specifically, it aims to develop methods for detecting grassland management practices. Farmers are managing grasslands with a wide range of practices, having different impacts on biomass and calendars. Therefore, frequent and regular satellite acquisitions are mandatory, especially because grasslands exhibit the particularity of potential rapid regrowth after management. The joint exploitation of Sentinel-1 and Sentinel-2 and the increase of acquired data raise new challenges. The high dimension and the heterogeneous physical nature of the data, with various spatial, spectral and temporal domains, are among the aspects to be explored. At the same time, recent advances in computing resources and machine learning algorithms are bringing to the forefront deep learning strategies suitable for dealing with the reported requirements, such as large-scale processing and data mining.In this context, the main objective of this PhD is to develop new methodologies allowing the frequent and regular monitoring of grasslands and the detection of their management practices. Under this purpose, this PhD: (i) uses the advances of deep learning architectures to develop a multi-source methodology exploiting the synergy and capabilities of both Sentinel-1 and Sentinel-2 data. The developed recurrent-based methodology targets to regress multivariate SAR time series towards optical NDVI and proposes the incorporation of contextual knowledge to reduce the impact of exogenous factors leading to SAR data variability ; (ii) explores methods aiming to detect heterogeneous changes in vegetation status associated to grassland management practices.The proposed Sentinels Regression for Vegetation Monitoring (SenRVM) approach provides NDVI time series with no missing data at 6 days. The results, compared to the NDVI obtained by Sentinel-2, show low errors and good stability on contrasted vegetation surfaces and different large-scale geographical contexts. An ablation study of satellite and ancillary features and a comparison to commonly adopted gap-filling methods for retrieving information over short- and long-term data gaps underline the methodological contributions. To accurately detect management practices, a segmentation of grassland parcels at the superpixel-scale, justified by their rotational management, furthermore allows exploiting the dense time series over homogeneous areas. Diverse 1D time series change detection methodologies are compared using two constructed large-scale validation datasets. The results achieve high performances in retrieving the different patterns related to grassland management.The proposed methodologies integrate freely accessible data, whose continuity is ensured, and exploit deep learning methods favoring large-scale and versatile applications. Therefore, they are foundations for the extraction from multi-modal satellite image time series of relevant information related to the grassland ecosystem whose understanding is essentia

Suivi de la dynamique des prairies permanentes par analyse des séries temporelles multi-modales

