AI4Boundaries: an open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography

Field boundaries are at the core of many agricultural applications and are a key enabler for the operational monitoring of agricultural production to support food security. Recent scientific progress in deep learning methods has highlighted the capacity to extract field boundaries from satellite and aerial images with a clear improvement from object-based image analysis (e.g. multiresolution segmentation) or conventional filters (e.g. Sobel filters). However, these methods need labels to be trained on. So far, no standard data set exists to easily and robustly benchmark models and progress the state of the art. The absence of such benchmark data further impedes proper comparison against existing methods. Besides, there is no consensus on which evaluation metrics should be reported (both at the pixel and field levels). As a result, it is currently impossible to compare and benchmark new and existing methods. To fill these gaps, we introduce AI4Boundaries, a data set of images and labels readily usable to train and compare models on field boundary detection. AI4Boundaries includes two specific data sets: (i) a 10 m Sentinel-2 monthly composites for large-scale analyses in retrospect and (ii) a 1 m orthophoto data set for regional-scale analyses, such as the automatic extraction of Geospatial Aid Application (GSAA). All labels have been sourced from GSAA data that have been made openly available (Austria, Catalonia, France, Luxembourg, the Netherlands, Slovenia, and Sweden) for 2019, representing 14.8 M parcels covering 376 K km2. Data were selected following a stratified random sampling drawn based on two landscape fragmentation metrics, the perimeter/area ratio and the area covered by parcels, thus considering the diversity of the agricultural landscapes. The resulting “AI4Boundaries” dataset consists of 7831 samples of 256 by 256 pixels for the 10 m Sentinel-2 dataset and of 512 by 512 pixels for the 1 m aerial orthophoto. Both datasets are provided with the corresponding vector ground-truth parcel delineation (2.5 M parcels covering 47 105 km2), and with a raster version already pre-processed and ready to use. Besides providing this open dataset to foster computer vision developments of parcel delineation methods, we discuss the perspectives and limitations of the dataset for various types of applications in the agriculture domain and consider possible further improvements. The data are available on the JRC Open Data Catalogue: http://data.europa.eu/89h/0e79ce5d-e4c8-4721-8773-59a4acf2c9c9 (European Commission, Joint Research Centre, 2022).</p

Towards consistent inland water body mapping across space and time from optical Earth observation systems

Inland water bodies, while covering less than 4% of the Earth surface, are essential to many global dynamic processes such as biogeochemical cycles, biodiversity, climate change and ecosystem services. Mapping the temporal distribution of terrestrial water is thus crucial for scientific research as well as for sustainable ecosystem management. The current deployment of an unprecedented Earth observation satellite constellation provides a unique opportunity to monitor quantitatively our changing environment. The tsunami of heterogeneous spatial datasets recently available required appropriate methods to extract relevant information. In this context, we aimed at mapping consistently inland water bodies with optical remote sensing by developing methods taking into account spatial and temporal resolutions independently of the environmental context and the observation sources. First, the thesis proposes a framework improving delineation of water bodies by handling sub-metric multi-source data acquired in heterogeneous observation conditions. Secondly, we assess the minimum size of water body mappable at sub-pixel level from 10-m sensors, with a specific interest for Sentinel-2 instrument potential. Thirdly, mapping water bodies using twice-daily 250-m MODIS observation was successfully demonstrated to produce maps and indicators describing the location, the intra-annual and the inter-annual behavior of all African inland water bodies. Finally, we address the challenge of water body map validation by proposing and applying an original validation strategy specific for a land cover class underrepresented at a global scale. Altogether, the frameworks and methods developed during this thesis contribute to consistently map inland water bodies.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 201

Monitoring African water bodies from twice-daily MODIS observation

Building on the availability of high revisit frequency Earth Observation satellites at medium spatial resolution (250 m), this study investigates the feasibility of temporal monitoring of water bodies at a continental scale with MODIS. A 2004–2010 time series of twice-daily observations covering the whole African continent was systematically processed using a surface water detection method to derive 10-day indicators describing the location, the intra- and inter-annual variability as well as the temporal characterization of water bodies (i.e. seasonal or permanent water and maximum extent). The multispectral surface reflectance transformation in the HSV color space allows a per-pixel identification of surface water. The water aggregation time indicator provides the water occurrence for each 10-day period built from the seven years of observations. The cartographic products were successfully cross-validated with already existing maps and water products. The validation of the water body maximum extent map estimates the commission error at less than 6% and the seasonality information was also found to be consistent with the Köppen climatic classification

Hyperspatial and Multi-SourceWater Body Mapping : A Framework to Handle Heterogeneities from Observations and Targets over Large Areas

Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to update hyperspatial resolution (<1 m) land thematic mapping over large areas by handling multi-source and heterogeneous data. This framework deals with heterogeneity both from observation and the targeted features. First, observation diversity comes from the different platform and sensor types (25-cm passive optical and 1-m LiDAR) as well as the different instruments (three cameras and two LiDARs) used in heterogeneous observation conditions (date, time, and sun angle). Second, the local heterogeneity of the targeted features results from their within-type diversity and neighborhood effects. This framework is applied to surface water bodies in the southern part of Belgium (17,000 km2). This makes it possible to handle both observation and landscape contextual heterogeneity by mapping observation conditions, stratifying spatially and applying ad hoc classification procedures. The proposed framework detects 83% of the water bodies—if swimming pools are not taken into account—and more than 98% of those water bodies greater than 100 m2, with an edge accuracy below 1 m over large areas

Monitoring African surface water dynamic using medium resolution daily data allows anomalies detection in nearly real time

This paper proposes to use a water detection methodology based on a colorimetric approach to develop a near real time system allowing to monitor and to detect anomalies at a fine time resolution and in a systematic way The algorithm was calibrated over Africa using daily reflectance MODIS data from 2003 to 2011. The proposed approach has 3 major outputs updatable in near real time: (1) a permanent water mask (2) a every 10-days surface water map consolidated with time series and (3) an anomalies detection using 10 years of detection reanalysis. Three validation approaches are developed to deal with the large coverage and the high temporal resolution. The methodology is generic and could be applied to other extent and sensors

Monitoring African surface water dynamic using medium resolution daily data allows anomalies detection in nearly real time

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Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi- and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors’ publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors’ applications with Sentinel-2’s and (ii) analyzes the applications’ requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses

Development of a new Water Bodies detection algorithm adapted to new sensors (MERIS, MODIS) to increase spatial and temporal resolution

Detecting small water bodies (<1km2) in nearly real time in the whole Africa is a capital issue for natural resource management as well as environment monitoring. In the framework of Geoland2 (BioPar), this research project aims to reinforce and develop the ability to monitor water bodies in nearly real time (10 days). The evaluation of two existing algorithms for detecting water bodies from SPOT Vegetation data leads to their combination for an enhanced reliability and then to the adaptation of the method to new sensors (MODIS, MERIS)