The vast grassland surfaces as well as the growing recognition of the ecosystem services they provide have revealed urgent needs for their conservation and sustainable management. In particular, over-exploitation causes a significant decrease in their capacity to provide multiple ecosystem services. Despite the acknowledged importance of management practices, there are currently no large-scale efforts reporting on their frequency and nature.Satellite remote sensing appears to be a suitable tool for efficient grassland monitoring. Satellite time series specifically allow synoptic and regular observations. Combined, the characteristics provided by complementary optical and Synthetic Aperture Radar (SAR) images from the Sentinels bring new opportunities to monitor grassland vegetation conditions.The research conducted in this PhD thesis intends to investigate the capabilities and the synergy of Sentinel time series for grassland monitoring. Specifically, it aims to develop methods for detecting grassland management practices. Farmers are managing grasslands with a wide range of practices, having different impacts on biomass and calendars. Therefore, frequent and regular satellite acquisitions are mandatory, especially because grasslands exhibit the particularity of potential rapid regrowth after management. The joint exploitation of Sentinel-1 and Sentinel-2 and the increase of acquired data raise new challenges. The high dimension and the heterogeneous physical nature of the data, with various spatial, spectral and temporal domains, are among the aspects to be explored. At the same time, recent advances in computing resources and machine learning algorithms are bringing to the forefront deep learning strategies suitable for dealing with the reported requirements, such as large-scale processing and data mining.In this context, the main objective of this PhD is to develop new methodologies allowing the frequent and regular monitoring of grasslands and the detection of their management practices. Under this purpose, this PhD: (i) uses the advances of deep learning architectures to develop a multi-source methodology exploiting the synergy and capabilities of both Sentinel-1 and Sentinel-2 data. The developed recurrent-based methodology targets to regress multivariate SAR time series towards optical NDVI and proposes the incorporation of contextual knowledge to reduce the impact of exogenous factors leading to SAR data variability ; (ii) explores methods aiming to detect heterogeneous changes in vegetation status associated to grassland management practices.The proposed Sentinels Regression for Vegetation Monitoring (SenRVM) approach provides NDVI time series with no missing data at 6 days. The results, compared to the NDVI obtained by Sentinel-2, show low errors and good stability on contrasted vegetation surfaces and different large-scale geographical contexts. An ablation study of satellite and ancillary features and a comparison to commonly adopted gap-filling methods for retrieving information over short- and long-term data gaps underline the methodological contributions. To accurately detect management practices, a segmentation of grassland parcels at the superpixel-scale, justified by their rotational management, furthermore allows exploiting the dense time series over homogeneous areas. Diverse 1D time series change detection methodologies are compared using two constructed large-scale validation datasets. The results achieve high performances in retrieving the different patterns related to grassland management.The proposed methodologies integrate freely accessible data, whose continuity is ensured, and exploit deep learning methods favoring large-scale and versatile applications. Therefore, they are foundations for the extraction from multi-modal satellite image time series of relevant information related to the grassland ecosystem whose understanding is essentialLes vastes surfaces de prairies ainsi que la reconnaissance croissante des services écosystémiques qu'elles fournissent ont révélé des besoins urgents pour leur conservation et leur gestion durable. En particulier, leur surexploitation entraîne une diminution de leur capacité à fournir des services écosystémiques. En dépit de la nécessité d’obtenir des données décrivant l’exploitation des prairies, l’observation de la fréquence et de la nature de leur exploitation demeure restreinte.La télédétection par satellite est un outil approprié pour un suivi efficace des prairies. Les séries temporelles d'images satellites permettent des observations synoptiques et régulières. Combinées, les caractéristiques fournies par les images complémentaires optiques et radars des satellites Sentinel offrent de nouvelles opportunités. Les recherches menées dans le cadre de ce doctorat visent à étudier les capacités et la synergie des séries temporelles Sentinel pour le suivi des prairies. Plus spécifiquement, elles visent le développement de méthodes de détection des pratiques agricoles. La gestion de chaque prairie est faite avec des intensités et des calendriers distincts. Par conséquent, des acquisitions fréquentes et régulières sont d’autant plus indispensables que les prairies peuvent repousser rapidement après leur exploitation.L’abondance des données de Sentinel-1 et Sentinel-2 et leur exploitation conjointe soulèvent de nouvelles problématiques. La haute dimension et la nature physique hétérogène des données, conjuguant divers domaines spatiaux, spectraux et temporels, font partie des aspects à explorer. Récemment, les progrès en matière de ressources informatiques et d'algorithmes d'apprentissage automatique mettent au premier plan les stratégies d'apprentissage profond, qui permettent de relever les défis exposés, tels que le traitement à grande échelle et l'extraction d’informations complexes. L’objectif principal de la thèse est donc de développer des méthodes permettant le suivi en continu des prairies et la détection de leur exploitation. À cette fin, la thèse : (i) utilise les progrès permis par l'apprentissage profond, pour développer une méthodologie multi-source exploitant la synergie des données Sentinel-1 et Sentinel-2. La méthodologie développée vise spécifiquement à régresser les séries temporelles radars multivariées vers le NDVI optique et propose l'incorporation de connaissances contextuelles pour réduire l'impact de facteurs exogènes ; (ii) explore différentes méthodes permettant de détecter l’exploitation hétérogène des prairies.L'approche proposée, nommée Sentinels Regression for Vegetation Monitoring (SenRVM), fournit des séries temporelles de NDVI complètes avec une répétitivité de six jours. Les résultats, comparés aux NDVI obtenus par Sentinel-2, indiquent de faibles erreurs et une bonne stabilité sur diverses surfaces de végétation et différents contextes géographiques. Une étude d'ablation des données satellitaires et auxiliaires ainsi qu’une comparaison avec des méthodes communément adoptées pour interpoler les données manquantes soulignent la pertinence des contributions méthodologiques. Pour détecter avec précision les pratiques agricoles, une segmentation des parcelles de prairie à l'échelle du superpixel, justifiée par leur gestion rotative, permet d'exploiter les séries temporelles denses sur des zones homogènes. Différentes méthodologies de détection de changements sont comparées à l'aide de jeux de données de validation construits. Les résultats atteignent des performances élevées dans l’identification des différentes tendances liés à la gestion des prairies. L'intégration de données accessibles gratuitement, dont la continuité est assurée, et l'exploitation de méthodes d'apprentissage profond favorisant les applications à grande échelle et polyvalentes, ont permis d'introduire des méthodes qui proposent des bases pour la collecte d'informations pertinentes liées à l'écosystème des prairie

Assessing the Interest of a Multi-Modal Gap-Filling Strategy for Monitoring Changes in Grassland Parcels

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Use of Sentinel-1 imagery for flood management in a reservoir-regulated river basin

International audienc

Joint analysis of SAR and optical satellite images time series for grassland mowing event detection.

International audienceThroughout Europe, grasslands are a major component of the landscape comprising 40% of agricultural land. Permanent Grassland (PM) means land used to grow herbaceous forage crops naturally (self-seeded) or through cultivation (sown) and that has not been included in the crop rotation of the holding for five years or more. PM are major ecosystems associated with high biodiversity which provide a wide range of ecosystem services (e.g. carbon sequestration, water quality, flood and erosion control).Grasslands have an important carbon storage capacity which is valuable for climate protection. Different studies have demonstrated that grassland managements such as grazing or mowing can cause significant effects on carbon storage in soils. Identifying and mapping grassland management practices over time can thus have important impact on climate studies.Remote sensing allows a synoptic and regular monitoring through systematic acquisitions of Earth Observation imagery. The emergence of free and easily Sentinels satellite data provided by the European Copernicus programme offers new possibilities for grassland monitoring. Sentinel-1 (S1) and Sentinel-2 (S2) mission acquire radar and optical satellite image times series at high temporal resolution and fine spatial resolution. They fully match the requirements both for yearly and real-time monitoring.In this work, we target to jointly exploit both data sources to dynamically detect mowing events (MowEve) on permanent grasslands. Thematic related analysis of the datasets will highlight strengths and weaknesses of both optical and radar imagery. (i) S2 appears efficient for MowEve detection, with significant variations in the vegetation status that can be easily detected in the spectral signal extracted from the time series of images. But the temporal revisit of S2 although nominally 5 days is often reduced even by half due to the frequent cloud cover (ii) SAR images acquisitions being independent of illumination conditions or cloud cover allows for systematic acquisitions and revisit rate of 6 days. Data consistency makes S1 data essential during fast phenomena such as MowEve. Yet, radar data appears very sensitive to soil moisture, precipitations and geometrical properties making interpretation of their time series more challenging.MowEve detection being weakly supervised, the proposed methodology relies on applying traditional change detection strategies on a low-level fused S1 and S2 data representation. Recurrent Neural Networks will be trained to derive yearly or real-time synthetic S2 vegetation indices from both S2 and S1 observations. Furthermore, through attention mechanisms, our proposed RNN architecture will be able to take into account external data (climate, clouds, topography, etc.) so as to dynamically weight at parcel-level the contribution of optical and radar images. Such method will contribute to obtain dense temporal optical profiles without missing data and compatible with MowEve detection.An experimental evaluation will be carried out on a test site covering an area of 110x110 Km in France (Mâcon region). Object-oriented analysis will be presented based on permanent grasslands derived from the Land Parcel Identification System. The proposed approach will be compared with traditional MowEve methods essentially based on thresholding independently the different modalities

On the joint exploitation of optical and SAR imagery for grassland monitoring.

International audienceTime 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. Grasslands are such crucial surfaces, both in terms of economic and environmental issues and the automatic and frequent monitoring of their agricultural practices is required for many purposes. To address this problem, the deep-based SenDVI framework is presented. SenDVI proposes an object-based methodology to estimate NDVI values from Sentinel-1 SAR observations and contextual knowledge (weather, terrain). Values are regressed every 6 days for compliance with monitoring purposes. Very satisfactory results are obtained with this low-level multimodal fusion strategy (R 2 =0.84 on a Sentinel-2 tile). Finer analysis is however required to fully assess the relevance of each modality (Sentinel-1, Sentinel-2, weather, terrain) and feature sets and to propose the simplest conceivable framework. Results show that not all features are necessary and can be discarded while others have a mandatory contribution to the regression task. Moreover, experiments prove that accuracy can be improved by not saturating the network with non-essential information (among contextual knowledge in particular). This allows to move towards more operational solution

Flood Hazard Mapping in a Reservoir-regulated River Basin using Sentinel-1 imagery: The Case of Serres Basin.

International audienceFlood is a natural disaster and causes loss of life and property destruction. Flood hazard Monitoring and Mapping is of great importance because it represents a significant contribution to risk management. The present study investigated the flood event occurred in 2014-2015 at Serres Basin, a reservoir-regulated river basin, aiming to understand its spatio-temporal dynamic of the flood hazard. Within the Strymon River basin, a transboundary river outflows to Kerkini Lake-reservoir which has the role of regulating water flow to downstream for irrigation purposes and flood protection. For this research, a dataset of Sentinel-1 SAR GRD images was collected and processed covering the period of October 2014 – October 2015. Based on SAR images binary water and non-water products were generated and interpreted. Satellite Earth Observation has proved to be an effective tool for hazard dynamic extension mapping and in combination with hydro-meteorological data can be a significant knowledge in flood disaster management

Challenges in Grassland Mowing Event Detection with Multimodal Sentinel Images

International audiencePermanent 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-Time and seasonally PG cover. Here, pros and cons of Sentinel-2 (S2) and Sentinel-1 (S1) time series exploitation for Mowing Events (MowEve) detection are analysed. A deep-based approach is proposed to obtain consistent and homogeneous biophysical parameter times series for MowEve detection. Recurrent Neural Networks are proposed as regression strategy allowing the synergistic integration of optical and Synthetic Aperture Radar data to reconstruct dense NDVI times series. Experimental results corroborates the interest of deriving consistent and homogeneous series of biophysical parameters for subsequent MowEve detection